Prediction of In-hospital Mortality Among Intensive Care Unit Patients Using Modified Daily Laboratory-based Acute Physiology Score, Version 2.
Prediction of In-hospital Mortality Among Intensive Care Unit Patients Using Modified Daily Laboratory-based Acute Physiology Score, Version 2.
复制标题
使用改良的基于实验室的每日急性生理学评分（第 2 版）预测重症监护病房患者的院内死亡率。
DOI：
10.1097/mlr.0000000000001878
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发表时间：
2023
期刊：
影响因子：
3
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,RaymondAdams;Kerlin,MeetaPrasad
背景：重症监护室（ICU）患者的死亡率预测通常依赖于单一ICU入院急性指标，而不考虑随后的临床变化。目的：评估新模型，包括改良入院和每日时间更新的基于急性生理学评分的第2版（LAPS 2），以预测ICU患者的院内死亡率。研究设计：回顾性队列研究。患者：2017年10月至2019年9月5家医院的ICU患者。指标：我们使用logistic回归、惩罚logistic回归和随机森林模型，在患者水平和患者日水平模型中单独使用入院LAPS 2预测ICU入院30天内的住院死亡率，或入院和患者日水平的每日LAPS 2。多变量模型包括患者和入院特征。我们使用4家医院进行培训，第五家医院进行验证，对每家医院进行重复分析作为验证集。我们使用标度Brier评分（SBS）、c-统计量和校准plots.Results来评估性能：该队列包括13，993例患者和107，699个ICU日。在验证医院中，患者日水平模型包括每日LAPS 2（SBS：0.119 - 0.235; c-统计量：0.772 - 0.878）在患者水平上始终优于仅使用入院LAPS 2的模型（SBS：0.109 - 0.175; c-统计量：0.768 - 0.867）和患者日水平（SBS：0.064 - 0.153; c-统计量：0.714 - 0.861）模型。在所有预测的死亡率，每日模型更好地校准比模型与入院LAPS 2 alone.Conclusions：患者日水平的模型纳入每日，时间更新LAPS 2预测死亡率之间的ICU人口执行以及或优于模型合并修改后的入院LAPS 2单独。在该人群的研究中，每日使用LAPS 2可能为临床诊断和风险调整提供一种改进的工具。
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.