physician assign score to determine need for anesthesia

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan-.

StatPearls [Internet].

American society of anesthesiologists staging.

Lauren A. Hocevar ; Brian M. Fitzgerald .

Affiliations

Last Update: January 29, 2023 .

Definition/Introduction

In 1963, the American Society of Anesthesiologists instituted a system to assess a patient's physical health status and clinical risk during anesthetic administration and surgical operation. Pre-operatively, the patient is subjectively assigned a score according to their physical status, which is determined by the anesthesiologist after considering patient presentation, history, and functional limitations. Assigning this score, ranked ASA I through ASA VI, would thereby attempt to categorize the patient's risk of perioperative complications based on their physical status and overall health. Patients assigned to higher numerical categories have increased risk of perioperative adverse events. The goal of creating the ASA Physical Status Classification System (ASA-PS) was to improve patient outcomes and predict perioperative risk. [1] Despite its setbacks, it has since become a standard practice during perioperative encounters and plays a key role in preventative medicine associated with anesthesia.

Issues of Concern

A topic of concern commonly encountered with assigning ASA scores is that there is often significant variation with how providers may classify the same patient. Assessment and evaluation of patients can vary between providers in different specialties compared to the staff anesthesiologists, causing a significant increase in standard deviation even when participants had access to the same medical records. This situation proves to be a problem that is more prevalent outside the specific specialty, posing a potential threat to the success of healthcare teams composed of multiple providers from multiple specialties. [2] Studies have shown that adding examples for each respective score aided both anesthesia and non-anesthesia providers consistently classifying patients accurately. [3] In 2014, the ASA provided access to a catalog of examples for simplification when assigning an ASA score, increasing accuracy and decreasing inter-observer variation. [4]

Clinical Significance

Using the ASA Physical Status Classification System to evaluate and prepare for possible adverse events remains one of the most widely used pre-operative screening methods for all providers worldwide [5] . The ASA Physical Status Classification System has been shown to predict the frequency of perioperative morbidity and mortality. Research has shown that the implementation of the classification system correctly predicts the frequency and severity of adverse events, which improves patient outcomes [5] . The ASA score is assigned based on the presence and severity of systemic disease in a patient. Examples of some disease processes commonly encountered are listed with their respective score assignment. The letter E may be added onto any category (i.e., ASA IIE) to denote an emergency.

ASA I: a healthy patient with no evidence of active or chronic disease processes, non-smoker, and BMI under 30. [6]

ASA II: a patient with mild systemic disease. Examples include a patient who has no functional limitations and well-controlled disease, BMI under 35, is a social drinker or smokes cigarettes, or has well-controlled hypertension. [6]

ASA III: a patient with severe systemic disease that is not life-threatening. Examples include patients with functional limitations as a result of systemic disease, poorly treated hypertension or diabetes, renal failure, morbid obesity, stable angina, or pacemaker. [6]

ASA IV: A patient with severe systemic disease that is a constant threat to life. Examples include patients with functional limitations as a result of severe systemic disease such as unstable angina, poorly controlled COPD, symptomatic CHF, recent MI, or stroke less than three months prior. [6]

ASA V: A moribund patient who is not expected to survive without surgical intervention. Examples include ruptured abdominal aortic aneurysm, massive trauma, or extensive intracranial bleeding with mass effect. [6]

ASA VI: A patient declared brain-dead, who is a transplant donor.

Nursing, Allied Health, and Interprofessional Team Interventions

During perioperative care, the actions of the nurse contribute not only to patient outcomes but also in preventing and monitoring for adverse events. Nurses should not only understand what may place a patient in a specified category but also should be able to interpret any complications each patient may face as a result of their assigned score and/or illness. With the education and utilization of the ASA Physical Status Classification System, a nurse can intervene when necessary and collaborate with other providers to ensure the best possible clinical outcome. Interpretations of a patients’ categorization are important to nursing staff as they will use the complete clinical evaluation to guide their monitoring of the patient and to communicate effectively with other members of the anesthesia care team. [7] This categorization also applies to pharmacists and other health professionals who may be contributing to a patient's care plan. Studies have shown that interprofessional team communication failures continue to be a leading cause of adverse outcomes. [8] Therefore, an increased effort toward improving collaboration between nurse and physician anesthesia providers ultimately improves patient care. [9] [10] [Level 3]

Nursing, Allied Health, and Interprofessional Team Monitoring

Nurses will provide pre- and post-operative care in addition to monitoring changes in the anesthetic plan. As stated previously, it is essential that nurses understand the ASA Physical Classification System and what conditions a patient may have that might increase their chance of adverse outcomes during the perioperative period. Nurses play a vital role in perioperative care as they closely monitor patients in the PACU for possible adverse outcomes. Nurses should integrate the patient's ASA categorization into their perioperative care plan and monitor for any adverse outcomes that may be associated with their illness.

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Disclosure: Lauren Hocevar declares no relevant financial relationships with ineligible companies.

Disclosure: Brian Fitzgerald declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

Cite this Page Hocevar LA, Fitzgerald BM. American Society of Anesthesiologists Staging. [Updated 2023 Jan 29]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan-.

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Standards and Practice Parameters

Statement on ASA Physical Status Classification System

Developed By: Committee on Economics Last Amended: December 13, 2020 (original approval: October 15, 2014)

The ASA Physical Status Classification System has been in use for over 60 years. The purpose of the system is to assess and communicate a patient’s pre-anesthesia medical co-morbidities. The classification system alone does not predict the perioperative risks, but used with other factors (eg, type of surgery, frailty, level of deconditioning), it can be helpful in predicting perioperative risks.

The definitions and examples shown in the table below are guidelines for the clinician. To improve communication and assessments at a specific institution, anesthesiology departments may choose to develop institutional-specific examples to supplement the ASA-approved examples.

Patient physical status at a glance.

For more than 60 years, the ASA Physical Status Classification System has helped guide clear communication and better patient care. Gain access to the full range of ASA resources and help advocate for improved patident safety.

Join ASA today ›

Assigning a Physical Status classification level is a clinical decision based on multiple factors. While the Physical Status classification may initially be determined at various times during the preoperative assessment of the patient, the final assignment of Physical Status classification is made on the day of anesthesia care by the anesthesiologist after evaluating the patient.

Current Definitions and ASA-Approved Examples

For more information on the ASA Physical Status Classification system and the use of examples, the following publications are helpful. Additionally, in the reference section of each of the articles, one can find additional publications on this topic.

Abouleish AE, Leib ML, Cohen NH. ASA provides examples to each ASA physical status class. ASA Monitor 2015; 79:38-9 http://monitor.pubs.asahq.org/article.aspx?articleid=2434536
Hurwitz EE, Simon M, Vinta SR, et al. Adding examples to the ASA-Physical Status classification improves correct assignments to patients. Anesthesiology 2017; 126:614-22
Mayhew D, Mendonca V, Murthy BVS. A review of ASA physical status – historical perspectives and modern developments. Anaesthesia 2019; 74:373-9
Leahy I, Berry JG, Johnson C, Crofton C, Staffa S, Ferrari LR. Does the Current ASA Physical Status Classification Represent the Chronic Disease Burden in Children Undergoing General Anesthesia? Anesthesia & Analgesia, October 2019;129(4):1175-1180
Ferrari L, Leahy I, Staffa S, Johnson C, Crofton C, Methot C, Berry J. One Size Does Not Fit All: A Perspective on the American Society of Anesthesiologists Physical Status Classification for Pediatric Patients. Anesthesia & Analgesia, June 2020;130(6):1685-1692
Ferrari LR, Leahy I, Staffa SJ, Berry JG. The Pediatric Specific American Society of Anesthesiologists Physical Status Score: A Multi-center Study. Anesth Analg 2021 March; 132:807-817. PMID: 32665468

Last updated by: Governance

Date of last update: December 13, 2020

Research article
Open access
Published: 09 July 2020

Assignment of pre-event ASA physical status classification by pre-hospital physicians: a prospective inter-rater reliability study

Kristin Tønsager ORCID: orcid.org/0000-0002-5289-0442 1 , 2 , 3 ,
Marius Rehn 1 , 2 , 4 ,
Andreas J. Krüger 1 , 5 ,
Jo Røislien 3 , 1 &
Kjetil G. Ringdal 6 , 7 , 8

BMC Anesthesiology volume 20 , Article number: 167 ( 2020 ) Cite this article

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Metrics details

Individualized treatment is a common principle in hospitals. Treatment decisions are made based on the patient’s condition, including comorbidities. This principle is equally relevant out-of-hospital. Furthermore, comorbidity is an important risk-adjustment factor when evaluating pre-hospital interventions and may aid therapeutic decisions and triage. The American Society of Anesthesiologists Physical Status (ASA-PS) classification system is included in templates for reporting data in physician-staffed pre-hospital emergency medical services (p-EMS) but whether an adequate full pre-event ASA-PS can be assessed by pre-hospital physicians remains unknown. We aimed to explore whether pre-hospital physicians can score an adequate pre-event ASA-PS with the information available on-scene.

The study was an inter-rater reliability study consisting of two steps. Pre-event ASA-PS scores made by pre- and in-hospital physicians were compared. Pre-hospital physicians did not have access to patient records and scores were based on information obtainable on-scene. In-hospital physicians used the complete patient record (Step 1). To assess inter-rater reliability between pre- and in-hospital physicians when given equal amounts of information, pre-hospital physicians also assigned pre-event ASA-PS for 20 of the included patients by using the complete patient records (Step 2). Inter-rater reliability was analyzed using quadratic weighted Cohen’s kappa (κ w ).

For most scores (82%) inter-rater reliability between pre-and in-hospital physicians were moderate to substantial (κ w 0,47-0,89). Inter-rater reliability was higher among the in-hospital physicians (κ w 0,77 to 0.85). When all physicians had access to the same information, κ w increased (κ w 0,65 to 0,93).

