# Developing dynamic prognostic and risk-stratification models for informing prescribing decisions in older adults with Coronavirus Disease 2019

> **NIH NIH RF1** · BRIGHAM AND WOMEN'S HOSPITAL · 2021 · $524,715

## Abstract

Project Summary
While over 80% patients with Coronavirus Disease 2019 (COVID-19) experienced only mild illness, the
mortality rates have been reported to be 6.4-13.4% in vulnerable populations, including older adults and
patients with multiple co-morbidities. Pharmacological treatments are primarily used for patients with moderate
to severe disease. Optimal prescribing of drug therapy relies heavily on accurate risk stratification based on
patient prognosis. Since it is known that COVID-19 can often cause rapid clinical deterioration, it is critical to
have a prognostic tool well-predictive of disease progression and adverse clinical outcomes, so the
pharmacological treatments or other interventions can be initiated timely. Also, during the COVID-19
pandemic, many healthcare facilities need to operate beyond regular capacity with limited resources, such as
mechanical ventilators, therapeutic agents, and intensive care unit (ICU) bed availability. A reliable prognostic
tool is essential for optimal decisions regarding medical disposition (e.g., home monitoring vs. admission) and
resource allocation (eg, ICU beds and mechanical ventilators). While there are seemingly abundant data in
prognostic prediction for patients with COVID-19, there remain two major knowledge gaps. First, all of the
existing prediction models only consider factors measured at hospital admission without incorporating dynamic
changes of biomarkers over time. The models thus have limited clinical applicability since many of these
biomarkers are repeated multiple times during a treatment course and clinicians need to know how these
dynamic changes can inform medical decisions. Second, while medication use and the initiation timing are
highly informative of disease severity, they were not used for prognostic prediction in the prior models. We aim
to build a prospective prognostic modeling system based on near-real-time electronic health record (EHR) data
from Mass General Brigham, a large care delivery network in Massachusetts that includes 2 tertiary and 11
secondary hospitals and >30 ambulatory centers. We have established the basic infrastructure and currently
receive weekly data updates. The database currently has >14,000 confirmed cases of COVID-19 and are
expanding at the rate of 500-1000 confirmed cases per week, allowing us to build prediction models with rich
data input and ability to perform prospective validation. We will develop a dynamic prognostic tool incorporating
baseline characteristics, time-varying factors with their dynamic changes, medication use and its timing to
predict key clinical outcomes. Data accrued from March to August, 2020 will be used for model derivation and
data from September to December, 2020 will be used for prospective validation. In addition to the predictors
reported in the literature, we will search for novel predictors by screening through the rich EHR data using
TreeScan, a novel, validated, statistical tool adopted by the US Food and D...

## Key facts

- **NIH application ID:** 10189838
- **Project number:** 3RF1AG063381-01S1
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** JOSHUA K LIN
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $524,715
- **Award type:** 3
- **Project period:** 2019-05-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10189838

## Citation

> US National Institutes of Health, RePORTER application 10189838, Developing dynamic prognostic and risk-stratification models for informing prescribing decisions in older adults with Coronavirus Disease 2019 (3RF1AG063381-01S1). Retrieved via AI Analytics 2026-06-03 from https://api.ai-analytics.org/grant/nih/10189838. Licensed CC0.

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