Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
Institute of Kidney Disease, Inflammation and Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
Ren Fail. 2024 Dec;46(1):2315298. doi: 10.1080/0886022X.2024.2315298. Epub 2024 Feb 15.
The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD).
After employing least absolute shrinkage and selection operator regression for feature selection, six distinct methodologies were employed in the construction of the model. The selection of the optimal model was based on the area under the curve (AUC). Furthermore, the interpretation of the chosen model was facilitated through the utilization of SHapley Additive exPlanation (SHAP) values and the Local Interpretable Model-Agnostic Explanations (LIME) algorithm.
This study collected data and enrolled 5041 patients on CHF combined with CKD from 2008 to 2019, utilizing the Medical Information Mart for Intensive Care Unit. After selection, 22 of the 47 variables collected post-intensive care unit admission were identified as mortality-associated and subsequently utilized in the development of ML models. Among the six models generated, the eXtreme Gradient Boosting (XGBoost) model demonstrated the highest AUC at 0.837. Notably, the SHAP values highlighted the sequential organ failure assessment score, age, simplified acute physiology score II, and urine output as the four most influential variables in the XGBoost model. In addition, the LIME algorithm explains the individualized predictions.
In conclusion, our study accomplished the successful development and validation of ML models for predicting in-hospital mortality in critically ill patients with CHF combined with CKD. Notably, the XGBoost model emerged as the most efficacious among all the ML models employed.
本研究旨在开发和验证一种机器学习（ML）模型，用于预测充血性心力衰竭（CHF）合并慢性肾脏病（CKD）的危重症患者的住院死亡率。
在使用最小绝对收缩和选择算子回归进行特征选择后，使用了六种不同的方法来构建模型。通过曲线下面积（AUC）选择最优模型。此外，通过使用 Shapley 加法解释（SHAP）值和局部可解释模型不可知解释（LIME）算法来解释所选模型。
本研究利用医疗信息监护病房采集了 2008 年至 2019 年 5041 例 CHF 合并 CKD 患者的数据，在选择后，从入院后 47 个变量中确定了 22 个与死亡率相关的变量，并将其用于 ML 模型的开发。在生成的六个模型中，极端梯度提升（XGBoost）模型的 AUC 最高，为 0.837。值得注意的是，SHAP 值突出了序贯器官衰竭评估评分、年龄、简化急性生理学评分 II 和尿量作为 XGBoost 模型中四个最具影响力的变量。此外，LIME 算法解释了个体预测。
总之，我们的研究成功地开发和验证了用于预测 CHF 合并 CKD 的危重症患者住院死亡率的 ML 模型。值得注意的是，XGBoost 模型是所有使用的 ML 模型中最有效的模型。
