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Machine Learning as a Tool to Predict Early and Chronic Graft Dysfunction after Lung Transplantation

Award Number: K25HL186354
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP
PERIOD OF PERFORMANCE START DATE: 06/01/2026
PERIOD OF PERFORMANCE END DATE: 03/31/2031

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Machine Learning as a Tool to Predict Early and Chronic Graft Dysfunction after Lung Transplantation - PROJECT SUMMARY Lung transplantation is the treatment of last resort for a large number of patients with end-stage lung diseases, but is complicated by a modest post-transplant survival. Mortality after transplantation is mostly driven by chronic lung allograft dysfunction (CLAD). An early complication of lung transplantation, primary graft dysfunction (PGD), is a major risk factor for subsequent development of CLAD. Tools to more accurately predict these complications are urgently needed. This proposal hypothesizes that integrating clinical and biological markers from donors and recipients will predict the incidence and severity of PGD and CLAD. This award will support the career development of a mathematician–data scientist toward a critical and underexplored area. The candidate has a strong foundation in mathematical modeling, machine learning, and biomedical data science, and aims to apply these skills to improve outcomes after lung transplantation. Through this K25 award, he will transition to an independent research career centered on translational, data-driven solutions to clinical challenges in transplant medicine. This will be achieved under the following Aims: Aim 1: Identify and validate predictors of PGD incidence and severity. Aim 2: Identify and validate predictors of CLAD onset and subtype. In pursuit of both Aims, we will obtain biospecimens from 3 anatomical sites (donor lung pre-transplant, donor lung 2 hrs post-transplant, and serially blood samples from the recipient) and donor and recipient clinical data from ≥200 patients at two large lung transplant centers. We will perform a multiplex assay measuring 71 cytokine/growth factors to screen samples for markers of cell activation, chemotaxis, injury, angiogenesis and growth factors. We will use computational phenotyping and machine learning to predict outcomes from these comprehensive biological and clinical data. These models will also allow us to identify novel markers for both outcomes that could improve mechanistic understanding and support point-of-care test design. Trained models and candidate markers will be validated using multiplex and ELISA assays of prospectively collected samples from recipients at two major transplant centers over the course of the study. The ability to predict PGD and to forecast CLAD onset are essential for the development of life-, health-, and graft-extending interventions. Our proposed project builds upon important ongoing work while introducing five advances: multi-site specimens, a broad-scope injury/immune response cytokine panel, analysis of conventional storage solution, specialized computational modeling, and multi-center validation. Successful completion of this project will yield externally validated biomarker panels for PGD incidence and severity and CLAD onset and subtype suitable for developing point-of-care tests.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $142,568 )
2026	2026	UNIVERSITY OF FLORIDA	1523 UNION RD RM 207	GAINESVILLE	FL	32611	ALACHUA	USA	Lung Diseases Research	000	1	5/19/2026	NEW	$142,568
														Subtotal = $142,568

Grand Total All Awards = $142,568

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Machine Learning as a Tool to Predict Early and Chronic Graft Dysfunction after Lung Transplantation

Award Number: K25HL186354

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP

PERIOD OF PERFORMANCE START DATE: 06/01/2026

PERIOD OF PERFORMANCE END DATE: 03/31/2031

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