Conclusions

Pre-hospital physicians can score an adequate pre-event ASA-PS on-scene for most patients. To further increase inter-rater reliability, we recommend access to the full patient journal on-scene. We recommend application of the full ASA-PS classification system for reporting of comorbidity in p-EMS.

Peer Review reports

Tailored treatment through adapted choice of therapy, medication and monitoring to each patient is a common principle in hospitals [ 1 , 2 , 3 ]. In all parts of critical care, decisions are made based on the patient’s condition, including the patient’s comorbidities [ 1 , 2 , 4 ]. Decisions of dose adjusted medication and volume loading before anesthesia are common examples of individualized adaptions in the operating room [ 4 ]. Pre-hospital critical care is a continuum, and pre-hospital management is often a part of the patient’s course [ 5 , 6 ]. As such, stratification on comorbidity, and individualized treatment, is equally relevant and valid for pre-hospital patients. In line with this principle, the patient’s health status before the acute event should be accounted for in triage on-scene and to determine threshold for, and timing of interventions and physiological targets [ 7 , 8 ].

Risk adjustments allows for better judgement about the effectiveness and quality of alternative therapies [ 1 ]. Comorbidity is an important risk adjustment factor when evaluating pre-hospital interventions [ 9 , 10 ]. In general, there is an agreement that outcome after trauma is influenced by the patient’s physical state before the trauma occurs [ 11 ]. Thus, to include a comorbidity measure is a prerequisite for comparisons and improves the precision of outcome prediction for trauma patients [ 8 , 9 , 12 ]. However, to obtain information on comorbidity from in-hospital records may be challenging for pre-hospital services due to logistics and legal issues of access and other strategies for obtaining this information should be explored.

Several methods for reporting comorbidities in pre-hospital emergency medical services (p-EMS) exists [ 8 , 9 , 13 ]. The American Society of Anesthesiologists Physical Scale (ASA-PS) classification system is used globally by anesthesiologists and classifies the preoperative physical health condition in patients before anesthesia and surgery. ASA-PS was originally designed to allow for statistical analyses of outcomes and to standardize terminology [ 14 , 15 ], not to predict perioperative risk [ 15 ], but research has shown that the ASA-PS correlates well with overall surgical mortality [ 14 ]. Although the reliability of ASA-PS may be discussed, the scale is widely accepted as a tool to decide pre-operative health status [ 16 ]. The use of ASA-PS has expanded to the pre- and in-hospital critical care environment and pre-event ASA-PS, which is ASA-PS before the present injury or illness, [ 17 ] describes the inherent physiological state of a patient before an event. Pre-event ASA-PS is shown to be an independent predictor of mortality after trauma [ 8 ] and is included in templates for reporting of comorbidity in p-EMS and trauma [ 18 , 19 ]. We therefore used pre-event ASA-PS as a comorbidity measure for the present study.

Ideally, pre-hospital services should have access to the full patient journal on-scene. Reality is however different and access to the full patient journal tends to be restricted for most pre-hospital services on-scene. P-EMS services must thus commonly base their decisions on the more limited amount of data and observations obtainable on-scene than for in-hospital physicians. Obtaining the complete medical history from seriously ill or injured patients on-scene is considered unfeasible, and reporting a dichotomized pre-event ASA-PS (pre-event ASA-PS 1 or pre-event ASA-PS > 1) is thus often recommended [ 20 ]. This simplification of the scale provides a very rough measure of comorbidity with low clinical discriminatory capabilities. Whether an adequate full pre-event ASA-PS can be assessed by pre-hospital physicians based only on the limited information generally available on-scene has not been explored and remains unknown. If scores between pre-and in-hospital physicians do not differ more than between in-hospital physicians, then the pre-hospital scores are just as “correct” as the in-hospital scores and can be used accordingly.

The aim of the present study was to explore whether it is possible for pre-hospital physicians to score an adequate pre-event ASA-PS already while on-scene.

Prospective observational inter-rater reliability study. We assessed the degree of agreement among two raters using the ASA-PS scale under different circumstances to decide whether different access to information influenced the scores. All patients admitted by p-EMS to two Norwegian hospitals during a period of three-months (Stavanger University Hospital 19 Aug – 18 Nov 2016 and St. Olav University Hospital 1 Feb – 30 Apr 2017) were included. Following the inclusion periods, in-hospital physicians scored all included patients (Step 1). Data collection for the second part of the study (Step 2) was finished 21 Mar 2018. All Norwegian p-EMS services are staffed with anesthesiologists and respond to all types of emergency conditions, search and rescue missions and inter-hospital transfers.

We used the pre-event ASA-PS to assess comorbidity. The pre-event ASA-PS does not take the present event into account and describes the physiological state of the patient before an event [ 8 , 11 , 21 ]. The ASA-PS provides a global, subjective index of a patient’s overall health status, and pre-existing medical conditions are categorized on a scale of increasing medical severity (ASA-PS 1–5) [ 17 ].

Step 1. Inter-rater reliability study of pre- versus in-hospital scores

Pre-hospital physicians assigned a pre-event ASA-PS score on-scene based on information available out-of-hospital only. The pre-hospital physicians did not have access to the full patient records. If the physician was unable to decide on a pre-event ASA-PS score on-scene, the score was kept unassigned and the main reason declared. After the three-month inclusion period, three in-hospital anesthesiologists at each of the two sites were given access to full patient records for all included patients at each site. Blinded from the pre-event ASA-PS score allocated by p-EMS each in-hospital physician used this information to assign pre-event ASA-PS scores for the included patients. No specific training for ASA-PS scoring was provided.

Step 2. Inter-rater reliability with equal access to data

Because p-EMS generally do not have access to the full patient journal comparing pre-hospital on-scene scores with in-hospital scores is an asymmetric comparison (as in-hospital physicians have access to more information). We thus did not expect perfect agreement between pre- and in-hospital raters. To assess agreement of pre-event ASA-PS scores when pre- and in-hospital physicians had access to equal data, 20 patients were selected by an on-line randomizer and re-scored by the pre-hospital physicians when given access to complete patient records. The rationale behind this was to assess whether an observed difference in scoring was due to different physicians (pre- versus in-hospital) or different data availability.

We were unable to identify any studies in which pre-event ASA-PS was scored in a real-time pre-hospital setting. Without prior empirical information on the variation of the phenomenon under study we were consequently unable to perform sample size calculations [ 22 , 23 ]. Statistical rules of thumb for sample size varies in the literature and sample sizes from 10 to 50 is reported [ 24 ]. Combining existing advice, we chose to included 20 patients per physician to evaluate inter-rater reliability [ 24 ]. If no agreement between pre- and in-hospital physicians for 20 patients could be established, we considered the pre-hospital scores to be irrelevant.

Patients and physicians were anonymized prior to further statistical analyses.

Guidelines for Reporting Reliability and Agreement Studies (GRRAS) was used [ 25 ].

Statistical analyses

ASA-PS is an ordinal scale and agreement between two ASA-PS measures on the same individual was thus assessed using quadratic weighted Cohen’s Kappa (κ w ); a modification of Cohen’s Kappa that also accounts for the degree of disagreement between raters [ 26 ]. κ w is a number between 0 and 1. κ w < 0.10 indicates no inter-rater reliability, while 0.11–0.40 indicates slight, 0.41–0.60 indicates fair, 0.61–0.80 indicates moderate and 0.8–1.0 indicates substantial inter-rater reliability [ 27 ].

If two measurement methods are to be considered similar their results should be indistinguishable from one another [ 28 ]. Using κ w values between pre- and in-hospital physicians as a measure of agreement, we performed minimax hierarchical agglomerative clustering; a method for exploring the inner agreement structure of a dataset [ 29 ]. The result from this clustering process is presented visually as dendrograms. Such dendrograms look like up-side-down trees, grouping elements that agree the most near the bottom of the graph, with decreasing agreement (i.e. inter-rater reliability) the higher on the graph. This approach allowed us to visually explore whether the agreement between pre-and in-hospital physicians were indeed indistinguishable from one another. The overall mean agreement [ 30 ] for all pre- versus in-hospital physicians was also calculated. Data were analyzed using IBM SPSS statistics version 22 and R 3.1.0.

Pre-event ASA-PS was registered for a total of 312 patients. We excluded four patients admitted to non-participating hospitals and three patients without identifiable patient records. One physician scored only four patients, three with pre-event ASA-PS 3 and one that could not be scored. This did not allow for κ w calculations, as scores were identical, and this physician and corresponding patients were thus excluded. In total 301 patients were available for further statistical analysis.

Pre-hospital physicians scored a median (range) of 21 (5–40) patients. Five patients (2%) could not be scored on-scene (four were unconscious and one was not able to communicate).

The distribution of ASA-PS scores between pre- and in-hospital physicians are presented in Table 1 .

κ w values for pre-event ASA-PS scores assigned by pre-hospital physicians on-scene, and subsequent scores based on complete patient records by in-hospital physicians are presented in Fig. 1 .

κ w values for pre-event ASA-PS scores. Estimated inter-rater reliability between each pre-hospital (PDoc) and in-hospital (IDoc) physician using quadratic weighted Cohen’s kappa with 95% CI values

κ w values ranged from 0.77 to 0.85 among the three in-hospital physicians, and from 0.47 to 0.89 when comparing the pre- to in-hospital physicians. The mean kappa values were 0,67 (PDocs Stavanger), 0,78 (IDocs Stavanger), 0,75 (PDocs Trondheim) and 0,84 (IDocs Trondheim). For most scores (82%) inter-rater reliability between pre-and in-hospital physicians were moderate to substantial (κ w > 0.61).

