Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying.

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.

Decrease in Exacerbations During the Coronavirus Disease 2019 Pandemic in a Cohort of Veterans with COPD.

BACKGROUND: Studies have shown a decline in hospitalizations due to acute exacerbations of COPD (AECOPD) during the coronavirus disease 2019 (COVID-19) pandemic. However, the impact of the pandemic in AECOPD of all severities in longitudinal cohorts of patients is lacking. METHODS: We conducted analysis of 123 individuals with COPD who have been followed since 2017. AECOPDs of mild (treatment at home), moderate (emergency department or urgent visit evaluation), and severe (hospitalization) type were assessed by chart review and patient interview. Compliance with preventive measures to avoid COVID-19 infection was assessed in 2020. Differences between the rate of AECOPD by year was analyzed as well as differences in preventive measures by COPD disease severity. RESULTS: During the COVID-19 pandemic in 2020, there was a significant reduction in AECOPDs in our cohort with 26 participants (21%) having an exacerbation compared to 46 (37%) in 2019, 52 (42%) in 2018, and 44 (36%) in 2017. Mean exacerbation rates decreased 54% overall and 74% in frequent exacerbators compared with the prior 3-year average. The decrease was noted in AECOPDs of all severities. Overall, there was a high rate of reported compliance with social distancing and face mask use that was significantly higher in the group with more severe COPD based on symptoms and forced expiratory volume in 1 second. CONCLUSIONS: Individuals with COPD, including frequent exacerbators, showed a marked decrease in AECOPD during the COVID-19 pandemic and high adherence to recommended preventive measures. Evaluation of the impact of preventive strategies on AECOPD in a non-pandemic setting may be of value and requires further study.