Hong Caogen, Sun Zhoujian, Hao Yuzhe, Dong Zhanghuiya, Gu Zhaodan, Huang Zhengxing
College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.
Jiangsu Automation Research Institute, Lianyungang, China.
JMIR Med Inform. 2022 Oct 14;10(10):e37484. doi: 10.2196/37484.
Studies have shown that more than half of patients with heart failure (HF) with acute kidney injury (AKI) have newonset AKI, and renal function evaluation markers such as estimated glomerular filtration rate are usually not repeatedly tested during the hospitalization. As an independent risk factor, delayed AKI recognition has been shown to be associated with the adverse events of patients with HF, such as chronic kidney disease and death.
The aim of this study is to develop and assess of an unsupervised machine learning model that identifies patients with HF and normal renal function but who are susceptible to de novo AKI.
We analyzed an electronic health record data set that included 5075 patients admitted for HF with normal renal function, from which 2 phenogroups were categorized using an unsupervised machine learning algorithm called K-means clustering. We then determined whether the inferred phenogroup index had the potential to be an essential risk indicator by conducting survival analysis, AKI prediction, and the hazard ratio test.
The AKI incidence rate in the generated phenogroup 2 was significantly higher than that in phenogroup 1 (group 1: 106/2823, 3.75%; group 2: 259/2252, 11.50%; P<.001). The survival rate of phenogroup 2 was consistently lower than that of phenogroup 1 (P<.005). According to logistic regression, the univariate model using the phenogroup index achieved promising performance in AKI prediction (sensitivity 0.710). The generated phenogroup index was also significant in serving as a risk indicator for AKI (hazard ratio 3.20, 95% CI 2.55-4.01). Consistent results were yielded by applying the proposed model on an external validation data set extracted from Medical Information Mart for Intensive Care (MIMIC) III pertaining to 1006 patients with HF and normal renal function.
According to a machine learning analysis on electronic health record data, patients with HF who had normal renal function were clustered into separate phenogroups associated with different risk levels of de novo AKI. Our investigation suggests that using machine learning can facilitate patient phengrouping and stratification in clinical settings where the identification of high-risk patients has been challenging.
研究表明，超过半数的急性肾损伤（AKI）合并心力衰竭（HF）患者发生的是新发AKI，而估算肾小球滤过率等肾功能评估指标在住院期间通常不会反复检测。作为一个独立的危险因素，AKI的延迟识别已被证明与HF患者的不良事件相关，如慢性肾脏病和死亡。
本研究旨在开发并评估一种无监督机器学习模型，该模型能够识别肾功能正常但易发生新发AKI的HF患者。
我们分析了一个电子健康记录数据集，其中包括5075例肾功能正常的HF住院患者，使用一种名为K均值聚类的无监督机器学习算法将其分为2个表型组。然后，我们通过生存分析、AKI预测和风险比检验来确定推断出的表型组指数是否有可能成为一个重要的风险指标。
生成的表型组2中的AKI发病率显著高于表型组1（组1：106/2823，3.75%；组2：259/2252，11.50%；P<0.001）。表型组2的生存率始终低于表型组1（P<0.005）。根据逻辑回归分析，使用表型组指数的单变量模型在AKI预测方面表现良好（灵敏度为0.710）。生成的表型组指数作为AKI的风险指标也具有显著性（风险比为3.20，95%可信区间为2.55-4.01）。将所提出的模型应用于从重症监护医学信息数据库（MIMIC）III中提取的1006例肾功能正常的HF患者的外部验证数据集，也得到了一致的结果。
根据对电子健康记录数据的机器学习分析，肾功能正常的HF患者被聚类为与不同新发AKI风险水平相关的不同表型组。我们的研究表明，在识别高危患者具有挑战性的临床环境中，使用机器学习可以促进患者的表型分组和分层。