The mean agreement between all pre-hospital physicians and each of the three in-hospital physicians is generally high. However, the three in-hospital physicians tend to agree more with one another than they agree with the pre-hospital physicians. This is demonstrated in Fig. 2 .

Pre- versus in-hospital agreement. Mean agreement between all pre-hospital physicians (PDocs) and the three in-hospital physicians (IDoc) at the two sites, using on-scene pre-hospital scores and in-hospital scores respectively

When pre- and in-hospital physicians scored the same 20 patients with equal access to information, the agreement was strengthened. The difference in inter-rater reliability between the pre- and in-hospital physicians was much smaller, with κ w values ranging from 0.65 to 0.93, indicating moderate to substantial agreement. Corresponding dendrograms for the two sites demonstrate that scores from pre- and in-hospital physicians do not cluster but remain largely indistinguishable from one another (Fig. 3 ).

Agreement when given equal access to information. Dendrograms depict inter-rater reliability between pre- (PDoc) and in-hospital (IDoc) physicians when scoring the same 20 patients with pre-event ASA-PS given equal access to information. PDocs are indistinguishable from IDocs

The present study is a study of ASA-PS scoring in real life situations. As pre-hospital physicians did not have access to the full patient journal (Step 1), perfect agreement in ASA-PS scoring between pre-and in-hospital physicians was not to be expected. When comparing pre- and in-hospital pre-event ASA-PS scores, agreement was generally high ranging from fair to substantial. Most scores (82%) demonstrated moderate (64%) to substantial (18%) agreement, indicating that pre-hospital physicians can obtain sufficient data on-scene to score an adequate pre-event ASA-PS for most patients. Because the total number of pre-hospital scores are high, the impact of uncertainty in the scores, represented by broad 95% confidence intervals in Fig. 1 , is reduced.

When pre- and in-hospital physicians scored pre-event ASA-PS on the same patients with access to complete patient records, agreement improved and ranged from moderate (52%) to substantial (48%). This indicates that ASA-PS scores from pre- and in-hospital physicians are indistinguishable from one another when they have equal data access (Fig. 3 .). Accordingly, observed differences in pre-event ASA-PS scores in the first part of the study may be attributed to differences in data availability and time pressure on-scene rather than to factors related to individual physicians.

Comorbidity is an important risk-adjustment factor when evaluating pre-hospital interventions and the effect of p-EMS [ 9 , 10 ]. Additionally, adjustment for comorbidity significantly increase the predictive accuracy of trauma outcome prediction models [ 9 , 12 , 31 , 32 ]. The inherent nature of p-EMS favors a method for reporting comorbidities that is both readily available and time effective. ASA-PS is a well-known physical health condition scale, globally applied by anesthesiologists and surgeons, supporting the notion that pre-event ASA-PS may be advantageous for reporting comorbidity in p-EMS. However, studies have found substantial inter-observer variation [ 21 , 33 ]. Most of these studies are hypothetical case scenarios designed by researchers [ 8 , 16 , 21 ]. In the present study we found that the agreement between pre- and in-hospital scores is acceptable for most patients and argue that pre-event pre-hospital ASA-PS should be applied for documentation of comorbidity in p-EMS.

Obtaining complete medical history from seriously ill patients on-scene is considered unfeasible. Accordingly, a dichotomized pre-event ASA-PS is often reported [ 20 ]. This is a very rough measure of comorbidity with low clinical discriminatory ability and will not distinguish between mild and severe systemic disease. Our results indicate that p-EMS can assign an adequate full-scale pre-event ASA-PS score already on-scene.

Significantly less accuracy of assigning ASA-PS is reported for non-anesthesiologists compared to anesthesiologists, possibly limiting the validity of pre-hospital pre-event ASA-PS scores to anesthesiologist-staffed services [ 34 ]. Standardized education and encouraged use may decrease variability for less proficient users [ 35 ]. Knowledge of comorbidity is relevant for all emergency medical services to aid decision-making and to target the treatment. Reliability of pre-event ASA-PS scored by paramedics is unknown and should be subject for further research. Precise definitions of each ASA-PS class, along with training for use, may improve reliability and usability for all users.

Although the physicians in the present study did not have access to patient records only 2% of the patients could not be scored on-scene, all of which had impaired consciousness. These patients remain a challenge for p-EMS regarding comorbidity assessment. Access to patient records in p-EMS may increase feasibility and precision of pre-event ASA-PS scores and systems for field data access should be available. Summary care records (SCRs) are electronic records of important patient information available for authorized health care staff involved in patient care [ 36 ]. The prevalence of summary care records (SCRs) is increasing [ 36 ]. SCRs may provide timely and relevant patient information regardless of regional affiliation. Whether access to SCRs will increase reliability of pre-event ASA-PS scores on-scene remains unknown.

Limitations

The study was performed in a highly specialized anesthesiologist-staffed system and the results may not be transferable to other p-EMS. When number of assigned scores is low, conclusions may be inaccurate. Patients who died prior to hospital arrival were excluded. These patients are among the most severely sick or injured patients and may have a substantial comorbidity burden. Omitting these patients may overestimate the rate of agreement in this study.

For an anesthesiologist-staffed EMS covering a mixed patient population, an adequate pre-event ASA-PS can be assigned on-scene. When data access was equal, pre-event ASA-PS scores by pre- and in-hospital physicians were indistinguishable from each other. When pre-event ASA-PS was scored on-scene with restricted data access, inter-rater reliability was lower, but acceptable. We recommend application of the full pre-event ASA-PS classification system for documentation of comorbidity in p-EMS.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

The American Society of Anesthesiologists Physical Status

Physician-staffed pre-hospital emergency medical services

Guidelines for Reporting Reliability and Agreement Studies

Quadratic weighted Cohen’s Kappa

Pre-hospital physician

In-hospital physician

Summary care records

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Acknowledgements

The authors are grateful to the donors of the Norwegian Air Ambulance Foundation. The authors thank all pre-hospital physicians in Stavanger and Trondheim who collected pre-hospital data and Guro Mæhlum Krüger, Trond Nordseth, Helge Haugland, Katrine Finsnes, Unni Bergland and Linda Rørtveit who collected in-hospital data.

The Norwegian Air Ambulance Foundation funded this project but played no part in study design, data collection, analysis, writing or submitting to publication.

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Kristin Tønsager, Marius Rehn, Andreas J. Krüger & Jo Røislien

Department of Anesthesiology and Intensive Care, Stavanger University Hospital, Stavanger, Norway

Kristin Tønsager & Marius Rehn

Faculty of Health Sciences, University of Stavanger, Stavanger, Norway

Kristin Tønsager & Jo Røislien

Pre-hospital Division, Air Ambulance Department, Oslo University Hospital, Oslo, Norway

Marius Rehn

Department of Emergency Medicine and Pre-Hospital Services, St. Olav’s Hospital, Trondheim, Norway

Andreas J. Krüger

Department of Anesthesiology, Vestfold Hospital Trust, Tønsberg, Norway

Kjetil G. Ringdal

Prehospital Division, Vestfold Hospital Trust, Tønsberg, Norway

Norwegian Trauma Registry, Oslo University Hospital, Oslo, Norway

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KT, KGR and AJK conceived the idea. KT and AJK were involved in acquisition of data. KT analyzed the data, KGR, AJK, MR and JR supervised the analysis. All authors were involved in the interpretation of the data. KT drafted the manuscript and KGR, AJK, MR and JR revised it critically. All authors have read and approved the final version of the manuscript. All authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Tønsager, K., Rehn, M., Krüger, A.J. et al. Assignment of pre-event ASA physical status classification by pre-hospital physicians: a prospective inter-rater reliability study. BMC Anesthesiol 20 , 167 (2020). https://doi.org/10.1186/s12871-020-01083-x

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physician assign score to determine need for anesthesia

ASA stands for American Society of Anesthesiologists . In 1963 the ASA adopted a five category physical status classification system for assessing a patient before surgery . A sixth category was later added. These are:

A normal healthy patient .
A patient with mild systemic disease .
A patient with severe systemic disease .
A patient with severe systemic disease that is a constant threat to life .
A moribund patient who is not expected to survive without the operation .
A declared brain-dead patient whose organs are being removed for donor purposes.

If the surgery is an emergency, the physical status score is followed by “E” (for emergency ) for example “3E”. Category 5 is always an emergency so should not be written without "E". The category 6E probably does not exist. The original definition of emergency in 1940, when ASA classification was first designed, was "a surgical procedure which, in the surgeon's opinion, should be performed without delay" [1] . This gives an opportunity to the surgeon to manipulate the schedule of surgery for personal convenience. An emergency is now "defined as existing when delay in treatment of the patient would lead to a significant increase in the threat to life or body part [2] . If it is correct then severe pain due to broken bones, ureteric stone or parturition is not emergency.

These definitions appear in each annual edition of the ASA Relative Value Guide. There is no additional information that can be helpful to further define these categories. [3]

1 Limitations and proposed modifications
5 References
6 External links

Limitations and proposed modifications

Different authors give different versions of this ASA definition. [4] It is because this classification is vague and far from perfect. Many authors try to explain it on the basis of 'functional limitation' or 'anxiety' of patient which are not mentioned in the actual definition. Often different anesthesia providers assign different scores to the same patient. [5] [6] [7] [8] For example, heart attack ( myocardial infarction ), though grave, is a 'local' problem and is not a 'systemic' disease, so a patient with recent (or old) heart attack, in the absence of any other systemic disease, does not truly fit in any category of the ASA classification, yet having poor post-surgery survival rates. Other severe heart, liver, lung or kidney diseases, although they greatly affect physical status of patient and outcome of surgery, cannot be labelled as “ systemic disease ” (which means a generalized disorder of the whole body like hypertension or diabetes mellitus ). Local diseases can also change physical status but not be mentioned in ASA classification.

