Cox Eline G M, Meijs Daniek A M, Wynants Laure, Sels Jan-Willem E M, Koeze Jacqueline, Keus Frederik, Bos-van Dongen Bianca, van der Horst Iwan C C, van Bussel Bas C T
Department of Critical Care, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands; Department of Intensive Care Medicine, Maastricht University Medical Center + (Maastricht UMC+), Maastricht, The Netherlands.
Department of Intensive Care Medicine, Maastricht University Medical Center + (Maastricht UMC+), Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands.
J Clin Epidemiol. 2025 Feb;178:111605. doi: 10.1016/j.jclinepi.2024.111605. Epub 2024 Nov 13.
Mortality prediction models are promising tools for guiding clinical decision-making and resource allocation in intensive care units (ICUs). Clearly specified predictor and outcome variables are necessary to enable external validation and safe clinical application of prediction models. The objective of this study was to identify the predictor and outcome variables used in different mortality prediction models in the ICU and investigate their reporting.
For this scoping review, MEDLINE, EMBASE, Web of Science, and the Cochrane Central Register of Controlled Trials were searched. Studies developed within a general ICU population reporting on prediction models with mortality as a primary or secondary outcome were eligible. The selection criteria were adopted from a review by Keuning et al. Predictor and outcome variables, variable characteristics (defined as units, definitions, moments of measurement, and methods of measurement), and publication details (defined as first author, year of publication and title) were extracted from the included studies. Predictor and outcome variable categories were demographics, chronic disease, care logistics, acute diagnosis, clinical examination and physiological derangement, laboratory assessment, additional diagnostics, support and therapy, risk scores, and (mortality) outcomes.
A total of 56 mortality prediction models, containing 204 unique predictor and outcome variables, were included. The predictor variables most frequently included in the models were age (40 times), admission type (27 times), and mechanical ventilation (21 times). We observed that single variables were measured with different units, according to different definitions, at a different moment, and with a different method of measurement in different studies. The reporting of the unit was mostly complete (98% overall, 95% in the laboratory assessment category), whereas the definition of the variable (74% overall, 63% in the chronic disease category) and method of measurement (70% overall, 34% in the demographics category) were most often lacking.
Accurate and transparent reporting of predictor and outcome variables is paramount to enhance reproducibility, model performance in different contexts, and validity. Since unclarity about the required input data may introduce bias and thereby affect model performance, this study advocates that prognostic ICU models can be improved by transparent and clear reporting of predictor and outcome variables and their characteristics.
死亡率预测模型是指导重症监护病房（ICU）临床决策和资源分配的有前景的工具。明确指定预测变量和结果变量对于预测模型的外部验证和安全临床应用至关重要。本研究的目的是确定ICU中不同死亡率预测模型所使用的预测变量和结果变量，并调查其报告情况。
对于这项范围综述，检索了MEDLINE、EMBASE、科学网和Cochrane对照试验中央注册库。在一般ICU人群中开发的、报告以死亡率作为主要或次要结果的预测模型的研究符合要求。选择标准采用了Keuning等人综述中的标准。从纳入的研究中提取预测变量和结果变量、变量特征（定义为单位、定义、测量时间和测量方法）以及发表细节（定义为第一作者、发表年份和标题）。预测变量和结果变量类别包括人口统计学、慢性病、护理后勤、急性诊断、临床检查和生理紊乱、实验室评估、额外诊断、支持与治疗、风险评分以及（死亡率）结果。
共纳入56个死亡率预测模型，包含204个独特的预测变量和结果变量。模型中最常包含的预测变量是年龄（40次）、入院类型（27次）和机械通气（21次）。我们观察到，在不同研究中，单个变量根据不同定义、在不同时间、使用不同测量方法进行测量时，其单位也不同。单位的报告大多完整（总体为98%，实验室评估类别中为95%），而变量的定义（总体为74%，慢性病类别中为63%）和测量方法（总体为70%，人口统计学类别中为34%）最常缺失。
准确且透明地报告预测变量和结果变量对于提高可重复性、模型在不同情境下的性能以及有效性至关重要。由于所需输入数据不明确可能会引入偏差，从而影响模型性能，本研究主张通过透明且清晰地报告预测变量和结果变量及其特征来改进ICU预后模型。
