Dynamic modeling of hospitalized COVID-19 patients reveals disease state-dependent risk factors.

OBJECTIVE: The study sought to investigate the disease state-dependent risk profiles of patient demographics and medical comorbidities associated with adverse outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. MATERIALS AND METHODS: A covariate-dependent, continuous-time hidden Markov model with 4 states (moderate, severe, discharged, and deceased) was used to model the dynamic progression of COVID-19 during the course of hospitalization. All model parameters were estimated using the electronic health records of 1362 patients from ProMedica Health System admitted between March 20, 2020 and December 29, 2020 with a positive nasopharyngeal PCR test for SARS-CoV-2. Demographic characteristics, comorbidities, vital signs, and laboratory test results were retrospectively evaluated to infer a patient's clinical progression. RESULTS: The association between patient-level covariates and risk of progression was found to be disease state dependent. Specifically, while being male, being Black or having a medical comorbidity were all associated with an increased risk of progressing from the moderate disease state to the severe disease state, these same factors were associated with a decreased risk of progressing from the severe disease state to the deceased state. DISCUSSION: Recent studies have not included analyses of the temporal progression of COVID-19, making the current study a unique modeling-based approach to understand the dynamics of COVID-19 in hospitalized patients. CONCLUSION: Dynamic risk stratification models have the potential to improve clinical outcomes not only in COVID-19, but also in a myriad of other acute and chronic diseases that, to date, have largely been assessed only by static modeling techniques.

Budget constrained machine learning for early prediction of adverse outcomes for COVID-19 patients.

The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients through improved risk stratification and patient outcome prediction. However, in resource constrained environments the clinical utility of such data-driven predictive tools may be limited by the cost or unavailability of certain laboratory tests. We leveraged EHR data to develop an ML-based tool for predicting adverse outcomes that optimizes clinical utility under a given cost structure. We further gained insights into the decision-making process of the ML models through an explainable AI tool. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Health System in northwest Ohio and southeastern Michigan. We tested the performance of various ML approaches for predicting either increasing ventilatory support or mortality. We performed post hoc analysis to obtain optimal feature sets under various budget constraints. We demonstrate that it is possible to achieve a significant reduction in cost at the expense of a small reduction in predictive performance. For example, when predicting ventilation, it is possible to achieve a 43% reduction in cost with only a 3% reduction in performance. Similarly, when predicting mortality, it is possible to achieve a 50% reduction in cost with only a 1% reduction in performance. This study presents a quick, accurate, and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.

Impact of Comorbidities on SARS-CoV-2 Viral Entry-Related Genes.

Viral entry mechanisms for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are an important aspect of virulence. Proposed mechanisms involve host cell membrane-bound angiotensin-converting enzyme 2 (ACE2), type II transmembrane serine proteases (TTSPs), such as transmembrane serine protease isoform 2 (TMPRSS2), lysosomal endopeptidase Cathepsin L (CTSL), subtilisin-like proprotein peptidase furin (FURIN), and even potentially membrane bound heparan sulfate proteoglycans. The distribution and expression of many of these genes across cell types representing multiple organ systems in healthy individuals has recently been demonstrated. However, comorbidities such as diabetes and cardiovascular disease are highly prevalent in patients with Coronavirus Disease 2019 (COVID-19) and are associated with worse outcomes. Whether these conditions contribute directly to SARS-CoV-2 virulence remains unclear. Here, we show that the expression levels of ACE2, TMPRSS2 and other viral entry-related genes, as well as potential downstream effector genes such as bradykinin receptors, are modulated in the target organs of select disease states. In tissues, such as the heart, which normally express ACE2 but minimal TMPRSS2, we found that TMPRSS2 as well as other TTSPs are elevated in individuals with comorbidities compared to healthy individuals. Additionally, we found the increased expression of viral entry-related genes in the settings of hypertension, cancer, or smoking across target organ systems. Our results demonstrate that common comorbidities may contribute directly to SARS-CoV-2 virulence and we suggest new therapeutic targets to improve outcomes in vulnerable patient populations.