Rafi Khandaker BA , Karuna Wongtangman MD , Marcus Frank , Felix Borngaesser MD , Richard V. Smith MD , Linda Nie BA , Shweta Garg MS , Bilal Tufail MD , Jeffrey Freda MD, MBA , Preeti Anand MD , Adela Aguirre-Alarcon MD , Matthias Eikermann MD, PhD , Carina P. Himes MD
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In this study, we used these questions to evaluate their predictive value for mortality and adverse discharge to a nursing facility in comparison with the American Society of Anesthesiologists Physical Status [ASA-PS] and other risk assessment scores.</div></div><div><h3>Methods</h3><div>We analyzed data from adult patients undergoing non-ambulatory surgery between January 2016 and February 2023 at Montefiore Medical Center, a tertiary academic center in the Bronx, NY. The predetermined questionnaire items were defined as candidate predictors. Stepwise backwards elimination was used to identify independent predictors of mortality within 30 days of surgery. Model discrimination was assessed using area under the receiver operating characteristic curve [ROC-AUC] and was compared with ASA-PS, machine learning ASA [ML-ASA], Revised Cardiac Risk Index [RCRI], and Modified 5 Item Frailty Index [mFI-5] scores. Similarly, the model was evaluated in predicting non-home (adverse) discharge. Internal validation was performed using an independent cohort.</div></div><div><h3>Results</h3><div>In a developmental cohort of 59,099 patients, 891 (1.53 %) patients died within 30 days after surgery and 5013 (9.1 %) were adversely discharged. The final Anesthesia Risk Assessment Score [ARAS] model consisted of 6 independent predictors including history of stroke, seizure, heart failure/pacemaker or defibrillator implantation, liver failure, blood or bleeding disorder, and metabolic equivalents ≤4. The model showed superior predictive ability for 30-day postoperative mortality [AUC 0.82] compared to ASA-PS, ML-ASA, RCRI and mFI-5 [0.78, 0.79, 0.76, 0.72; p < 0.001, respectively]. Similar performance was observed when predicting adverse discharge [AUC 0.76 vs 0.70, 0.74, 0.65, 0.73; p < 0.001, respectively]. The results remained robust in the validation cohort (n = 13,137).</div></div><div><h3>Conclusion</h3><div>Six clinical questions that can be obtained directly from patients predict postoperative mortality and adverse discharge. The predictive accuracy is comparable to the ASA-PS, RCRI, and mFI-5 scores, with the advantage of being able to be used early in the preoperative evaluation triage process prior to clinician input.</div></div>","PeriodicalId":15506,"journal":{"name":"Journal of Clinical Anesthesia","volume":"106 ","pages":"Article 111918"},"PeriodicalIF":5.0000,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of the Anesthesia Risk Assessment Score (ARAS) for postoperative mortality and adverse discharge to a nursing facility\",\"authors\":\"Rafi Khandaker BA , Karuna Wongtangman MD , Marcus Frank , Felix Borngaesser MD , Richard V. Smith MD , Linda Nie BA , Shweta Garg MS , Bilal Tufail MD , Jeffrey Freda MD, MBA , Preeti Anand MD , Adela Aguirre-Alarcon MD , Matthias Eikermann MD, PhD , Carina P. Himes MD\",\"doi\":\"10.1016/j.jclinane.2025.111918\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>We developed a simple questionnaire that the surgeon's office uses when meeting with their patients to book a case. In this study, we used these questions to evaluate their predictive value for mortality and adverse discharge to a nursing facility in comparison with the American Society of Anesthesiologists Physical Status [ASA-PS] and other risk assessment scores.</div></div><div><h3>Methods</h3><div>We analyzed data from adult patients undergoing non-ambulatory surgery between January 2016 and February 2023 at Montefiore Medical Center, a tertiary academic center in the Bronx, NY. The predetermined questionnaire items were defined as candidate predictors. Stepwise backwards elimination was used to identify independent predictors of mortality within 30 days of surgery. Model discrimination was assessed using area under the receiver operating characteristic curve [ROC-AUC] and was compared with ASA-PS, machine learning ASA [ML-ASA], Revised Cardiac Risk Index [RCRI], and Modified 5 Item Frailty Index [mFI-5] scores. Similarly, the model was evaluated in predicting non-home (adverse) discharge. Internal validation was performed using an independent cohort.