# Forecasting Lung Transplant Benefit: A Dynamic Risk Modeling Approach

> **NIH NIH R01** · CLEVELAND CLINIC LERNER COM-CWRU · 2021 · $749,734

## Abstract

Project Summary
Increasing clinical demand for lung transplants has exacerbated the problem of rationing this limited yet life-
saving societal resource. The Lung Allocation Score (LAS) system was developed to improve overall survival
by identifying patients who would likely benefit the most from transplant. Despite this effort, there have been
increasing rates of waiting list mortality, declines in long-term survival after transplant and dramatic increases
in healthcare costs and utilization among transplant patients.
Our project focuses on improving the LAS system by: 1) designing better methodologies to more accurately
identify the progression of illness in a patient who is awaiting transplant, 2) predicting ideal timing of transplant
to maximize the number of years gained from a transplant, and 3) evaluating different allocation strategies and
their impact on individual and population level survival. We will achieve this by carrying out the following aims:
Aim 1: Update the lung allocation score (LAS) underlying risk models to better accommodate
 subpopulation-level differences over time among lung transplant candidates.
Aim 2: Develop and validate a forecasting model for lung transplant candidates’ dynamic health state and
 likelihood of transplantation over time using a systems-based microsimulation modeling approach.
Aim 3: Evaluate the impact of lung allocation strategies that optimize patient- and population-level functional
 and survival outcomes.
The results of this work will provide the foundation for improving lung allocation in the United States. We will
optimize timing of lung transplantation to maximize transplant benefit at the individual patient and population
levels. The methods identified in this project can be utilized in other scenarios where limited life saving
resources must be rationed.

## Key facts

- **NIH application ID:** 10171622
- **Project number:** 5R01HL153175-02
- **Recipient organization:** CLEVELAND CLINIC LERNER COM-CWRU
- **Principal Investigator:** JARROD DALTON
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $749,734
- **Award type:** 5
- **Project period:** 2020-06-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10171622

## Citation

> US National Institutes of Health, RePORTER application 10171622, Forecasting Lung Transplant Benefit: A Dynamic Risk Modeling Approach (5R01HL153175-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10171622. Licensed CC0.

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