术后死亡率和护理机构不良出院麻醉风险评估评分(ARAS)的发展

IF 5 2区 医学 Q1 ANESTHESIOLOGY
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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引用次数: 0

摘要

我们开发了一份简单的调查问卷,供外科医生办公室在与病人会面预约病例时使用。在这项研究中,我们使用这些问题来评估它们对死亡率和护理机构不良出院的预测价值,并将其与美国麻醉师身体状况学会(ASA-PS)和其他风险评估评分进行比较。方法:我们分析了2016年1月至2023年2月在纽约州布朗克斯区蒙特菲奥雷医学中心(Montefiore Medical Center)接受非门诊手术的成年患者的数据。预定的问卷项目被定义为候选预测因子。逐步向后排除法用于确定手术30天内死亡率的独立预测因素。采用受试者工作特征曲线下面积(ROC-AUC)评估模型辨识度,并与ASA- ps、机器学习ASA [ML-ASA]、修正心脏风险指数[RCRI]和修正5项脆弱指数[mFI-5]评分进行比较。同样,该模型在预测非家庭(不良)排放方面进行了评估。采用独立队列进行内部验证。结果在59099例患者中,891例(1.53%)患者术后30天内死亡,5013例(9.1%)患者不良出院。最终的麻醉风险评估评分(ARAS)模型由6个独立预测因素组成,包括卒中史、癫痫发作史、心力衰竭/起搏器或除颤器植入史、肝功能衰竭史、血液或出血性疾病史、代谢当量≤4。该模型对术后30天死亡率的预测能力优于ASA-PS、ML-ASA、RCRI和mFI-5 [AUC 0.82] [0.78, 0.79, 0.76, 0.72];p & lt;分别为0.001)。在预测不良排出时也有类似的表现[AUC 0.76 vs 0.70, 0.74, 0.65, 0.73;p & lt;分别为0.001)。在验证队列(n = 13,137)中,结果仍然稳健。结论直接从患者处获得的6个临床问题可预测术后死亡率和不良出院。预测准确性与ASA-PS、RCRI和mFI-5评分相当,其优势在于能够在临床医生输入之前用于术前评估分诊过程的早期。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
CiteScore
7.40
自引率
4.50%
发文量
346
审稿时长
23 days
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.
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