Sahni Nishant, Tourani Roshan, Sullivan Donald, Simon Gyorgy
Division of General Internal Medicine, University of Minnesota, Delaware Street SE, MMC 741, Minneapolis, MN, USA.
Institute of Health Informatics, University of Minnesota, Minneapolis, MN, USA.
J Gen Intern Med. 2020 May;35(5):1413-1418. doi: 10.1007/s11606-020-05733-1. Epub 2020 Mar 10.
Predicting death in a cohort of clinically diverse, multi-condition hospitalized patients is difficult. This frequently hinders timely serious illness care conversations. Prognostic models that can determine 6-month death risk at the time of hospital admission can improve access to serious illness care conversations.
The objective is to determine if the demographic, vital sign, and laboratory data from the first 48 h of a hospitalization can be used to accurately quantify 6-month mortality risk.
This is a retrospective study using electronic medical record data linked with the state death registry.
Participants were 158,323 hospitalized patients within a 6-hospital network over a 6-year period.
Main measures are the following: the first set of vital signs, complete blood count, basic and complete metabolic panel, serum lactate, pro-BNP, troponin-I, INR, aPTT, demographic information, and associated ICD codes. The outcome of interest was death within 6 months.
Model performance was measured on the validation dataset. A random forest model-mini serious illness algorithm-used 8 variables from the initial 48 h of hospitalization and predicted death within 6 months with an AUC of 0.92 (0.91-0.93). Red cell distribution width was the most important prognostic variable. min-SIA (mini serious illness algorithm) was very well calibrated and estimated the probability of death to within 10% of the actual value. The discriminative ability of the min-SIA was significantly better than historical estimates of clinician performance.
min-SIA algorithm can identify patients at high risk of 6-month mortality at the time of hospital admission. It can be used to improved access to timely, serious illness care conversations in high-risk patients.
预测临床情况多样、患有多种疾病的住院患者的死亡情况具有挑战性。这常常阻碍及时进行关于严重疾病护理的沟通。能够在入院时确定6个月死亡风险的预后模型可以改善获得严重疾病护理沟通的机会。
确定住院最初48小时的人口统计学、生命体征和实验室数据是否可用于准确量化6个月的死亡风险。
这是一项使用与州死亡登记处相关联的电子病历数据的回顾性研究。
参与者为6年内6家医院网络中的158323名住院患者。
主要测量指标如下：第一组生命体征、全血细胞计数、基本代谢指标和全套代谢指标、血清乳酸、脑钠肽前体、肌钙蛋白I、国际标准化比值（INR）、活化部分凝血活酶时间（aPTT）、人口统计学信息以及相关的国际疾病分类代码。感兴趣的结局是6个月内死亡。
在验证数据集上评估模型性能。一种随机森林模型——微型严重疾病算法——使用了住院最初48小时内的8个变量，预测6个月内死亡的曲线下面积（AUC）为0.92（0.91 - 0.93）。红细胞分布宽度是最重要的预后变量。微型严重疾病算法校准良好，估计的死亡概率与实际值相差在10%以内。微型严重疾病算法的判别能力明显优于临床医生表现的历史估计值。
微型严重疾病算法可以在入院时识别出6个月死亡风险高的患者。它可用于改善高危患者及时获得严重疾病护理沟通的机会。
