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Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of
Mohammad M Banoei1,2, Roshan Dinparastisaleh3, Ali Vaeli Zadeh4
1Department of Critical Care Medicine, University of Calgary, Alberta, Canada.
Insights
This study developed a machine learning model to predict COVID-19 mortality in hospitalized patients. The model identifies key clinical and biochemical predictors, aiding in risk stratification and patient management.
Area of Science:
- Medical Informatics
- Machine Learning in Healthcare
- Epidemiology
Background:
- The COVID-19 pandemic presents significant challenges due to high mortality and varied clinical presentations.
- Accurate prediction of mortality and identification of outcome predictors are critical for managing critically ill COVID-19 patients.
Purpose of the Study:
- To develop and validate a machine learning model for predicting hospital mortality in COVID-19 patients.
- To identify key clinical, comorbidity, and biochemical predictors of mortality in COVID-19.
Main Methods:
- A multivariate predictive analysis using 108 features from 250 hospitalized COVID-19 patients.
- Development of a SIMPLS-based model for mortality prediction, with data split into training and validation sets.
- Latent class analysis (LCA) to cluster patients into high- and low-risk groups.
Main Results:
- The SIMPLS model achieved moderate predictive power (Q²=0.24) and high accuracy (AUC>0.85) in predicting mortality.
- Key predictors included coronary artery disease, diabetes, altered mental status, age > 65, dementia, CRP, prothrombin, and lactate.
- Clustering analysis successfully identified distinct high- and low-risk patient groups among survivors.
Conclusions:
- A machine learning-based mortality prediction model can aid in clinical decision-making for COVID-19 patients.
- Identifying critical predictors and risk groups enhances patient management strategies.
- This approach offers valuable insights for handling future infectious disease outbreaks.
Background:
The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate. Predicting mortality and identifying outcome predictors are crucial for COVID patients who are critically ill. Multivariate and machine learning methods may be used for developing prediction models and reduce the complexity of clinical phenotypes.
Methods:
Multivariate predictive analysis was applied to 108 out of 250 clinical features, comorbidities, and blood markers captured at the admission time from a hospitalized cohort of patients (N = 250) with COVID-19. Inspired modification of partial least square (SIMPLS)-based model was developed to predict hospital mortality. Prediction accuracy was randomly assigned to training and validation sets. Predictive partition analysis was performed to obtain cutting value for either continuous or categorical variables. Latent class analysis (LCA) was carried to cluster the patients with COVID-19 to identify low- and high-risk patients. Principal component analysis and LCA were used to find a subgroup of survivors that tends to die.
Results:
SIMPLS-based model was able to predict hospital mortality in patients with COVID-19 with moderate predictive power (Q2 = 0.24) and high accuracy (AUC > 0.85) through separating non-survivors from survivors developed using training and validation sets. This model was obtained by the 18 clinical and comorbidities predictors and 3 blood biochemical markers. Coronary artery disease, diabetes, Altered Mental Status, age > 65, and dementia were the topmost differentiating mortality predictors. CRP, prothrombin, and lactate were the most differentiating biochemical markers in the mortality prediction model. Clustering analysis identified high- and low-risk patients among COVID-19 survivors.
Conclusions:
An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors.
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