This scoring system assumes that age of the patient has no relation to physical fitness, which is not true. Neonates and very old people, even in the absence of any systemic disease, tolerate anesthesia and surgery badly in comparison to young adults. Similarly this classification ignores patients with malignancy ( cancer ). This scoring system could not be improved to a more elaborated and scientific form, probably because it is often used for price reimbursement.

Some anesthetists now propose that like an 'E' modifier for emergency, a 'P' modifier for pregnancy should be added to the ASA score. [9]

While anesthesia providers use this scale to indicate the patient's overall physical health or "sickness" preoperatively, it is regarded by hospitals, law firms, accrediting boards and other health care groups as a scale to predict risk [10] , and thus decide if a patient should have – or should have had – an operation. [11] To predict operative risk, age and obesity of the patient, the nature and severity of the operative procedure, selection of anesthetic techniques, the competency of the surgical team (surgeon, anesthesia providers and assisting staff), duration of surgery or anesthesia, availability of equipment, medicine, blood, implants and especially the level of post-operative care etc. are often far more important than simple ASA score.

In 1940-41, ASA asked a committee of three physicians (Meyer Saklad, M.D., Emery Rovenstine, M.D., and Ivan Taylor, M.D.) to study, examine, experiment and devise a system for the collection and tabulation of statistical data in anesthesia which could be applicable under any circumstances. [1] This effort was the first by any medical specialty to stratify risk for its patients. [12] While their mission was to determine predictors for operative risk, they quickly dismissed this task as being impossible to devise. They state:

"In attempting to standardize and define what has heretofore been considered 'Operative Risk', it was found that the term … could not be used. It was felt that for the purposes of the anesthesia record and for any future evaluation of anesthetic agents or surgical procedures, it would be best to classify and grade the patient in relation to his physical status only." [11]

The scale they proposed addressed the patient's preoperative state only, not the surgical procedure or other factors that could influence surgical outcome. They hoped anesthesiologists from all parts of the country would adopt their "common terminology," making statistical comparisons of morbidity and mortality possible by comparing outcomes to "the operative procedure and the patient's preoperative condition". [1] [13]

They described a six-point scale, ranging from a healthy patient (class 1) to one with an extreme systemic disorder that is an imminent threat to life (class4). The first four points of their scale roughly correspond to today's ASA classes 1-4, which were first published in 1963. [5] The original authors included two classes that encompassed emergencies which otherwise would have been coded in either the first two classes (class 5) or the second two (class 6). By the time of the 1963 publication of the present classification, two modifications were made. First, previous classes 5 and 6 were removed and a new class 5 was added for moribund patients not expected to survive 24 hours, with or without surgery. Second, separate classes for emergencies were eliminated in lieu of the "E" modifier of the other classes. [14] [13] The sixth class is now used for declared brain-dead organ donors. Saklad gave examples of each class of patient in an attempt to encourage uniformity. Unfortunately, the ASA did not later describe each category with examples of patients and thus actually increased confusion.

Anesthesiologist
Anesthetic equipment
Anesthetics
BIS monitor to assess the depth of anaesthesia
Nurse anesthetist or Certified Registered Nurse Anesthetist (CRNA)
Perioperative mortality
Patient safety
Patient safety organization
↑ 1.0 1.1 1.2 Saklad M. Grading of patients for surgical procedures. Anesthesiology 1941; 2:281-4.
↑ ASA Relative Value Guide 2002, American Society of Anesthesiologists, page xii, Code 99140.
↑ Fehrenbach, Margaret J. "ASA Physical Status Classification System" . Retrieved 2007-07-09 .
↑ 5.0 5.1 Little JP: Consistency of ASA grading. Anaesthesia. 1995 Jul;50(7):658-9.
↑ Haynes SR, Lawler PG (1995). "An assessment of the consistency of ASA physical status classification allocation". Anaesthesia . 50 (3): 195–9. PMID 7717481 .
↑ Owens WD, Felts JA, Spitznagel EL: ASA physical status classification: A study of consistency of ratings. Anesth 1978, 49:239-43.
↑ Harling DW. Consistency of ASA Grading. Anaesthesia. 1995 Jul;50(7):659.
↑ Pratt, Stephen D. "Clinical Forum Revisited: The "P" Value" (PDF) . Spring 2003 newsletter . The Society for Obstetric Anesthesia and Perinatology (SOAP). pp. 9–11 . Retrieved 2007-07-09 .
↑ William D. Owens, M.D. American Society of Anesthesiologists Physical Status Classification System Is Not a Risk Classification System. Anesthesiology. 94(2):378, February 2001.
↑ 11.0 11.1 Lema, Mark J (2002). "Using the ASA Physical Status Classification May Be Risky Business" . ASA Newsletter . American Society of Anesthesiologists . Retrieved 2007-07-09 . Unknown parameter |month= ignored ( help )
↑ Spell, Nathan O.; Lubin, Michael F.; Smith, Robert Metcalf; Dodson, Thomas F. Medical Management of the Surgical Patient: A Textbook of Perioperative Medicine . Cambridge, UK: Cambridge University Press. ISBN 0-521-82800-7 . line feed character in |title= at position 55 ( help ) CS1 maint: Multiple names: authors list ( link )
↑ 13.0 13.1 Segal, Scott. "Women Presenting in Labor Should be Classified as ASA E: Pro" . Winter 2003 newsletter . SOAP . Retrieved 2007-07-09 .
↑ New classification of physical status. Anesthesiology 1963; 24:111

External links

American Society of Anesthesiologists
"Women Presenting in Labor Should be Classified as ASA E"
"Women Presenting in Labor Should NOT be Classified as ASA E"
"Is it true that ASA 1 excludes the very young and very old?"
"Anesthesia History Files"

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Clinical agreement in the American Society of Anesthesiologists physical status classification

Kayla M. Knuf ORCID: orcid.org/0000-0002-8505-3552 1 ,
Christopher V. Maani 1 &
Adrienne K. Cummings 1

Perioperative Medicine volume 7 , Article number: 14 ( 2018 ) Cite this article

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The American Society of Anesthesiologists physical status (ASA-PS) classification is not intended to predict risk, but increasing ASA-PS class has been associated with increased perioperative mortality. The ASA-PS class is being used by many institutions to identify patients that may require further workup or exams preoperatively. Studies regarding the ASA-PS classification system show significant variability in class assignment by anesthesiologists as well as providers of different specialties when provided with short clinical scenarios. Discrepancies in the ASA-PS accuracy have the potential to lead to unnecessary testing and cancelation of surgical procedures. Our study aimed to determine whether these differences in ASA-PS classification were present when actual patients were evaluated rather than previously published scenario-based studies.

A retrospective chart review was completed for patients >/= 65 years of age undergoing elective total hip or total knee replacements. One hundred seventy-seven records were reviewed of which 101 records had the necessary data. The outcome measures noted were the ASA-PS classification assigned by the internal medicine clinic provider, the ASA-PS classification assigned by the Pre-Anesthesia Unit (PAU) clinic provider, and the ASA-PS classification assigned on the day of surgery (DOS) by the anesthesia provider conducting the anesthetic care.

A statistically significant difference was shown between the internal medicine and the PAU preoperative ASA-PS designation as well as between the internal medicine and DOS designation (McNemar p = 0.034 and p = 0.025). Low kappa values were obtained confirming the inter-observer variation in the application of the ASA-PS classification of patients by providers of different specialties [Kappa of 0.170 (− 0.001, 0.340) and 0.156 (− 0.015, 0.327)].

Conclusions

There was disagreement in the ASA-PS class designation between two providers of different specialties when evaluating the same patients with access to full medical records. When the anesthesia-run PAU and the anesthesia assigned DOS ASA-PS class designations were evaluated, there was agreement. This agreement was seen between anesthesia providers regardless of education or training level. The difference in the application of the ASA-PS classification in our study appeared to be reflective of department membership and not reflective of the individual provider’s level of training.

As the concept of a single surgical procedure has transitioned to a comprehensive perioperative process, the outcomes of many major elective operations have improved. Care now focuses on a preoperative evaluation, early planning for discharge, and post-procedure rehabilitation (Donabedian 1966 ; Bader 2012 ). This integrated perioperative system promotes the combination of the three care phases: preoperative, intraoperative, and postoperative. As this transition of perioperative ideology continues, patients will benefit from multidisciplinary management for effective and efficient patient care (Adamina et al. 2011 ; Perioperative Surgical Home n.d. ).

The preoperative component requires comprehensive preoperative evaluations. This has resulted in a change from a simple day of surgery evaluation to the establishment of standardized preoperative clinics. The purpose of these more thorough preoperative clinics is to allow for deliberate and careful clinical evaluation with additional investigation and optimization of medical conditions as indicated to promote better patient outcomes and reduce unnecessary medical expenses. Studies have linked the implementation of preoperative clinics with improved patient outcomes such as decreased in-hospital mortality and cost-reduction due to a decrease in day of surgery cancelations (Hoyt n.d. ; Blitz et al. 2016 ; Whitlock et al. 2015 ). There are many types of preoperative clinics with multiple staffing models including providers from a variety of specialties and training levels (Johnson et al. 2014 ).

There are several components to a preoperative evaluation, including the American Society of Anesthesiologists Physical Status (ASA-PS) classification which was established in the 1940s and has since undergone multiple revisions. While not intended to predict risk, increasing ASA-PS class has been associated with increased perioperative mortality (Lemmens et al. 2008 ; Hopkins et al. 2016 ). The incidence of perioperative morbidity also rises with increasing ASA-PS class from 3.9% in an ASA 1 to 33.7% in an ASA 4 (Menke et al. 1993 ). As the perioperative system of care evolves, many institutions are attempting to maximize value via patient stratification, i.e. requiring only patients with higher ASA-PS classification scores to undergo formal preoperative evaluation and allowing those with lower ASA-PS classification scores to bypass preoperative clinics in an effort to streamline care. This has important implications as the provider who assigns the initial ASA-PS class stratifies the patient to either further preoperative evaluation or preoperative bypass. While the ASA-PS classification is one component of the preoperative evaluation, it has important ramifications in perioperative medicine as well as the practice of anesthesia. The classification affects surgical decision making, the anesthetic plan, and billing/reimbursement practices. Due to these consequences, it is important to have a consistent application of the ASA-PS classification system across providers, clinics, and specialties.

