Journal of clinical anesthesia · 2025
Development of the Anesthesia Risk Assessment Score (ARAS) for postoperative mortality and adverse discharge to a nursing facility.
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Khandaker Rafi, Wongtangman Karuna, Frank Marcus, Borngaesser Felix, Smith Richard V, Nie Linda, Garg Shweta, Tufail Bilal, Freda Jeffrey, Anand Preeti, Aguirre-Alarcon Adela, Eikermann Matthias, Himes Carina P
Аннотация
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.
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.