背景:将多维患者数据用于亚型肝肾综合征(HRS)可以个性化患者护理。机器学习(ML)共识聚类可以识别具有独特临床特征的HRS亚群。在这项研究中,我们的目标是使用无监督ML聚类方法,确定有临床意义的HRS住院患者聚类.
方法:根据2003-2014年全国住院患者样本中5564例主要因HRS入院的患者特征,进行共识聚类分析,以确定临床上不同的HRS亚组。我们应用标准化的平均差来评估关键子组特征,并比较了指定集群之间的住院死亡率。
结果:该算法根据患者特征揭示了四个最佳的HRS亚组。第1组患者(n=1617)年龄较大,更有可能患有非酒精性脂肪性肝病,心血管合并症,高血压,和糖尿病。第2组患者(n=1577)更年轻,更可能患有丙型肝炎,并且不太可能有急性肝功能衰竭。第3组患者(n=642)年轻,更有可能非选修录取,对乙酰氨基酚过量,急性肝功能衰竭,发展院内医疗并发症和器官系统衰竭,需要辅助治疗,包括肾脏替代疗法,机械通气。第4组患者(n=1728)年轻,更有可能患酒精性肝硬化和吸烟。33%的患者在医院死亡。第1组(OR1.53;95%CI1.31-1.79)和第3组(OR7.03;95%CI5.73-8.62)的住院死亡率较高,与第2组相比,第4组的院内死亡率相当(OR1.13;95%CI0.97~1.32).
结论:共识聚类分析提供了具有不同结局的临床特征和临床不同HRS表型的模式。
BACKGROUND: The utilization of multi-dimensional patient data to subtype hepatorenal syndrome (HRS) can individualize patient care. Machine learning (ML)
consensus clustering may identify HRS subgroups with unique clinical profiles. In this study, we aim to identify clinically meaningful clusters of hospitalized patients for HRS using an unsupervised ML clustering approach.
METHODS: Consensus clustering analysis was performed based on patient characteristics in 5564 patients primarily admitted for HRS in the National Inpatient Sample from 2003-2014 to identify clinically distinct HRS subgroups. We applied standardized mean difference to evaluate key subgroup features, and compared in-hospital mortality between assigned clusters.
RESULTS: The algorithm revealed four best distinct HRS subgroups based on patient characteristics. Cluster 1 patients (n = 1617) were older, and more likely to have non-alcoholic fatty liver disease, cardiovascular comorbidities, hypertension, and diabetes. Cluster 2 patients (n = 1577) were younger and more likely to have hepatitis C, and less likely to have acute liver failure. Cluster 3 patients (n = 642) were younger, and more likely to have non-elective admission, acetaminophen overdose, acute liver failure, to develop in-hospital medical complications and organ system failure, and to require supporting therapies, including renal replacement therapy, and mechanical ventilation. Cluster 4 patients (n = 1728) were younger, and more likely to have alcoholic cirrhosis and to smoke. Thirty-three percent of patients died in hospital. In-hospital mortality was higher in cluster 1 (OR 1.53; 95% CI 1.31-1.79) and cluster 3 (OR 7.03; 95% CI 5.73-8.62), compared to cluster 2, while cluster 4 had comparable in-hospital mortality (OR 1.13; 95% CI 0.97-1.32).
CONCLUSIONS: Consensus clustering analysis provides the pattern of clinical characteristics and clinically distinct HRS phenotypes with different outcomes.