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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Using predictive analytic modeling, the Veterans Affairs (VA) Geriatrics and Extended Care Data Analysis Center (GECDAC) identified vulnerable “High-Need High-Risk” (HNHR) Veterans, as requiring more support and services. We sought to identify variables linked with utilization of our outpatient HNHR C4 clinic offering Comprehensive Geriatric Assessment, Care Planning, Care Coordination, and Co-management". Of 724 HNHR Veterans contacted, 531 were reached and invited to participate; 193 were not reached, 326 were reached but declined the C4 clinic, 205 attended the clinic. We compared these groups. Independent variables were organized using Anderson’s behavioral model into predisposing (age, gender, race, ethnicity), enabling (drive time, service eligibility, Area Deprivation Index, marital status), and need factors (mental health cognitive condition, ambulatory care sensitive conditions, NOSOS, JFI, CAN, etc.). C4 enrollment acceptance was the outcome. Results showed that compared to patients who declined, HNHR veterans who attended C4 clinic had more chronic health conditions(p<0.01), more service eligibility(p=0.01), more driving time to the closest VA clinic(p=0.01), and more were married (p=0.01). Patients who declined C4 clinic might have greater barriers to care access. Accessing needed healthcare among HNHR older adults maybe impacted more by enabling factors that allow the individual to seek care if needed and are the resources that may facilitate access to services, rather than need factors, which include individuals' perceptions of their health and functional state, and healthcare needs assessed by professionals. More social and intermediary determinants of health should be incorporated as enabling factors into models striving to understand drivers of healthcare use.