Department Intensive Care Medicine, Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, Netherlands.
Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands.
Acta Anaesthesiol Scand. 2022 Nov;66(10):1228-1236. doi: 10.1111/aas.14138. Epub 2022 Aug 31.
This study aimed to improve the PREPARE model, an existing linear regression prediction model for long-term quality of life (QoL) of intensive care unit (ICU) survivors by incorporating additional ICU data from patients' electronic health record (EHR) and bedside monitors.
The 1308 adult ICU patients, aged ≥16, admitted between July 2016 and January 2019 were included. Several regression-based machine learning models were fitted on a combination of patient-reported data and expert-selected EHR variables and bedside monitor data to predict change in QoL 1 year after ICU admission. Predictive performance was compared to a five-feature linear regression prediction model using only 24-hour data (R = 0.54, mean square error (MSE) = 0.031, mean absolute error (MAE) = 0.128).
The 67.9% of the included ICU survivors was male and the median age was 65.0 [IQR: 57.0-71.0]. Median length of stay (LOS) was 1 day [IQR 1.0-2.0]. The incorporation of the additional data pertaining to the entire ICU stay did not improve the predictive performance of the original linear regression model. The best performing machine learning model used seven features (R = 0.52, MSE = 0.032, MAE = 0.125). Pre-ICU QoL, the presence of a cerebro vascular accident (CVA) upon admission and the highest temperature measured during the ICU stay were the most important contributors to predictive performance. Pre-ICU QoL's contribution to predictive performance far exceeded that of the other predictors.
Pre-ICU QoL was by far the most important predictor for change in QoL 1 year after ICU admission. The incorporation of the numerous additional features pertaining to the entire ICU stay did not improve predictive performance although the patients' LOS was relatively short.
本研究旨在通过纳入患者电子健康记录（EHR）和床边监测器中的额外 ICU 数据来改进 PREPARE 模型，该模型是一种现有的 ICU 幸存者长期生活质量（QoL）的线性回归预测模型。
纳入了 2016 年 7 月至 2019 年 1 月期间收治的 1308 名年龄≥16 岁的成年 ICU 患者。对患者报告数据和专家选择的 EHR 变量以及床边监测器数据进行了几种基于回归的机器学习模型拟合，以预测 ICU 入院后 1 年的 QoL 变化。预测性能与仅使用 24 小时数据的五特征线性回归预测模型进行了比较（R=0.54，均方误差（MSE）=0.031，平均绝对误差（MAE）=0.128）。
纳入的 ICU 幸存者中 67.9%为男性，中位年龄为 65.0[IQR：57.0-71.0]。中位住院时间（LOS）为 1 天[IQR 1.0-2.0]。纳入整个 ICU 住院期间的额外数据并未提高原始线性回归模型的预测性能。表现最好的机器学习模型使用了七个特征（R=0.52，MSE=0.032，MAE=0.125）。ICU 前 QoL、入院时存在脑卒中和 ICU 期间测量的最高体温是预测性能的最重要贡献者。ICU 前 QoL 对预测性能的贡献远远超过其他预测因子。
ICU 前 QoL 是 ICU 入院后 1 年 QoL 变化的最重要预测因子。尽管患者的 LOS 相对较短，但纳入大量与整个 ICU 住院期间相关的额外特征并未提高预测性能。