Studies regarding the ASA-PS classification system show significant variability in class assignment by anesthesiologists when provided with short clinical scenarios or hypothetical vignettes (Owens et al. 1978 ; Cuvillon et al. 2011 ; Mak et al. 2002 ; Riley et al. 2014 ). Variability is also seen in retrospective chart review comparing the ASA-PS class assigned at a preoperative clinic versus the ASA-PS class assigned in the operating room (Sankar et al. 2014 ). Inter-rater reliability is not the only issue with the ASA-PS class system, but intra-rater reliability which one would expect to show near perfect agreement has shown only moderate agreement in the pediatric cancer setting (Tollinche et al. 2018 ). Not only is there disagreement between anesthesia providers, but providers of different specialties also lack consistency. A recent study administered a survey of clinical scenarios to anesthesia providers, surgeons, and internists. In this study, providers of different specialties not only assigned an ASA-PS classification score less consistently, but they also had a tendency to underrate the class of the patients when compared to anesthesia providers given the same scenario (Curatolo et al. 2017 ; Eakin and Bader 2017 ).

When clinical scenarios are used to study the assignment of the ASA-PS classes, there are many limitations. Study participants are unable to ask for additional information or to extract and analyze applicable data from the medical record. Our study seeks to retrospectively assess the consistency of the ASA-PS class assignment between anesthesia providers and internists when evaluating patients undergoing total hip and total knee replacements at our institution during a 2-year period (Table 1 ). Due to variability in training and exposure to the ASA-PS classification system, our hypothesis predicted disagreement between the ASA-PS classes assigned by internal medicine and anesthesia providers on the same patient when both providers complete a history and physical exam with access to the entire medical record.

After obtaining IRB approval, this single-center study was completed. Surgical scheduling software was queried for all patients >/= 65 years of age undergoing elective total hip or total knee replacements with surgical dates between 01 Jan 2015 and 31 Dec 2016 at a contemporary military treatment facility (MTF). A total of 303 patients were screened in the specified time period. These records were reviewed to eliminate emergent cases as well as to ensure that the patients had visited both the internal medicine preoperative clinic and the preoperative anesthetic unit (PAU). The resulting 177 records were reviewed of which 101 records were assigned an ASA-PS classification by both the medicine preoperative clinic and the PAU clinic (Table 2 ). These were included in the data analysis (Fig. 1 ).

Consort diagram

At our institution, surgeons and anesthesia providers can make referrals to the internal medicine preoperative clinic based on clinical judgment. There is no algorithm that establishes which patients would benefit from additional resources in the form of an internal medicine preoperative visit. There is a stratification process in which the surgeons can determine who completes a PAU clinic visit versus who can bypass the PAU. Bypass is reserved for ASA-PS 1 and 2 patients. These patients are contacted telephonically by the PAU to determine if there are any outstanding issues that may need to be addressed by a PAU visit. The surgeons can refer ASA-PS 1 and ASA-PS 2 to the PAU based on their preference or if the surgeon believes they would benefit from seeing an anesthesia provider prior to the day of surgery. The order in which these visits occur is variable as the appointments are booked by the patient. The ASA-PS classification used in this study was the ASA-PS classification assigned following the initial encounter by both the PAU and the internal medicine clinic (Table 3 ).

For these records, the ASA classification from each visit as well as the day of surgery (DOS) ASA-PS class recorded by the anesthesia provider completing the case were collected. Supplemental data including age, BMI, gender, tobacco use, alcohol use, drug use, cardiac risk score, exercise tolerance (measured in metabolic equivalents), identified medical comorbidities, current medications, preoperative EKGs, additional preoperative cardiac study results, and preoperative pulmonary function test results were also collected (Table 4 ).

The outcome measures noted were the ASA-PS classification assigned by the internal medicine clinic provider, the ASA-PS classification assigned by the PAU clinic provider, and the ASA-PS classification assigned on the DOS by the anesthesia provider. There is no formal training in assigning an ASA-PS classification in our internal medicine department. Training is provided to PAU providers that are not anesthesia trained, specifically the Nurse Practioners and the Physician Assistants that see patients in the clinic.

Data analysis software was used to perform the following analyses [SPSS v22.0 (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp)]. To assess the overall disagreement between the data sets, a McNemar test was completed with the following pairings: medicine and PAU, medicine and DOS, and PAU and DOS. To assess the overall agreement between the data sets, kappa statistics along with 95% confidence intervals were calculated for the aforementioned pairings (Table 5 ).

Three ASA-PS classifications documented by separate medical providers in reference to the same patient were obtained via retrospective chart review. The source of these ASA-PS classification sets were from the internal medicine preoperative appointment, the anesthesia PAU appointment, and the DOS anesthesia record. Medicine preoperative ASA-PS classifications were performed by resident physicians from the Department of Medicine with staff physician supervision. ASA-PS classifications from the PAU were performed by anesthesia providers and non-anesthesia providers with varying levels of experience, while those from the DOS were performed solely by anesthesia providers. The levels of experience included Physician Assistants (PAs) working in the PAU, Nurse Practitioners (NPs) working in the PAU, Student Registered Nurse Anesthetists (SRNAs), Certified Registered Nurse Anesthetists (CRNAs), Anesthesiology Residents, and Staff Anesthesiologists.

One record was excluded from the analysis, as it was designated an ASA-PS of 1 by the DOS anesthesia provider but as an ASA-PS of 2 by both the medicine and the PAU provider. Due to the fact that there were no other ASA-PS 1 designations in the data set, the McNemar test could not be performed. The McNemar test can be used only on paired nominal data; thus, the model could not be met as there was only 1 observed value of ASA-PS class 1.

When the ASA-PS class designation was compared between the internal medicine and the PAU preoperative assessment as well as between the internal medicine preoperative assessment and DOS designation, there was a statistically significant difference (McNemar p = 0.034 and p = 0.025, respectively). On further analysis of these groups, low kappa values were obtained further confirming the inter-observer variation in the application of the ASA-PS classification of patients by providers of different specialties [Kappa of 0.170 (− 0.001, 0.340) and 0.156 (− 0.015, 0.327), respectively].

Among the sets of ASA-PS classification from the PAU and the DOS, the low McNemar value demonstrates that the null hypothesis of marginal homogeneity cannot be rejected in respect to these two data sets indicating that these two sets of data are not in disagreement. Furthermore, the kappa value for these two sets of classifications was 0.863 (0.696, 1.030) indicating near perfect agreement between the two groups regarding the ASA-PS class assigned.

The goal of this study was to determine inter-rater reliability of the ASA-PS assignment between anesthesia and internal medicine providers in two preoperative clinics. We found disagreement in the designated ASA-PS classification between these two providers when evaluating the same patient with access to his or her full medical record. When the anesthesia-run PAU and the anesthesia assigned DOS ASA-PS class designations were evaluated, there was agreement. Interestingly, over half of the PAU evaluations in this study were completed by PAs or NPs from the department of anesthesia. These were non-anesthesia providers who were oriented and trained by licensed anesthesia providers. Approximately half of the DOS evaluations were completed by staff physicians and staff CRNAs while the other half were completed by trainees (with either direct or indirect supervision by a privileged anesthesia provider). There was agreement seen between anesthesia department staff regardless of education or training level. The difference in the application of the ASA-PS classification in our study appeared to be reflective of department membership and not reflective of the individual provider’s level of training.

The agreement in ASA-PS assignment seen in the anesthesia department at our institution regardless of training level suggests that the standard application of the classification system can be taught and learned. It also specifically implies that non-anesthesia providers could more predictably rate ASA-PS after education and brief training sponsored by the Anesthesia Department. This competency could be achieved independent of education or training level. Improving the inter-rater reliability between providers of different specialties will improve communication, preoperative risk stratification patient optimization, and perioperative care. To our knowledge, no study has looked at ASA-PS classification between providers of different specialties using a retrospective review of existing patient data. Prior studies utilized surveys of hypothetical clinical scenarios focusing on straightforward medical problems without clinical evaluation or correlation. These studies had a correct or designated ASA-PS class which was used to evaluate the accuracy of responders. While “correctness” can be determined in hypothetical, “static” clinical scenarios, it cannot always be determined in clinical situations with an actual patient. In “real-life” clinical situations that are often evolving or dynamic, it is the inter-rater reliability that is most useful in the preoperative management of patients.

While the ASA-PS class designation by the anesthesia provider on the day of surgery is the only ASA-PS class that matters in regard to billing and charting, there are potential clinical implications to non-anesthesia providers assigning an ASA-PS class early in the perioperative process. According to the American College of Cardiology/American Heart Association (ACC/AHA) Guidelines on Perioperative Cardiovascular Evaluation and Management of Patients Undergoing Noncardiac Surgery, assessment is made of a major adverse cardiac event (MACE) which leads to further workup or proceeding directly to surgery (Fleisher et al. 2014 ). While this was traditionally done with the Revised Cardiac Risk Index (RCRI), two new tools, the Gupta Myocardial Infarction or Cardiac Arrest (MICA) calculator as well as the National Surgical Quality Improvement Program (NSQIP) Surgical Risk Calculator, are mentioned in the guidelines. Both of these tools require the assignment of a ASA-PS class to produce the estimated perioperative risk of MACE. These risk tools are utilized by non-anesthesia providers and are a part of the perioperative cardiac assessment which determines which patients require further testing prior to noncardiac surgery.

