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Phenotype clustering of hospitalized high-risk patients with COVID-19 - a machine learning approach within the
Mateusz Sokolski1, Sander Trenson2, Konrad Reszka3
1Wroclaw Medical University, Faculty of Medicine, Institute of Heart Diseases, Wroclaw, Poland and Intitute of Heart Diseases, University Hospital, Wroclaw, Poland. matsok@gmail.com.
Insights
Machine learning identified three patient clusters in COVID-19 patients with cardiovascular disease. Men with severe heart disease, risk factors, and inflammation faced the highest mortality risk.
Area of Science:
- Cardiology
- Infectious Diseases
- Machine Learning
Background:
- COVID-19 patients with cardiovascular disease (CV) or risk factors (RF) have varied prognoses.
- Predicting outcomes in this heterogeneous group requires advanced analytical methods.
Purpose of the Study:
- To phenotype hospitalized COVID-19 patients with CV disease/RF using unsupervised machine learning (ML).
- To identify distinct patient clusters within the PCHF-COVICAV registry for outcome prediction.
Main Methods:
- Unsupervised ML (K-medoids algorithm) applied to 458 patients from the PCHF-COVICAV registry.
- Analysis incorporated 46 baseline variables including demographics, clinical status, comorbidities, and biochemical data.
Main Results:
- Three distinct patient clusters were identified.
- Cluster 1: Predominantly women with fewer comorbidities/RF.
- Cluster 2: Predominantly men with non-CV conditions and milder symptoms.
- Cluster 3: Predominantly men with severe CV disease, high RF, inflammation, organ dysfunction, and highest 6-month mortality.
Conclusions:
- ML successfully delineated three clinical clusters among COVID-19 patients with CV disease/RF.
- A specific cluster of males with severe CV disease, heart failure, multiple RF, and inflammation exhibited a significantly poorer prognosis.
Imtroduction:
The high-risk population of patients with cardiovascular (CV) disease or risk factors (RF) suffering from COVID-19 is heterogeneous. Several predictors for impaired prognosis have been identified. However, with machine learning (ML) approaches, certain phenotypes may be confined to classify the affected population and to predict outcome. This study aimed to phenotype patients using unsupervised ML technique within the International Postgraduate Course Heart Failure Registry for patients hospitalized with COVID-19 and Cardiovascular disease and/or RF (PCHF-COVICAV).
Material And Methods:
Patients from the eight centres with follow-up data available from the PCHF-COVICAV registry were included in this ML analysis (K-medoids algorithm).
Results:
Out of 617 patients included into the prospective part of the registry, 458 [median age: 76 (IQR:65-84) years, 55% male] were analyzed and 46 baseline variables, including demographics, clinical status, comorbidities and biochemical characteristics were incorporated into the ML. Three clusters were extracted by this ML method. Cluster 1 (n = 181) represents mainly women with the least number of overall comorbidities and cardiovascular RF. Cluster 2 (n = 227) is characterized mainly by men with non-CV conditions and less severe symptoms of infection. Cluster 3 (n=50) mainly represents men with the highest prevalence of cardiac comorbidities and RF, more extensive inflammation and organ dysfunction with the highest 6-month all-cause mortality risk.
Conclusions:
The ML process has identified three important clinical clusters from hospitalized COVID-19 CV and/or RF patients. The cluster of males with severe CV disease, particularly HF, and multiple RF presenting with increased inflammation had a particularly poor outcome.
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