Xie Wanqiu, Franz Henriette, Yakulov Toma Antonov
Renal Division, Department of Medicine, Faculty of Medicine and Medical Center-University of Freiburg, 79106 Freiburg, Germany.
Department of Biomedicine, University of Basel, Pestalozzistr. 20, CH-4056 Basel, Switzerland.
J Clin Med. 2025 Jun 26;14(13):4552. doi: 10.3390/jcm14134552.
Acute kidney injury (AKI) in critically ill patients is often complicated by arrhythmias, potentially affecting outcomes. This study aimed to develop predictive models for arrhythmias in AKI patients and assess the impact of antiarrhythmic drugs on in-hospital mortality. We conducted a multi-database retrospective cohort study using MIMIC-IV and eICU databases. XGBoost and Bayesian Information Criterion (BIC) models were employed to identify key predictors of arrhythmias. Weighted log-rank and Cox analysis evaluated the effect of amiodarone and metoprolol on in-hospital mortality. Among 14,035 critically ill AKI patients, 5614 individuals (40%) developed arrhythmias. Both XGBoost and BIC showed predictive power for arrhythmias. The XGBoost model identified HR_max, HR_min, and heart failure as the most important features, while the BIC model highlighted heart failure had the highest odds ratio (OR 1.18, 95% CI 1.16-1.20) as a significant predictor. Patients experiencing arrhythmia is associated with in-hospital mortality (arrhythmia group: 636 (11.3%) vs. non-arrhythmia group: 587 (7.0%), < 0.01). Antiarrhythmic medications showed a statistically significant effect on in-hospital mortality (amiodarone: HR 0.28, 95% CI 0.19-0.41, < 0.01). Our predictive models demonstrated a robust discriminatory ability for identifying arrhythmia occurrence in critically ill AKI patients, with identified risk factors showing strong clinical relevance. The significant association between arrhythmia occurrence and increased in-hospital mortality underscores the clinical importance of early identification and management. Furthermore, amiodarone therapy effectively reduced the risk of in-hospital mortality in these patients, even after accounting for time-dependent biases. The findings highlight the necessity of precise arrhythmia definition, careful consideration of time-dependent covariates, and comprehensive model validation for clinically actionable insights.
危重症患者的急性肾损伤（AKI）常并发心律失常，这可能会影响治疗结果。本研究旨在建立AKI患者心律失常的预测模型，并评估抗心律失常药物对住院死亡率的影响。我们使用MIMIC-IV和eICU数据库进行了一项多数据库回顾性队列研究。采用XGBoost和贝叶斯信息准则（BIC）模型来识别心律失常的关键预测因素。加权对数秩检验和Cox分析评估了胺碘酮和美托洛尔对住院死亡率的影响。在14035例危重症AKI患者中，5614例（40%）发生了心律失常。XGBoost和BIC模型均显示出对心律失常的预测能力。XGBoost模型确定HR_max、HR_min和心力衰竭是最重要的特征，而BIC模型则突出显示心力衰竭具有最高的优势比（OR 1.18，95%CI 1.16 - 1.20），是一个显著的预测因素。发生心律失常的患者与住院死亡率相关（心律失常组：636例（11.3%） vs. 无心律失常组：587例（7.0%），<0.01）。抗心律失常药物对住院死亡率有统计学显著影响（胺碘酮：HR 0.28，95%CI 0.19 - 0.41，<0.01）。我们的预测模型在识别危重症AKI患者心律失常发生方面显示出强大的鉴别能力，所确定的风险因素具有很强的临床相关性。心律失常发生与住院死亡率增加之间的显著关联凸显了早期识别和管理的临床重要性。此外，即使在考虑了时间依赖性偏倚后，胺碘酮治疗仍有效降低了这些患者的住院死亡风险。研究结果强调了精确心律失常定义、仔细考虑时间依赖性协变量以及进行全面模型验证以获得临床可操作见解的必要性。