Not only is accuracy of the ASA-PS class necessary to ensure the appropriate preoperative workup, but consistent ASA-PS classification also ensures accuracy of survival prediction models as well as quality comparisons among institutions (Skaga et al. 2007 ; Kuza et al. 2017 ). The ASA-PS class is also used by NSQIP to compare quality of care among hospitals. A recent study showed that the misclassification of the ASA-PS class significantly impacted the observed/expected mortality leading to skewed data in quality assessment between institutions (Helkin et al. 2017 ).

Our study had several limitations. While the retrospective nature of this study eliminated some of the shortcomings of prior studies, it introduced new limitations inherent to a retrospective study. Specifically, we were unable to collect full data sets as the ASA-PS class was not measured in a large subset of the patient population. Additionally, all data was collected retrospectively from the medical record; thus, if either provider did not take a full medical history and account for all medical comorbidities that in and of itself could explain the differences in the ASA-PS classification. Secondly, due to the inclusion criteria used, ASA-PS classes 1 and 5 were not represented in this study. While this is likely not clinically relevant, without full representation of all classes, we were unable to determine the applicability of the results to ASA classes 1 and 5. Thirdly, a large number of medical records were excluded due to insufficient data. Specifically, the most common reason for an incomplete data set was that the ASA-PS classification was missing from the medicine preoperative appointment. If these 76 records had been included, the results and significance of the study may have been different. Lastly, this retrospective study was completed at a military treatment facility (MTF) which had several implications. The patient population consisted solely of active duty military, retirees, and their dependents. These patients had increased access to care and decreased cost of care when compared with a civilian population. As a result, this population may have had an improved baseline health status when compared with a civilian population which may have resulted in less patient variability.

While this study was retrospective in nature and conducted at a MTF, we believe that the results are applicable to civilian facilities. The disagreement between providers’ use of the ASA-PS classification system as well as lack of uniformity in preoperative evaluations offers an opportunity for improving perioperative outcomes and patient safety. As comprehensive perioperative care continues to expand in a multidisciplinary fashion, preoperative evaluations form the cornerstone of patient stratification and resource allocation. If evaluations cannot be completed in an appropriate and consistent manner across perioperative providers, there is the potential for increased cost and decreased quality of care.

While research shows the inconsistencies that exist in the application of the ASA-PS classification system, further study is needed to determine how to solve this issue. It is difficult to ascertain the etiology of the inconsistency. Is it secondary to a lack of knowledge, or does it point to a deeper issue with the classification system we use? The next step would be to design an educational intervention that focuses on application of a consistent approach to the ASA-PS classification system. If this intervention results in improvement of inter-rater reliability between specialties, the likely explanation is a lack of knowledge/familiarity.

In summary, there was a statistically significant difference in the application of the ASA-PS classification system between providers of the internal medicine department and the anesthesia department. In a clinical setting, the “right” ASA-PS classification is not nearly as important as reliable ASA-PS class designations between providers. The agreement between anesthesia providers of varying levels of training shows that consistent application is possible.

Declarations

The view(s) expressed herein are those of the author(s) and do not reflect the official policy or position of Brooke Army Medical Center, the U.S. Army Medical Department, the U.S. Army Office of the Surgeon General, the Department of the Air Force, the Department of the Army, the Department of Defense, or the U.S. Government.

Abbreviations

American College of Cardiology/American Heart Association Guidelines

American Society of Anesthesiologists physical status

Day of Surgery

Major Adverse Cardiac Event

Myocardial Infarction or Cardiac Arrest

National Surgical Quality Improvement Program

Pre-Anesthesia Unit

Revised Cardiac Risk Index

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Published: 02 May 2022

Discordant American Society of Anesthesiologists Physical Status Classification between anesthesiologists and surgeons and its correlation with adverse patient outcomes

Charlene Xian Wen Kwa 1 ,
Jiaqian Cui 1 ,
Daniel Yan Zheng Lim 2 ,
Yilin Eileen Sim 1 ,
Yuhe Ke 1 &
Hairil Rizal Abdullah 1 , 3

Scientific Reports volume 12 , Article number: 7110 ( 2022 ) Cite this article

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The American Society of Anesthesiologists Physical Status Classification (ASA) is used for communication of patient health status, risk scoring, benchmarking and financial claims. Prior studies using hypothetical scenarios have shown poor concordance of ASA classification among healthcare providers. There is a paucity of studies using clinical data, and of clinical factors or patient outcomes associated with discordant classification. The study aims to assess ASA classification concordance between surgeons and anesthesiologists, factors surrounding discordance and its impact on patient outcomes. This retrospective cohort study was conducted in a tertiary medical center on 46,284 consecutive patients undergoing elective surgery between January 2017 and December 2019. The ASA class showed moderate concordance (weighted Cohen’s κ 0.53) between surgeons and anesthesiologists. We found significant associations between discordant classification and patient comorbidities, age and race. Patients with discordant classification had a higher risk of 30-day mortality (odds ratio (OR) 2.00, 95% confidence interval (CI) = 1.52–2.62, p < 0.0001), 1-year mortality (OR 1.53, 95% CI = 1.38–1.69, p < 0.0001), and Intensive Care Unit admission > 24 h (OR 1.69, 95% CI = 1.47–1.94, p < 0.0001). Hence, there is a need for improved standardization of ASA scoring and cross-specialty review in ASA-discordant cases.

Introduction

The American Society of Anesthesiologists physical status classification (ASA class) is a widely utilized grading system first introduced in 1941 1 and revised in 1961 2 to assess and communicate the preoperative health of patients undergoing anesthesia. It consists of six categories ranging from Class 1 (describing a healthy patient) to Class 6 (referring to the brain-dead organ donor). Clinical examples for each ASA class were added in 2014 3 with the aim of improving inter-rater reliability or concordance 4 .

ASA scoring has significance both clinically and from a health services perspective. While ASA scoring alone is not intended for the prediction of perioperative risks 5 , it has been shown to be independently predictive of perioperative morbidity and mortality 6 and is included as part of several perioperative risk assessment tools that are widely used by surgeons and anesthesiologists. These include the National Surgical Quality Improvement Program risk calculator 7 , Gupta Myocardial Infarction or Cardiac Arrest calculator 8 , Surgical Outcome Risk Tool 9 and Combined Assessment of Risk Encountered in Surgery 10 , 11 . Discordance in ASA classification between healthcare providers is therefore concerning and may subject patients to contradictory risk counseling and inappropriate perioperative plans. At a health system level, discordant ASA scoring may undermine efforts for quality assurance 12 , allocations of critical care resources, risk-based remuneration for health outcomes and may result in potential financial costs from over-scoring 13 . ASA classes are also frequently reported in healthcare benchmarking exercises and payer billing documentations.

Multiple studies have reported moderate to poor concordance of the ASA class among various clinicians 14 , anesthesiologists 15 , 16 , 17 , 18 , 19 , 20 or restricted to specific patient cohorts 21 , 22 , 23 . One study examined specifically the agreement between anesthesiologists and surgeons using hypothetical patient scenarios 24 . There is a paucity of clinical data in this particular area. This is an important evidentiary gap as both specialties jointly manage patients undergoing surgeries. Furthermore, the association between discordant ASA classification and adverse patient outcomes has not been comprehensively studied previously.

To fill these knowledge gaps, our study aims to examine the concordance of ASA classification between surgeons scheduling patients for surgery and anesthesiologists conducting the outpatient preoperative evaluation. We further examined the clinical and demographic factors associated with discordant classification and whether discordant classification was associated with adverse postoperative outcomes.

Study design and data sources

This was a single-center retrospective cohort study conducted in Singapore General Hospital, the largest tertiary academic medical center in Singapore. It is a Level 1 Trauma Center and has all major surgical specialties other than pediatric surgery. The Singhealth Centralized Institutional Review Board (CIRB Reference Number 2020/2801) granted a waiver of consent due to the use of anonymized routinely collected clinical data and no patient interaction was required. All experimental protocols were approved by the Singhealth Centralized Institutional Review Board. The data analysis and statistical plan was written and filed before the data were accessed. All methods were carried out in accordance with relevant guidelines and regulations.

Our study cohort was extracted from the Perioperative and Anesthesia Subject Area, a curated electronic medical records database within our institution’s enterprise data warehouse (SingHealth-IHiS Electronic Health Intelligence System) which contains the records of all operative procedures performed since 2015. The system integrates patient information such as patient demographics, laboratory results, comorbidities and postoperative outcomes from multiple healthcare transactional systems, such as the hospital’s clinical information system (Sunrise Clinical Manager, Allscripts, Illinois, United States of America) and other administration and ancillary electronic systems. Mortality data on the system were synchronized with the National Electronic Health Records, including data from the National Registry of Births and Deaths, ensuring a near-complete mortality data follow-up.

In our institution, the ASA class is assigned by the surgeon on a standardized electronic admission form during the surgery listing process. Patients are then typically seen in the anesthesia preoperative clinic within a month of the surgery listing. Information on patient demographics, anthropometric parameters, preoperative comorbidities, and ASA class are routinely assessed by the attending anesthesiologist as part of structured clinical notes during the preoperative assessment, and are included within the database. The 2014 ASA scoring definition along with their published examples are available for reference in the anesthesia preoperative clinic and surgery clinic and is attached in Supplementary table S1 . The ASA classification by both anesthesiologists and surgeons in this study is hence based on the 2014 revision. While the anesthesiologist can potentially access the surgeon’s ASA class, it is usually independently assigned in our center. There are no financial incentives in assigning a higher ASA class both for anesthesiologists and surgeons within our local healthcare system.

