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Using machine learning to estimate survival curves for patients receiving an increased risk for disease transmission heart, liver, or lung versus waiting for a standard organ.
Transpl Infect Dis 2019; :e13181TI

Abstract

INTRODUCTION

Over 19% of deceased organ donors are labeled increased risk for disease transmission (IRD) for viral blood-borne disease transmission. Many potential organ recipients need to decide between accepting an IRD organ offer and waiting for a non-IRD organ.

METHODS

Using machine learning and simulation, we built transplant and waitlist survival models and compared the survival for patients accepting IRD organ offers or waiting for non-IRD organs for the heart, liver, and lung. The simulation consisted of generating 20 000 different scenarios of a recipient either receiving an IRD organ or waiting and receiving a non-IRD organ.

RESULTS

In the simulations, the 5-year survival probabilities of heart, liver, and lung recipients who accepted IRD organ offers increased on average by 10.2%, 12.7%, and 7.2%, respectively, compared with receiving a non-IRD organ after average wait times (190, 228, and 223 days, respectively). When the estimated waitlist time was at least 5 days for the liver, and 1 day for the heart and lung, 50% or more of the simulations resulted in a higher chance of 5-year survival when the patient received an IRD organ versus when the patient remained on the waitlist. We also developed a simple equation to estimate the benefits, in terms of 5-year survival probabilities, of receiving an IRD organ versus waiting for a non-IRD organ, for a particular set of recipient/donor characteristics.

CONCLUSION

For all three organs, the majority of patients are predicted to have higher 5-year survival accepting an IRD organ offer compared with waiting for a non-IRD organ.

Authors+Show Affiliations

H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA.H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA.H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA.H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31541522

Citation

Mark, Ethan, et al. "Using Machine Learning to Estimate Survival Curves for Patients Receiving an Increased Risk for Disease Transmission Heart, Liver, or Lung Versus Waiting for a Standard Organ." Transplant Infectious Disease : an Official Journal of the Transplantation Society, 2019, pp. e13181.
Mark E, Goldsman D, Keskinocak P, et al. Using machine learning to estimate survival curves for patients receiving an increased risk for disease transmission heart, liver, or lung versus waiting for a standard organ. Transpl Infect Dis. 2019.
Mark, E., Goldsman, D., Keskinocak, P., & Sokol, J. (2019). Using machine learning to estimate survival curves for patients receiving an increased risk for disease transmission heart, liver, or lung versus waiting for a standard organ. Transplant Infectious Disease : an Official Journal of the Transplantation Society, pp. e13181. doi:10.1111/tid.13181.
Mark E, et al. Using Machine Learning to Estimate Survival Curves for Patients Receiving an Increased Risk for Disease Transmission Heart, Liver, or Lung Versus Waiting for a Standard Organ. Transpl Infect Dis. 2019 Sep 21;e13181. PubMed PMID: 31541522.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Using machine learning to estimate survival curves for patients receiving an increased risk for disease transmission heart, liver, or lung versus waiting for a standard organ. AU - Mark,Ethan, AU - Goldsman,David, AU - Keskinocak,Pinar, AU - Sokol,Joel, Y1 - 2019/09/21/ PY - 2019/03/07/received PY - 2019/08/13/revised PY - 2019/09/15/accepted PY - 2019/9/22/pubmed PY - 2019/9/22/medline PY - 2019/9/22/entrez KW - Cox proportional hazards model KW - HBV transmission KW - HCV transmission KW - HIV transmission KW - heart transplantation KW - increased risk for disease transmission donor KW - liver transplantation KW - lung transplantation KW - survival analysis SP - e13181 EP - e13181 JF - Transplant infectious disease : an official journal of the Transplantation Society JO - Transpl Infect Dis N2 - INTRODUCTION: Over 19% of deceased organ donors are labeled increased risk for disease transmission (IRD) for viral blood-borne disease transmission. Many potential organ recipients need to decide between accepting an IRD organ offer and waiting for a non-IRD organ. METHODS: Using machine learning and simulation, we built transplant and waitlist survival models and compared the survival for patients accepting IRD organ offers or waiting for non-IRD organs for the heart, liver, and lung. The simulation consisted of generating 20 000 different scenarios of a recipient either receiving an IRD organ or waiting and receiving a non-IRD organ. RESULTS: In the simulations, the 5-year survival probabilities of heart, liver, and lung recipients who accepted IRD organ offers increased on average by 10.2%, 12.7%, and 7.2%, respectively, compared with receiving a non-IRD organ after average wait times (190, 228, and 223 days, respectively). When the estimated waitlist time was at least 5 days for the liver, and 1 day for the heart and lung, 50% or more of the simulations resulted in a higher chance of 5-year survival when the patient received an IRD organ versus when the patient remained on the waitlist. We also developed a simple equation to estimate the benefits, in terms of 5-year survival probabilities, of receiving an IRD organ versus waiting for a non-IRD organ, for a particular set of recipient/donor characteristics. CONCLUSION: For all three organs, the majority of patients are predicted to have higher 5-year survival accepting an IRD organ offer compared with waiting for a non-IRD organ. SN - 1399-3062 UR - https://www.unboundmedicine.com/medline/citation/31541522/Using_machine_learning_to_estimate_survival_curves_for_patients_receiving_an_increased_risk_for_disease_transmission_heart,_liver,_or_lung_versus_waiting_for_a_standard_organ L2 - https://doi.org/10.1111/tid.13181 DB - PRIME DP - Unbound Medicine ER -