入院时基于机器学习共识聚类识别的急性肾损伤临床亚型。

Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering.

机构信息

Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.

Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA.

出版信息

Med Sci (Basel). 2021 Sep 24;9(4):60. doi: 10.3390/medsci9040060.

DOI:10.3390/medsci9040060

PMID:34698185

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8544570/

Abstract

BACKGROUND

We aimed to cluster patients with acute kidney injury at hospital admission into clinically distinct subtypes using an unsupervised machine learning approach and assess the mortality risk among the distinct clusters.

METHODS

We performed consensus clustering analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 4289 hospitalized adult patients with acute kidney injury at admission. The standardized difference of each variable was calculated to identify each cluster's key features. We assessed the association of each acute kidney injury cluster with hospital and one-year mortality.

RESULTS

Consensus clustering analysis identified four distinct clusters. There were 1201 (28%) patients in cluster 1, 1396 (33%) patients in cluster 2, 1191 (28%) patients in cluster 3, and 501 (12%) patients in cluster 4. Cluster 1 patients were the youngest and had the least comorbidities. Cluster 2 and cluster 3 patients were older and had lower baseline kidney function. Cluster 2 patients had lower serum bicarbonate, strong ion difference, and hemoglobin, but higher serum chloride, whereas cluster 3 patients had lower serum chloride but higher serum bicarbonate and strong ion difference. Cluster 4 patients were younger and more likely to be admitted for genitourinary disease and infectious disease but less likely to be admitted for cardiovascular disease. Cluster 4 patients also had more severe acute kidney injury, lower serum sodium, serum chloride, and serum bicarbonate, but higher serum potassium and anion gap. Cluster 2, 3, and 4 patients had significantly higher hospital and one-year mortality than cluster 1 patients ( < 0.001).

CONCLUSION

Our study demonstrated using machine learning consensus clustering analysis to characterize a heterogeneous cohort of patients with acute kidney injury on hospital admission into four clinically distinct clusters with different associated mortality risks.

摘要

背景

本研究旨在采用无监督机器学习方法，根据患者入院时的人口统计学信息、主要诊断、合并症和实验室数据，将急性肾损伤患者聚类为临床特征明显的不同亚型，并评估不同亚组的死亡率。

方法

我们对 4289 例成年急性肾损伤住院患者的上述信息进行共识聚类分析。计算每个变量的标准化差值，以确定每个聚类的关键特征。评估急性肾损伤各聚类与住院和 1 年死亡率的关系。

结果

共识聚类分析确定了 4 个不同的聚类。其中，聚类 1 有 1201 例（28%）患者，聚类 2 有 1396 例（33%）患者，聚类 3 有 1191 例（28%）患者，聚类 4 有 501 例（12%）患者。聚类 1 的患者最年轻，合并症最少。聚类 2 和聚类 3 的患者年龄较大，基线肾功能较低。聚类 2 患者的血清碳酸氢盐、强离子差和血红蛋白较低，而血清氯较高，聚类 3 患者的血清氯较低，但血清碳酸氢盐和强离子差较高。聚类 4 的患者较年轻，更可能因泌尿生殖系统疾病和感染性疾病入院，但较少因心血管疾病入院。聚类 4 的患者还存在更严重的急性肾损伤，血清钠、氯和碳酸氢盐较低，而血清钾和阴离子间隙较高。聚类 2、3 和 4 的患者住院和 1 年死亡率明显高于聚类 1 的患者（<0.001）。