Participant cohort and variables

We included all patients aged 18 years old and above undergoing elective surgery under general or regional anesthesia or monitored anesthesia care between January 2017 and December 2019. Patients who underwent cardiothoracic surgery, transplant surgery, or surgery for burns injuries were excluded. Patients planned for elective cardiothoracic surgery in our center have the ASA field in the preoperative structured clinical note filled by the surgeon themselves (unlike other surgical patients, where the ASA field would be populated by an anesthesiologist), while patients requiring transplant surgery would usually have a standardized ASA class as there is organ failure necessitating the surgery. Patients with a missing ASA class by either the surgeon or anesthesiologist and patients assigned an ASA class of 5 or 6 by either the surgeon or anesthesiologist were also excluded (Fig. 1 ).

Study flow diagram for patient cohort definition. *The exclusions for patients not explicitly coded as elective surgeries and patients with ASA 5 or 6 are overlapping categories, and as a result sum to more than the difference between the first two steps.

For each patient, we obtained preoperative data such as age, sex, race, surgical specialty, and comorbidities including ischemic heart disease, congestive heart failure, cerebrovascular accidents, diabetes mellitus requiring insulin, and hypertension. These comorbidities are assessed by the anesthesiologist as part of the Revised Cardiac Risk Index, which is routinely used in our institution 25 . The ASA classes assigned by both the anesthesiologist and surgeon were obtained, and the relevant clinical outcomes (death within 30 days, death within 1 year, ICU admission for > 24 h) were determined.

Supplementary table S2 compares the characteristics of 264 patients who were excluded from our study as they had no valid ASA class. All 264 patients had missing anesthesiologist ASA class and there was no statistically significant difference between patients in the final cohort and the excluded patients for demographic variables (age, sex, race) and clinical outcomes. Among the excluded patients, there were fewer that had anesthesiologist-assessed comorbidities, and the differences were statistically significant for some. Our interpretation is that patients with incomplete anesthesiologist ASA class were more likely to have other areas incompletely assessed by the anesthesiologist. Overall, the number of such patients is small and not deemed to be a major source of bias.

Statistical analysis

Analyses were performed using Python version 3.7.1 and R version 4.0.2 with their base utility functions. Additional packages used in R included the “questionr” package for logistic regression, “pROC” for receiver operating characteristic curve analyses, and “irr” for concordance analyses.

Assessment of agreement between surgeon and anesthesiologist ASA classification

Cross tabulation was performed for the anesthesiologist’s ASA class against the surgeon’s ASA class. Concordance between these two variables was determined using Cohen’s weighted κ. The κ-statistic was interpreted in the manner of Altman as poor (0–0.2), fair (0.21–0.4), moderate (0.41–0.6), good (0.61–0.8) and very good (0.81–1.0) agreement 26 .

Our sample was drawn from a database that exhaustively documents all surgeries performed within the hospital, and we considered all sequential patients within the study time frame (January 2017–December 2019). As a comparison, the sample size calculation to detect a moderate agreement (κ > 0.4) and exclude a fair agreement (κ = 0.2) with a one-sided 95% confidence interval and 90% power is 186.

Descriptive statistics for overall cohort and subgroup analyses of discordant ASA classes

Descriptive statistics were calculated and expressed as counts and percentages for categorical data, and means with standard deviation for continuous data. The cohort was stratified into patients with concordant and discordant ASA classes. Univariate statistical analysis was performed using the chi-square test for categorical variables and the t-test for continuous variables. Subgroup analyses were also performed comparing patients where the surgeon assigned a lower ASA class against patients with a concordant ASA class, and likewise comparing patients where the anesthesiologist assigned a lower ASA class against patients with a concordant ASA class. In view of the multiple statistical comparisons, Bonferroni’s correction was used and the p-value cut-off for statistical significance was determined to be p < 0.001.

Effect of discordant ASA classification on clinical outcomes

The discordance of ASA classification between surgeons and anesthesiologists was calculated and stratified in several different ways. Three forms to express discordance were used. Firstly, as a binary variable representing whether the ASA classes were discordant or not; secondly, as a ternary variable representing whether the ASA classes were concordant, surgeon ASA class was lower, or anesthesiologist ASA class was lower; and lastly, as the raw difference with appropriate binning of categories with low counts. These variables, representing different ways of stratifying the degree of ASA classification discordance, were separately entered as the sole predictive variable into logistic regression models. A separate model was fitted for each of the clinical outcomes of death within 30 days, death within 1 year, and ICU admission for > 24 h. The unadjusted odds ratios and p-values were calculated for each stratum of ASA discordance, with the ASA concordant patients as the reference group.

In this analysis, we did not include any predictive factors besides ASA discordance. This is because ASA discordance should in theory have no significant effects on clinical outcome and it cannot be regarded as a prognostic marker per se. Rather, any significant effect of ASA discordance would suggest that it is a red flag indicator of potential shortcomings in the clinical care process. Any underlying factor to the ASA discordance, would necessarily have a collinear relationship with the ASA discordance itself. Hence it would not be appropriate to enter other factors alongside ASA discordance into the same regression model, particularly if these factors are suspected to be the cause of ASA discordance itself. These are analyzed separately in the prior section.

Concordance of surgeon and anesthesiologist ASA classification

Our final study cohort comprised 46,284 patients, of which 46.4% (21,474/46,284) were male and 53.6% (24,810/46,284) were female. The cross-tabulation of surgeon and anesthesiologist ASA class for all cases is presented in Table 1 . The weighted Cohen’s κ for concordance between surgeon and anesthesiologist class was 0.53, signifying moderate agreement.

Descriptive statistics and stratified analyses

67.4% of patients (31,186/46,284) had a concordant ASA class given by both surgeons and anesthesiologists. Descriptive statistics for the ASA concordant and discordant groups are presented in Table 2 . 79.4% of patients with discordant classes (11,985/15,098) had a lower ASA class assigned by the surgeon, and 20.6% (3113/15,098) had a lower ASA class assigned by the anesthesiologist.

For all baseline patient characteristics, there were significant differences in the presence of comorbidities between patients with concordant and discordant classes, with exception of the male sex and the presence of raised creatinine. Comorbidities that were present in a higher proportion of patients with discordant ASA class compared to those with concordant ASA class include a history of ischemic heart disease (14.1% vs. 8%, p < 0.0001), cerebrovascular accident (4.8% vs. 2.7%, p < 0.0001) and congestive heart failure (3% vs. 2%, p < 0.0001) and the presence of diabetes mellitus on insulin (4.8% vs. 3.3%, p < 0.0001). Patients with discordant ASA classifications also had a younger mean age (56 vs. 59 years old, p < 0.0001). All surgical specialties which were included also had significant differences with respect to discordance. Discordant ASA classification overall was associated with a higher risk of all adverse outcomes- death at 30 days, death at 1 year, and ICU admission of more than 24 h. When the discordant ASA classes were further stratified, we observed that a lower surgeon ASA class was associated with all negative outcomes. For patients where the surgeon ASA class was lower, the risk of a negative outcome was increased when there was greater difference between the surgeon and anesthesiologist ASA classification. On the other hand, a lower anesthesiologist ASA class was only associated with ICU admission > 24 h but not death at 30 days or 1 year. This is depicted in Fig. 2 , with additional details included in Supplementary table S3 .

Odds Ratio Plots for Risk of Adverse Outcomes with Different Levels of ASA Discordance. ( a ) Odds Ratio for death within 30 days; ( b ) Odds Ratio for death within 1 year; ( c ) Odds Ratio for ICU admission > 24 h. A lower surgeon ASA class as compared to the anesthesiologist class was associated with all three outcomes. On the other hand, a lower anesthesiologist ASA class was only associated with ICU admission > 24 h but not death at 30 days or 1 year.

We also conducted a subgroup comparison of the effects of discordant ASA class on clinical outcomes within the lower ASA class 1–2 groups, as well as within the higher ASA class 3–4 groups (Supplementary table S4 ). This showed significant difference in clinical outcomes when the discordance was between ASA class 3 and 4, whereas there was no difference in clinical outcomes when the discordance was between ASA classes 1 and 2.

General discussion

Our results demonstrate differences in ASA classification between surgeons and anesthesiologists in clinical practice after the addition of clinical examples in 2014, which have previously been studied only in hypothetical scenarios 24 , 27 or between anesthesiologists and Internal Medicine providers 14 . Furthermore, we found that discordant ASA classification is associated with adverse outcomes, particularly when the surgeon-assigned ASA class is lower.

The observed moderate concordance (κ 0.53) in our study is consistent with that reported in the retrospective cohort study by Sankar et al. between anesthesiologists in the preoperative clinic and on the day of surgery (κ value 0.61) before the 2014 ASA update 28 . Another study by Abouleish et al. of concordance between anesthesiologists in the preoperative clinic and on the day of surgery had similar results (κ value 0.62), but subsequently demonstrated ‘very good’ agreement (κ value 0.85) after the introduction of examples that were ASA and institutionally approved 29 .

The majority of discordant classification involved a lower class assigned by surgeons, with the largest group comprising those assigned ASA 3 by the anesthesiologist but ASA 2 by the surgeon. We observed that patients with discordant ASA class had a significantly higher proportion of comorbid clinical conditions (raised creatinine, diabetes mellitus on insulin, history of congestive heart failure, cerebrovascular accident, ischemic heart disease and smoking). This reflects the continuing subjectivity of the ASA scoring system despite the 2014 update, which was intended to improve concordance. The differences in recognition and perceived significance of comorbidities are likely to be a major contributing factor to discordant ASA classification. Of note, ASA-approved examples are not present on both the electronic forms used by the surgeon and the anesthesiologist. However, both groups of physicians have been familiarized with the classification and its examples via both regular and ad-hoc training sessions, and a hard copy of the examples are available in the both clinics’ resource folder for convenient perusal. There may be further need for standardization and education efforts in both clinics following this study.

