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A machine learning approach to predicting early and late postoperative reintubation
Journal of Clinical Monitoring and Computing ( IF 2.2 ) Pub Date : 2022-09-03 , DOI: 10.1007/s10877-022-00908-z
Mathew J Koretsky 1 , Ethan Y Brovman 2 , Richard D Urman 3 , Mitchell H Tsai 4 , Nick Cheney 5
Affiliation  

Accurate estimation of surgical risks is important for informing the process of shared decision making and informed consent. Postoperative reintubation (POR) is a severe complication that is associated with postoperative morbidity. Previous studies have divided POR into early POR (within 72 h of surgery) and late POR (within 30 days of surgery). Using data provided by American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP), machine learning classification models (logistic regression, random forest classification, and gradient boosting classification) were utilized to develop scoring systems for the prediction of combined, early, and late POR. The risk factors included in each scoring system were narrowed down from a set of 37 pre and perioperative factors. The scoring systems developed from the logistic regression models demonstrated strong performance in terms of both accuracy and discrimination across the different POR outcomes (Average Brier score, 0.172; Average c-statistic, 0.852). These results were only marginally worse than prediction using the full set of risk variables (Average Brier score, 0.145; Average c-statistic, 0.870). While more work needs to be done to identify clinically relevant differences between the early and late POR outcomes, the scoring systems provided here can be used by surgeons and patients to improve the quality of care overall.



中文翻译:

预测早期和晚期术后再插管的机器学习方法

准确估计手术风险对于告知共享决策和知情同意过程非常重要。术后再插管 (POR) 是一种与术后并发症有关的严重并发症。既往研究将 POR 分为早期 POR(手术后 72 小时内)和晚期 POR(手术后 30 天内)。使用美国外科医师学会 (ACS) 国家外科质量改进计划 (NSQIP) 提供的数据,利用机器学习分类模型(逻辑回归、随机森林分类和梯度增强分类)开发用于预测联合、早期手术的评分系统, 和迟到的 POR。每个评分系统中包含的风险因素是从一组 37 个术前和围手术期因素中缩小的。从逻辑回归模型开发的评分系统在不同 POR 结果的准确性和辨别力方面表现出色(平均 Brier 评分,0.172;平均 c 统计量,0.852)。这些结果仅比使用全套风险变量的预测稍差(平均 Brier 评分,0.145;平均 c 统计量,0.870)。虽然需要做更多的工作来确定早期和晚期 POR 结果之间的临床相关差异,但外科医生和患者可以使用此处提供的评分系统来提高整体护理质量。这些结果仅比使用全套风险变量的预测稍差(平均 Brier 评分,0.145;平均 c 统计量,0.870)。虽然需要做更多的工作来确定早期和晚期 POR 结果之间的临床相关差异,但外科医生和患者可以使用此处提供的评分系统来提高整体护理质量。这些结果仅比使用全套风险变量的预测稍差(平均 Brier 评分,0.145;平均 c 统计量,0.870)。虽然需要做更多的工作来确定早期和晚期 POR 结果之间的临床相关差异,但外科医生和患者可以使用此处提供的评分系统来提高整体护理质量。

更新日期:2022-09-04
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