# Computational methods using electronic health records and registry data to detect and predict clinical outcomes in rheumatic disease

> **NIH NIH K01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $130,424

## Abstract

PROJECT SUMMARY / ABSTRACT
This is a new application for a K01 award for Dr. Milena Gianfrancesco, an epidemiologist at the University of
California, San Francisco (UCSF) School of Medicine, who plans a research program focusing on
understanding risk factors as they relate to rheumatic disease patient outcomes, such as adverse events.
Combined with a training plan focused on computational text mining methods and advanced causal inference
statistics, the goal of the current study is to use large electronic health record and national registry data that
reflects real-world prescribing patterns to examine the risk of infection attributed to biologic disease-modifying
anti-rheumatic drugs in individuals with rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE).
While biologic medications have improved disease control and are associated with significant gains in patients’
quality of life, several studies have demonstrated that biologic use is associated with an increased risk of
serious adverse events, such as infection. How this risk differs based on a variety of patient factors, such as
age, race, and ethnicity, is currently unknown, leaving clinicians with insufficient information to predict the
probability of an adverse event occurring in a given patient who is prescribed a particular biologic.
This proposal will utilize established local electronic health record and national registry data to examine over
80,000 individuals with RA and SLE to address three specific aims. In Aim 1, Dr. Gianfrancesco will apply and
validate a text mining system to identify incident clinical and opportunistic infections from clinical notes. In Aim
2, Dr. Gianfrancesco will use the same databases to determine the longitudinal causal effect of biologics on
risk of infection. In Aim 3, a risk-assessment model to predict risk of infection will be developed and validated in
a rheumatology clinic. Findings from this study will further elucidate factors associated with infectious risk for
individuals prescribed biologics, thereby improving their safety in the ambulatory settings.
Dr. Gianfrancesco has assembled an exceptional mentorship team with expertise in computational text mining
methods, advanced causal inference statistics, rheumatology and patient safety outcomes, as well as
experience using national registry data to address these questions. She will have access to a rich research
environment and provided support for career development through programs such as the UCSF Clinical and
Translational Science Institute K-scholars program. Formal coursework and mentoring will also be
supplemented with attendance at national conferences related to rheumatology, epidemiology, and informatics.
Completing the proposed research and career development plan will allow Dr. Gianfrancesco to gain
experience in state-of-the-art computational methods using large datasets to better understand important
patient outcomes, such as serious adverse events. This mentored career de...

## Key facts

- **NIH application ID:** 10086403
- **Project number:** 5K01AR075085-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Milena Anne Gianfrancesco
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $130,424
- **Award type:** 5
- **Project period:** 2019-04-10 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10086403

## Citation

> US National Institutes of Health, RePORTER application 10086403, Computational methods using electronic health records and registry data to detect and predict clinical outcomes in rheumatic disease (5K01AR075085-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10086403. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*