As the ASA class is a component of several major surgical risk scoring systems used by both surgeons and anesthesiologists in clinical care, discordant ASA classification can adversely impact the reliability of perioperative risk scoring and subsequent risk counseling. The ASA class is routinely used in deciding what preoperative tests a patient requires at our institution and in other countries such as the United Kingdom 30 . Overestimation of the ASA class would increase the number of investigations a patient has before surgery, incurring unnecessary financial costs to the patient and healthcare system, while an underestimation of the ASA class may compromise patient safety. At the health system level, discordant classification can also affect the allocation of critical care resources and undermine the use of the ASA class in healthcare reimbursement and quality assurance efforts. This may disadvantage healthcare institutions financially and in inter-institutional rankings depending on which class is being reported to the external agencies. Other studies have shown that the addition of examples to the ASA class and reinforcement of its use were required to improve reliability 4 , 29 . Standardization efforts are needed to improve the utility of ASA classification in clinical practice and for uses beyond the original intention of communicating patient healthcare status.

We also note that certain demographic factors were associated with discordant ASA classification, such as in younger patients and those of minority ethnicity. We postulate that younger patients may be perceived to have lower severity of disease by some clinicians, hence grading them with a lower class. Minority race patients may face communication or cultural barriers in disease and symptom communication and this may adversely affect accurate healthcare assessment. Additionally, there could be an element of implicit racial bias among healthcare professionals against minority race patients, which has been exhibited in healthcare settings 31 . Ideally, demographic factors should not influence ASA scoring, which should be an objective reflection of patient physical status. This finding further supports the need for better standardization and education on ASA scoring. Further studies on special populations, such as pregnant patients, may also be useful.

Our study revealed that patients with discordant ASA classification had poorer clinical outcomes. All ASA discordant patients had a higher risk of ICU admission > 24 h, in overall and stratified analyses.

With respect to mortality, stratified analyses of discordant ASA classification showed that patients whose surgeon assigned a lower class had a higher risk of 30-day and 1-year mortality. The lower the surgeon ASA class was compared to the anesthesiologist ASA class, the higher the risk was for 30-day and 1-year mortality. In contrast, patients with discordant ASA who were classified lower by their anesthesiologist did not have such an association. This is noteworthy, given that simple differences in medical opinion leading to discordant patient assessments would not ordinarily be expected to correlate with patient outcomes. Failure to recognize a high perioperative risk patient or interval development of comorbidity in the short timespan between surgeon and anesthesiologist review could have contributed to the poorer patient outcomes seen in this group. A breakdown in communication of identified risks between surgeon and anesthesiologist may also be a significant mechanism by which ASA discordance may occur. However, in the absence of independent adjudication, it would be difficult to ascertain the extent to which this applies.

Finally, discrepancy between ASA class 1–2 grading among surgeons and anesthesiologists had no significant correlation with clinical outcomes, whereas discrepancy between the higher classes of 3–4 was significantly associated with death at 30 days and ICU admission > 24 h (Supplementary table S4 ). Further training should emphasize the importance of distinguishing the higher ASA classes as discrepancy at this level will have a significant impact on clinical outcomes.

Study strengths and limitations

Our study’s main strengths are that it was conducted in a large patient cohort spanning multiple years and encompassing the major categories of elective noncardiac surgery. Data collected was from 2017 to 2019, after the 2014 ASA update and with adequate time-lapse for familiarization, and before the 2020 ASA update to include clinical examples for obstetric and pediatric patients 5 . The study cohort hence does not span periods with potentially different interpretations of the ASA classification system. Further studies of data before and after the 2020 ASA update could be done to evaluate the implications of ASA discordance in special populations, such as obstetric and pediatric patients.

The data used was derived from clinical databases, rather than administrative or financial records. Furthermore, neither surgeons nor anesthesiologists have financial incentives tied to ASA scoring at our institution. This eliminates an important source of bias as its presence has been shown to be associated with potential upcoding of the ASA class 32 .

A limitation of our study is that the assignment of ASA class by surgeons and anesthesiologists for each patient was not done simultaneously. At our institution, surgeons assign the ASA class when listing the patient for surgery and anesthesiologists assign their class after that at the preoperative assessment. As such, while the surgeon is completely blinded to the anesthesiologist’s class, the anesthesiologist could be aware of the surgeon’s class. However, our anesthesiologists generally make an independent assessment of the patient’s healthcare status. The anesthesiologist assessment is also closer to the day of surgery than the surgeon’s and hence the anesthesiologist’s class has better recency. It is also possible that the patient’s health could have deteriorated in the period of time between the surgeon and anesthesiologist review, accounting for class discordance and association with poorer outcomes. However, the waiting time for preoperative assessment at our institution is generally short and most elective surgeries are premised on a relatively stable patient physical status. We do not deem this to be a major source of bias.

As near- contemporaneous ASA scoring was mandatory for both anesthesiologist and surgeon during the study period, potential sources of bias (e.g. recall bias, selection bias) that may affect retrospective studies are much less likely in our study. There was a very small proportion of potential patients (264 patients, < 1%) who had missing anesthesiologist ASA class. However, as addressed in Supplementary Table S2 , this is unlikely to be a major source of bias.

As our study only included patients who underwent elective surgery, its findings should not be generalized to emergency cases. Cardiothoracic, burns, and transplant surgery patients were also excluded, and our results may not apply in these groups of patients. Finally, as this was a single center study, this may limit generalizability, particularly in centers where ASA class impacts financial reimbursements (which is not present in our center) or centers with significantly different care patterns or patient comorbidity profiles.

Opportunities for future work

Our study data did not contain information that could individually identify the anesthesiologists or the surgeons assigning ASA class. As such, we were unable to control for clinician factors that might have influenced the accuracy of the ASA classification, such as level of training and seniority. Our information about comorbidities assessed by the clinicians, which directly impacts the ASA class, was limited to the anesthesiologists only (as there was no standardized assessment form for surgeon-assessed comorbidities during the period of study). Future analyses of ASA discordance may investigate these aspects further, to better understand the mechanisms of ASA discordance and other possible factors that influence it.

The association of discordant ASA classification with adverse patient outcomes is a cause for concern. Besides further education and reinforcement of standard ASA examples, there may be a need for quality improvement studies to determine if specific conditions require more detailed or contextualized examples within the institution. Discordant ASA classes may be a red flag for missed comorbidities or interval development of new comorbidities, and mandatory cross-specialty review in ASA discordant cases is a potential intervention to ensure that patients are accurately assessed and appropriately prepared for surgery.

In a large single-center cohort study that was performed after the 2014 update of the ASA class, there was moderate concordance between ASA classes assigned by anesthesiologists and surgeons in patients undergoing elective surgery. The majority of discordant patients were assigned a lower class by surgeons and is likely due to differences in recognition and grading of comorbidities. Patients with discordant ASA classes, and in particular those assigned lower ASA classes by surgeons, had a higher likelihood of 30-day mortality, 1-year mortality, and ICU admission > 24 h. Our results suggest a need for improvement in the standardization of ASA scoring and that discordant ASA assessments may be a red flag for missed comorbidities.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Charlene Xian Wen Kwa, Jiaqian Cui, Yilin Eileen Sim, Yuhe Ke & Hairil Rizal Abdullah

Health Service Research Unit, Medical Board, Singapore General Hospital, Outram Road, Singapore, 169608, Singapore

Daniel Yan Zheng Lim

Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore

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C.X.W.K., principal investigator: Study design, interpretation of data, drafting of manuscript. J.C.: interpretation of data, drafting of manuscript. D.Y.Z.L.: data collection and analysis, interpretation of results, drafting of manuscript, figure drawing. Y.E.S.: revision of manuscript. Y.K.: revision of manuscript. H.R.A.: Research idea, study design, revision of manuscript, supervision. All authors read and approved the final manuscript.

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Kwa, C.X.W., Cui, J., Lim, D.Y.Z. et al. Discordant American Society of Anesthesiologists Physical Status Classification between anesthesiologists and surgeons and its correlation with adverse patient outcomes. Sci Rep 12 , 7110 (2022). https://doi.org/10.1038/s41598-022-10736-5

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Received : 08 February 2022

Accepted : 11 April 2022

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DOI : https://doi.org/10.1038/s41598-022-10736-5

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What is an ASA Score in Surgery?
The ASA (American Society of Anesthesiology) score is a metric to
American Society of Anesthesiologists Staging
... determined by the anesthesiologist after considering patient presentation, history, and functional limitations. Assigning this score, ranked ASA I through
Statement on ASA Physical Status Classification System
The purpose of the system is to assess and communicate a patient's pre-anesthesia medical co-morbidities. The classification system alone does
ASA Physical Status Classification System
... determine the health of a person before a surgical procedure that requires anesthesia. ... They'll determine your ASA score shortly before your
ASA physical status classification system
Patients who must have anesthesia to arrest a secondary hemorrhage where the
Machine Learning Algorithm to Perform the American Society of
In addition, the ASA PS score is used in the determination of anesthesia coding and billing. ... physician anesthesiologist inter-variability
variability in the american society of anesthesiologists (asa)
There is a recognized need to know the risks of anesthesia apart from
Assignment of pre-event ASA physical status classification by pre
The pre-hospital physicians did not have access to the full patient records. If the physician was unable to decide on a pre-event ASA-PS score
ASA score
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Clinical agreement in the American Society of Anesthesiologists
ASA physical status assignment by non-anesthesia providers: do surgeons consistently downgrade the ASA score preoperatively? J Clin Anest
Discordant American Society of Anesthesiologists Physical Status
ASA physical status assignment by non-anesthesia providers: Do surgeons consistently downgrade the ASA score preoperatively?. J. Clin

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Discordant American Society of Anesthesiologists Physical Status Classification between anesthesiologists and surgeons and its correlation with adverse patient outcomes

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Effect of discordant ASA classification on clinical outcomes

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