Developing and Validating Machine Learning‐Driven Risk Indices to Predict Patient Dropout During Referral, Evaluation, and Waitlisting for Kidney Transplant
Fuente:
Wiley Open Access
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Artículo Open Access |
| Published: |
Wiley
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1867011094551199745 |
|---|---|
| author | Solaf Al Awadhi Enshuo Hsu Thomas B. H. Potter Ioannis A. Kakadiaris David A. Axelrod Faith Parsons Andrea M. Meinders Victoria Cassell Catherine Pulicken Zulqarnain Javed Paula K. Shireman Stefano Casarin A. L. Jonathan Gelfond Amy D. Waterman |
| author_facet | Solaf Al Awadhi Enshuo Hsu Thomas B. H. Potter Ioannis A. Kakadiaris David A. Axelrod Faith Parsons Andrea M. Meinders Victoria Cassell Catherine Pulicken Zulqarnain Javed Paula K. Shireman Stefano Casarin A. L. Jonathan Gelfond Amy D. Waterman Solaf Al Awadhi Enshuo Hsu Thomas B. H. Potter Ioannis A. Kakadiaris David A. Axelrod Faith Parsons Andrea M. Meinders Victoria Cassell Catherine Pulicken Zulqarnain Javed Paula K. Shireman Stefano Casarin A. L. Jonathan Gelfond Amy D. Waterman |
| collection | Wiley Open Access |
| contents | Developing and Validating Machine Learning‐Driven Risk Indices to Predict Patient Dropout During Referral, Evaluation, and Waitlisting for Kidney Transplant Solaf Al Awadhi Enshuo Hsu Thomas B. H. Potter Ioannis A. Kakadiaris David A. Axelrod Faith Parsons Andrea M. Meinders Victoria Cassell Catherine Pulicken Zulqarnain Javed Paula K. Shireman Stefano Casarin A. L. Jonathan Gelfond Amy D. Waterman Clinical Transplantation ABSTRACT Background Transplant is the optimal treatment for kidney failure; however, disparities in access persist. We developed and validated risk indices to predict early dropout at key stages of the transplant‐seeking process not captured in national registries. Methods We included patients referred for kidney transplant at Houston Methodist Hospital between June 2016, and November 2023. We collected demographic, clinical, patient‐ and contextual‐level socioeconomic variables from electronic health records and publicly available census data. We used machine learning (ML) models to predict the characteristics of patients at higher risk of dropping out: (1) at referral (before starting evaluation), (2) in the process of evaluation (before waitlisting), and (3) during waitlisting (before receiving a transplant). Model performance was evaluated using AUROC. Results Of 4133 referred patients, 46% did not attend their first transplant evaluation visit. Of 2414 patients who were medically eligible for transplant and started evaluation, 54% did not become waitlisted. Of 2457 waitlisted patients, 31% became inactive on the waitlist. Higher risk patients were consistently older, obese, and socioeconomically disadvantaged, with stage‐specific differences: social factors—such as being single, unemployed, less educated, and living in high‐deprivation areas—and African American race dominated at referral (AUROC 0.79); clinical comorbidities and both African American and Hispanic ethnicity were prominent at evaluation (AUROC 0.71); and Hispanic ethnicity, smoking, and digital exclusion were key drivers at waitlisting (AUROC 0.76). Conclusion ML models effectively identified dropout risk at referral, evaluation, and waitlisting, enabling early identification of at‐risk patients. Targeted interventions could reduce disparities, improve evaluation completion, and increase transplant access. 10.1111/ctr.70325 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1111/ctr.70325 |
| format | Artículo Open Access |
| id | wiley_oa_10_1111_ctr_70325 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Developing and Validating Machine Learning‐Driven Risk Indices to Predict Patient Dropout During Referral, Evaluation, and Waitlisting for Kidney Transplant Solaf Al Awadhi Enshuo Hsu Thomas B. H. Potter Ioannis A. Kakadiaris David A. Axelrod Faith Parsons Andrea M. Meinders Victoria Cassell Catherine Pulicken Zulqarnain Javed Paula K. Shireman Stefano Casarin A. L. Jonathan Gelfond Amy D. Waterman Clinical Transplantation Developing and Validating Machine Learning‐Driven Risk Indices to Predict Patient Dropout During Referral, Evaluation, and Waitlisting for Kidney Transplant Solaf Al Awadhi Enshuo Hsu Thomas B. H. