Feng Lu-Huai, Su Tingting, Huang Lina, Liao Tianbao, Lu Yang, Wu Lili
Department of Endocrinology and Metabolism Nephrology, Guangxi Medical University Cancer Hospital, Nanning, China.
Department of ECG Diagnostics, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Front Med (Lausanne). 2025 Feb 24;12:1544024. doi: 10.3389/fmed.2025.1544024. eCollection 2025.
Developing and validating a simple and clinically useful dynamic nomogram for predicting early acute kidney injury (AKI) in patients with acute heart failure (AHF) admitted to the intensive care unit (ICU).
Clinical data from patients with AHF were obtained from the Medical Information Mart for Intensive Care IV database. The patients with AHF were randomly allocated into derivation and validation sets. The independent predictors for AKI development in AHF patients were identified through least absolute shrinkage and selection operator and multivariate logistic regression analyses. A nomogram was developed based on the results of the multivariable logistic regression to predict early AKI onset in AHF patients, which was subsequently implemented as a web-based calculator for clinical application. An evaluation of the nomogram was conducted using discrimination, calibration curves, and decision curve analyses (DCA).
After strict screening, 1,338 patients with AHF were included in the derivation set, and 3,129 in the validation set. Sepsis, use of human albumin, age, mechanical ventilation, aminoglycoside administration, and serum creatinine levels were identified as predictive factors for AKI in patients with AHF. The discrimination of the nomogram in both the derivation and validation sets was 0.81 (95% confidence interval: 0.78-0.83) and 0.79 (95% confidence interval: 0.76-0.83). Additionally, the calibration curve demonstrated that the predicted outcomes aligned well with the actual observations. Ultimately, the DCA curves indicated that the nomogram exhibited favorable clinical applicability.
The nomogram that integrates clinical risk factors and enables the personalized prediction of AKI in patients with AHF upon admission to the ICU, which has the potential to assist in identifying AHF patients who would derive the greatest benefit from interventions aimed at preventing and treating AKI.
开发并验证一种简单且具有临床实用性的动态列线图，用于预测入住重症监护病房（ICU）的急性心力衰竭（AHF）患者早期急性肾损伤（AKI）。
从重症监护医学信息集市IV数据库中获取AHF患者的临床数据。将AHF患者随机分为推导集和验证集。通过最小绝对收缩和选择算子以及多变量逻辑回归分析确定AHF患者发生AKI的独立预测因素。基于多变量逻辑回归结果开发列线图，以预测AHF患者早期AKI的发生，随后将其作为基于网络的计算器用于临床应用。使用区分度、校准曲线和决策曲线分析（DCA）对列线图进行评估。
经过严格筛选，推导集中纳入1338例AHF患者，验证集中纳入3129例。脓毒症、人血白蛋白的使用、年龄、机械通气、氨基糖苷类药物的使用以及血清肌酐水平被确定为AHF患者发生AKI的预测因素。推导集和验证集中列线图的区分度分别为0.81（95%置信区间：0.78 - 0.83）和0.79（95%置信区间：0.76 - 0.83）。此外，校准曲线表明预测结果与实际观察结果吻合良好。最终，DCA曲线表明列线图具有良好的临床适用性。
该列线图整合了临床危险因素，能够对入住ICU的AHF患者AKI进行个性化预测，有可能帮助识别从预防和治疗AKI的干预措施中获益最大的AHF患者。
