# Temporal Phenotypes and Risk Models for the Post-COVID Syndrome and its sub-types

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $818,071

## Abstract

Project Summary/Abstract
The fight against the SARS-CoV-2, the coronavirus that causes COVID-19, is ramping up with vaccinations and
therapeutics. Yet there is a growing urgency to study and address the other side of the pandemic, a shapeshifting
byproduct known as the post-acute sequelae of COVID-19 (PASC), among other names. Even if several millions
are successfully vaccinated against the virus, many more are still likely to be infected and for hundreds of
thousands (if not millions) of those, recovery from the acute phase of COVID-19 infection will be grueling with a
debilitating second act. A collection of persistent physical (e.g., fatigue, dyspnea, chest pain, cough),
psychological (e.g., anxiety, depression, post-traumatic stress disorder), and neurocognitive symptoms (e.g.,
impaired memory and concentration) can appear and last for weeks or months in patients after acute COVID-
19, impeding their ability to function normally and costing the U.S. economy billions of dollars annually in medical
bills and lost incomes. However, little is known about the post-acute sequelae of COVID-19, the extent and
causes of its lingering health issues, which patients might develop them, and how to address them. We seek to
leverage electronic health records (EHRs) data from 7 hospital systems across the U.S. to develop and validate
a novel framework for studying evolving temporal phenotypes of the post-acute sequelae of COVID-19. For a
period of four years after each patient’s SARS-CoV-2 infection, we will track their clinical data, including clinical
notes and laboratory tests recorded in EHR notes to Curate validated cohorts with gold-standard, rule-based,
and silver-standard (computationally interpolated) labels for PASC phenotypes (Aim 1). We will utilize these
cohorts to develop and validate consistent and interpretable cohort identification and risk models of PASC
phenotypes, accounting for temporal ordering and progression of evolving phenotypes over time (Aim 2). Finally,
we will evaluate the generalizability the PASC models to develop a framework for modeling evolving temporal
phenotypes with EHR data through an objective methodology for evaluating bias in medical AI. (Aim 3). This
study will yield new knowledge regarding the phenotypic characteristics of the post-acute effects following known
SARS-CoV-2 infection and the underlying drivers that influence their presentation and onset. The novel
framework for studying evolving PASC phenotypes will capture and characterize new PASC problems that may
present 2-3 years post-acute infection and update risk models. Given uncertainties around the efficacy of current
vaccines against future mutations, the proposed learning systems will improve our capacity for adaptive
pandemic decision-making. Finally, the framework and underlying methodology developed in this study will lend
insights towards understanding persistent sequelae of other known/suspected viral infections and modeling other
evolving temporal ...

## Key facts

- **NIH application ID:** 10879064
- **Project number:** 5R01AI165535-03
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Hossein Estiri
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $818,071
- **Award type:** 5
- **Project period:** 2022-07-15 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10879064

## Citation

> US National Institutes of Health, RePORTER application 10879064, Temporal Phenotypes and Risk Models for the Post-COVID Syndrome and its sub-types (5R01AI165535-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10879064. Licensed CC0.

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