A retrospective prognostic evaluation using unsupervised learning in the treatment of COVID-19 patients with hypertension treated with ACEI/ARB drugs

PeerJ. 2024 May 13:12:e17340. doi: 10.7717/peerj.17340. eCollection 2024.

Abstract

Introduction: This study aimed to evaluate the prognosis of patients with COVID-19 and hypertension who were treated with angiotensin-converting enzyme inhibitor (ACEI)/angiotensin receptor B (ARB) drugs and to identify key features affecting patient prognosis using an unsupervised learning method.

Methods: A large-scale clinical dataset, including patient information, medical history, and laboratory test results, was collected. Two hundred patients with COVID-19 and hypertension were included. After cluster analysis, patients were divided into good and poor prognosis groups. The unsupervised learning method was used to evaluate clinical characteristics and prognosis, and patients were divided into different prognosis groups. The improved wild dog optimization algorithm (IDOA) was used for feature selection and cluster analysis, followed by the IDOA-k-means algorithm. The impact of ACEI/ARB drugs on patient prognosis and key characteristics affecting patient prognosis were also analysed.

Results: Key features related to prognosis included baseline information and laboratory test results, while clinical symptoms and imaging results had low predictive power. The top six important features were age, hypertension grade, MuLBSTA, ACEI/ARB, NT-proBNP, and high-sensitivity troponin I. These features were consistent with the results of the unsupervised prediction model. A visualization system was developed based on these key features.

Conclusion: Using unsupervised learning and the improved k-means algorithm, this study accurately analysed the prognosis of patients with COVID-19 and hypertension. The use of ACEI/ARB drugs was found to be a protective factor for poor clinical prognosis. Unsupervised learning methods can be used to differentiate patient populations and assess treatment effects. This study identified important features affecting patient prognosis and developed a visualization system with clinical significance for prognosis assessment and treatment decision-making.

Keywords: ACEI; ARB; COVID-19; Hypertension; Prognostic evaluation; Unsupervised learning.

MeSH terms

  • Aged
  • Algorithms
  • Angiotensin Receptor Antagonists* / therapeutic use
  • Angiotensin-Converting Enzyme Inhibitors* / therapeutic use
  • COVID-19 Drug Treatment
  • COVID-19*
  • Cluster Analysis
  • Female
  • Humans
  • Hypertension* / drug therapy
  • Male
  • Middle Aged
  • Prognosis
  • Retrospective Studies
  • SARS-CoV-2*
  • Unsupervised Machine Learning*

Grants and funding

This work was supported by the Shanghai Key Specialty Project of Clinical Pharmacy (No.YXZDZK-01), the Nature Science Foundation of Jiading District, the Shanghai (No.JDKW-2021-0043) and the Shanghai University of Medicine and Health Sciences Clinical Research Centre for Metabolic Vascular Diseases Project (No.20MC2020004). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.