Joshua Morriss

Graduate Trainee, Virginia Commonwealth University

2 active projects

Predicting Major Adverse Cardiac Events in Heart Failure Patients with COVID-19

Aim 1: Determine the predictors of mortality and hospitalization for patients with acute or chronic heart failure (A/CHF) that had a diagnosis of COVID-19. Rapid onset of new or worsening heart failure symptoms are characteristic of AHF. Concomitance of COVID-19…

Scientific Questions Being Studied

Aim 1: Determine the predictors of mortality and hospitalization for patients with acute or chronic heart failure (A/CHF) that had a diagnosis of COVID-19. Rapid onset of new or worsening heart failure symptoms are characteristic of AHF. Concomitance of COVID-19 presents additional challenges towards treating A/CHF patients. Studies provide several candidate clinical and laboratory measures associated with worse clinical outcomes for patients with A/CHF and COVID-19. Identifying COVID-19 specific predictors of mortality and hospitalization for A/CHF patients would help explain the pathophysiology behind the progression of COVID-19 in A/CHF patients.

Aim 2: Stratify the risk for suboptimal guideline-directed medical therapy (GDMT) for A/CHF patients with COVID-19. COVID-19 obstructs A/CHF patients from reaching their optimal target doses. Assigning patients into different strata at risk of not achieving optimal GDMT targets may provide clinicians with more impactful treatment options.

Project Purpose(s)

  • Disease Focused Research (severe acute respiratory syndrome, acute on chronic heart failure)

Scientific Approaches

This retrospective study will include demographic characteristics and clinical features from the All of Us A/CHF and COVID-19 combined cohorts. Missing values will be imputed by multiple imputation. Dimensionality of the data will be reduced by supervised selection. Associations between demographic and clinical features will be made with the outcome of 1-year re-hospitalization with A/CHF as the primary diagnosis. Models generated will utilize standard regression, random forests, and gradient boosting, and will be evaluated by their predictive values, sensitivity, specificity, and c-statistics.

Combined clinical features at baseline will undergo k-means cluster analysis to subset groups. Features will undergo processing as described above. A predictive model will be developed, and a Cox proportional hazards regression analysis for re-hospitalization will be performed for each subgroup. All analyses are to be conducted on the All of Us workbench in the latest version of R and Python.

Anticipated Findings

We may expect to find clinical features and laboratory parameters associated with elevated systemic inflammation, endothelial dysfunction, and hypercoagulation to be strong predictors of adverse outcomes for A/CHF patients who has contracted COVID-19. Clinical features like carbon dioxide and oxygen partial pressures in arterial blood may serve as correlates of worse outcomes. Predictive laboratory features may include high-sensitivity C-reactive protein (hs-CRP), brain and atrial natriuretic peptides (BNP/ANP), ferritin, interleukins, neutrophils, complete blood count and d-dimer quantities among others.

In stratifying patients at-risk of not adhering to GDMT, stratification we expect that data pertaining to a patient’s health care access and utilization, as well as the severity of their COVID-19 infection, may put them at greater risk of non-adherence. Severity of COVID-19 infection may be understood as a profile of high inflammation like elevated levels of hs-CRP or interleukins.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Collaborators:

  • Saud Alshammari - Graduate Trainee, Virginia Commonwealth University
  • Silas Contaifer - Graduate Trainee, Virginia Commonwealth University
  • Daniel Contaifer Junior - Project Personnel, Virginia Commonwealth University
  • VIRGINIA UNIVERSITY - Graduate Trainee, Virginia Commonwealth University

Duplicate of Phenotype - Ischemic Heart Disease

The Notebooks in this workspace can be used to implement well-known phenotype algorithms in one’s own research.

Scientific Questions Being Studied

The Notebooks in this workspace can be used to implement well-known phenotype algorithms in one’s own research.

Project Purpose(s)

  • Educational
  • Methods Development
  • Other Purpose (This is an All of Us Phenotype Library Workspace created by the Researcher Workbench Support team. It is meant to demonstrate the implementation of key phenotype algorithms within the All of Us Research Program cohort.)

Scientific Approaches

Not Applicable

Anticipated Findings

By reading and running the Notebooks in this Phenotype Library Workspace, researchers can implement the following phenotype algorithms:

Christianne L. Roumie; Jana Shirey-Rice, Sunil Kripalani. Vanderbilt University. MidSouth CDRN - Coronary Heart Disease Algorithm. PheKB; 2014. Available from https://phekb.org/phenotype/234

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

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