South China Normal University-Panyu Central Hospital Joint Laboratory of Basic and Translational Medical Research, Guangzhou Panyu Central Hospital, Guangzhou, 511400, Guangdong, China.
School of Life Sciences, South China Normal University, Guangzhou, 510631, Guangdong, China.
J Transl Med. 2022 Mar 18;20(1):136. doi: 10.1186/s12967-022-03340-8.
Predicting hospital mortality risk is essential for the care of heart failure patients, especially for those in intensive care units.
Using a novel machine learning algorithm, we constructed a risk stratification tool that correlated patients' clinical features and in-hospital mortality. We used the extreme gradient boosting algorithm to generate a model predicting the mortality risk of heart failure patients in the intensive care unit in the derivation dataset of 5676 patients from the Medical Information Mart for Intensive Care III database. The logistic regression model and a common risk score for mortality were used for comparison. The eICU Collaborative Research Database dataset was used for external validation.
The performance of the machine learning model was superior to that of conventional risk predictive methods, with the area under curve 0.831 (95% CI 0.820-0.843) and acceptable calibration. In external validation, the model had an area under the curve of 0.809 (95% CI 0.805-0.814). Risk stratification through the model was specific when the hospital mortality was very low, low, moderate, high, and very high (2.0%, 10.2%, 11.5%, 21.2% and 56.2%, respectively). The decision curve analysis verified that the machine learning model is the best clinically valuable in predicting mortality risk.
Using readily available clinical data in the intensive care unit, we built a machine learning-based mortality risk tool with prediction accuracy superior to that of linear regression model and common risk scores. The risk tool may support clinicians in assessing individual patients and making individualized treatment.
预测医院死亡率风险对于心力衰竭患者的护理至关重要，尤其是对于那些在重症监护病房中的患者。
我们使用一种新颖的机器学习算法，构建了一种风险分层工具，该工具与患者的临床特征和住院死亡率相关。我们使用极端梯度提升算法，从 Medical Information Mart for Intensive Care III 数据库中 5676 例患者的推导数据集生成了一个预测重症监护病房心力衰竭患者死亡率风险的模型。该模型与逻辑回归模型和常用的死亡率风险评分进行了比较。我们使用 eICU 协作研究数据库数据集进行外部验证。
机器学习模型的性能优于传统风险预测方法，曲线下面积为 0.831（95%CI 0.820-0.843），校准效果可接受。在外部验证中，该模型的曲线下面积为 0.809（95%CI 0.805-0.814）。当医院死亡率非常低、低、中、高和非常高时（分别为 2.0%、10.2%、11.5%、21.2%和 56.2%），该模型的风险分层具有特异性。决策曲线分析验证了机器学习模型在预测死亡率风险方面具有最佳的临床价值。
我们使用重症监护病房中易于获得的临床数据，构建了一种基于机器学习的死亡率风险工具，其预测准确性优于线性回归模型和常用风险评分。该风险工具可能有助于临床医生评估个体患者并制定个体化治疗方案。