</div></div><div><h3>Results</h3><div>In a developmental cohort of 59,099 patients, 891 (1.53 %) patients died within 30 days after surgery and 5013 (9.1 %) were adversely discharged. The final Anesthesia Risk Assessment Score [ARAS] model consisted of 6 independent predictors including history of stroke, seizure, heart failure/pacemaker or defibrillator implantation, liver failure, blood or bleeding disorder, and metabolic equivalents ≤4. The model showed superior predictive ability for 30-day postoperative mortality [AUC 0.82] compared to ASA-PS, ML-ASA, RCRI and mFI-5 [0.78, 0.79, 0.76, 0.72; p < 0.001, respectively]. Similar performance was observed when predicting adverse discharge [AUC 0.76 vs 0.70, 0.74, 0.65, 0.73; p < 0.001, respectively]. The results remained robust in the validation cohort (n = 13,137).</div></div><div><h3>Conclusion</h3><div>Six clinical questions that can be obtained directly from patients predict postoperative mortality and adverse discharge. 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Development of the Anesthesia Risk Assessment Score (ARAS) for postoperative mortality and adverse discharge to a nursing facility
Background
We developed a simple questionnaire that the surgeon's office uses when meeting with their patients to book a case. In this study, we used these questions to evaluate their predictive value for mortality and adverse discharge to a nursing facility in comparison with the American Society of Anesthesiologists Physical Status [ASA-PS] and other risk assessment scores.
Methods
We analyzed data from adult patients undergoing non-ambulatory surgery between January 2016 and February 2023 at Montefiore Medical Center, a tertiary academic center in the Bronx, NY. The predetermined questionnaire items were defined as candidate predictors. Stepwise backwards elimination was used to identify independent predictors of mortality within 30 days of surgery. Model discrimination was assessed using area under the receiver operating characteristic curve [ROC-AUC] and was compared with ASA-PS, machine learning ASA [ML-ASA], Revised Cardiac Risk Index [RCRI], and Modified 5 Item Frailty Index [mFI-5] scores. Similarly, the model was evaluated in predicting non-home (adverse) discharge. Internal validation was performed using an independent cohort.
Results
In a developmental cohort of 59,099 patients, 891 (1.53 %) patients died within 30 days after surgery and 5013 (9.1 %) were adversely discharged. The final Anesthesia Risk Assessment Score [ARAS] model consisted of 6 independent predictors including history of stroke, seizure, heart failure/pacemaker or defibrillator implantation, liver failure, blood or bleeding disorder, and metabolic equivalents ≤4. The model showed superior predictive ability for 30-day postoperative mortality [AUC 0.82] compared to ASA-PS, ML-ASA, RCRI and mFI-5 [0.78, 0.79, 0.76, 0.72; p < 0.001, respectively]. Similar performance was observed when predicting adverse discharge [AUC 0.76 vs 0.70, 0.74, 0.65, 0.73; p < 0.001, respectively]. The results remained robust in the validation cohort (n = 13,137).
Conclusion
Six clinical questions that can be obtained directly from patients predict postoperative mortality and adverse discharge. The predictive accuracy is comparable to the ASA-PS, RCRI, and mFI-5 scores, with the advantage of being able to be used early in the preoperative evaluation triage process prior to clinician input.
期刊介绍:
The Journal of Clinical Anesthesia (JCA) addresses all aspects of anesthesia practice, including anesthetic administration, pharmacokinetics, preoperative and postoperative considerations, coexisting disease and other complicating factors, cost issues, and similar concerns anesthesiologists contend with daily. Exceptionally high standards of presentation and accuracy are maintained.
The core of the journal is original contributions on subjects relevant to clinical practice, and rigorously peer-reviewed. Highly respected international experts have joined together to form the Editorial Board, sharing their years of experience and clinical expertise. Specialized section editors cover the various subspecialties within the field. To keep your practical clinical skills current, the journal bridges the gap between the laboratory and the clinical practice of anesthesiology and critical care to clarify how new insights can improve daily practice.