# An Automated Frailty Scoring System for Lung Transplantation Based on Bio-Geo-Composition

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $449,041

## Abstract

Abstract:
 Lung transplantation is a life-saving treatment for individuals suffering from end-stage lung diseases. The
number of lung transplants is increasing annually, and over 50% of worldwide lung transplants are performed in
the United States. However, the long-term survival of lung transplant patients lags behind other solid organ
transplants. To improve the lung transplant outcome and optimize the allocation of donor lungs, it is essential to
identify factors that are associated with transplant outcomes. We propose to systematically validate a new
concept called "Bio-Geo-Composition" as a potential biomarker for assessing lung transplant candidates. Our
goal is to develop an automated frailty and fitness scoring system to objectively assess a candidate's fitness for
a lung transplant, which we call the “Pittsburgh Transplant Fitness Score.” The PTFS will be designed to
accurately predict the intra- and post-operative outcomes primarily based on recipients' pre-transplant chest
computed tomography (CT) scans. The Bio-Geo-Composition concept assesses an individual's biological and
geometric attributes through three components: body tissues, lung characteristics, and thoracic geometry. We
will use advanced automated algorithms to comprehensively quantify Bio-Geo-Composition features depicted
on pre-transplant CT images and analyze their association with transplant outcomes during and after the surgery.
The significant factors will be integrated as a computer model with other patient characteristics to produce the
PTFS. The model will optimize to predict intraoperative complications (e.g., delayed chest closure), postoperative
complications (e.g., primary graft dysfunction, postoperative mechanical support, and ICU stay), and survival.
Awareness of the potential factors contributing to unfitness will allow for pre-transplant care to be tailored to
address these issues with the aim of improving fitness and maximizing the benefit of lung transplants.

## Key facts

- **NIH application ID:** 10939691
- **Project number:** 1R01HL174570-01
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Chadi Antonios Hage
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $449,041
- **Award type:** 1
- **Project period:** 2024-08-15 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10939691

## Citation

> US National Institutes of Health, RePORTER application 10939691, An Automated Frailty Scoring System for Lung Transplantation Based on Bio-Geo-Composition (1R01HL174570-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10939691. Licensed CC0.

---

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