Prediction of in-hospital mortality among intensive care unit patients using modified daily Laboratory-based Acute Physiology Scores, version 2 (LAPS2).
Prediction of in-hospital mortality among intensive care unit patients using modified daily Laboratory-based Acute Physiology Scores, version 2 (LAPS2).
复制标题
使用修改后的每日实验室急性生理学评分第 2 版 (LAPS2) 预测重症监护病房患者的院内死亡率。
DOI：
10.1101/2023.01.19.23284796
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Kerlin,MeetaPrasad
中科院分区：
文献类型：
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作者：
Kohn,Rachel;Weissman,GaryE;Wang,Wei;Ingraham,NicholasE;Scott,Stefania;Bayes,Brian;Anesi,GeorgeL;Halpern,ScottD;Kipnis,Patricia;Liu,VincentX;Dudley,RAdams;Kerlin,MeetaPrasad
背景：重症监护病房(ICU)患者的死亡率预测常常依赖于单一的ICU入院敏感度测量，而不考虑随后的临床变化。目的：评估结合改进的入院和每日更新的基于实验室的急性生理学评分第二版(LAPS2)的新模型来预测ICU患者的住院死亡率。研究设计：回溯性队列研究。患者：从2017年10月到2019年9月，5家医院的ICU患者。测量：我们使用Logistic回归、惩罚Logistic回归和随机森林模型预测ICU入院后30天内的患者水平和患者日水平的死亡率，或患者日水平的入院和每日LAPS2。多变量模型包括患者特征和入院特征。我们进行了内部-外部验证，使用4家医院进行培训，第五家医院进行验证，对每一家医院重复分析作为验证集。结果：队列包括13,993名患者和107,699天的ICU。在经过验证的医院中，包括每日LAPS2(sbs：0.119−0.235；c统计：0.772−0.878)在内的患者日水平模型在患者水平(sbs：0.109−0.175；c统计：0.768−0.867)和患者日水平(sbs：0.064−0.153；c统计：0.714−0.861)模型中的表现始终优于仅有LAPS2入院的模型。在所有预测的死亡率中，每日模型比仅有LAPS2入院的模型校准得更好。结论：结合每日、时间更新的LAPS2以预测ICU人群死亡率的患者-日水平模型的预测效果与仅包括修改后的LAPS2的模型相同或更好。在这一人群中，每日LAPS2的使用可能为临床预测和研究中的风险调整提供更好的工具。
Background:Mortality prediction for intensive care unit (ICU) patients frequently relies on single ICU admission acuity measures without accounting for subsequent clinical changes.Objective:Evaluate novel models incorporating modified admission and daily, time-updating Laboratory-based Acute Physiology Score, version 2 (LAPS2) to predict in-hospital mortality among ICU patients.Research design:Retrospective cohort study.Patients:ICU patients in 5 hospitals from October 2017 through September 2019.Measures:We used logistic regression, penalized logistic regression, and random forest models to predict in-hospital mortality within 30 days of ICU admission using admission LAPS2 alone in patient-level and patient-day-level models, or admission and daily LAPS2 at the patient-day level. Multivariable models included patient and admission characteristics. We performed internal-external validation using 4 hospitals for training and the fifth for validation, repeating analyses for each hospital as the validation set. We assessed performance using scaled Brier scores (SBS), c-statistics, and calibration plots.Results:The cohort included 13,993 patients and 107,699 ICU days. Across validation hospitals, patient-day-level models including daily LAPS2 (SBS: 0.119− 0.235; c-statistic: 0.772− 0.878) consistently outperformed models with admission LAPS2 alone in patient-level (SBS: 0.109− 0.175; c-statistic: 0.768− 0.867) and patient-day-level (SBS: 0.064− 0.153; c-statistic: 0.714− 0.861) models. Across all predicted mortalities, daily models were better calibrated than models with admission LAPS2 alone.Conclusions:Patient-day-level models incorporating daily, time-updating LAPS2 to predict mortality among an ICU population performs as well or better than models incorporating modified admission LAPS2 alone. The use of daily LAPS2 may offer an improved tool for clinical prognostication and risk adjustment in research in this population.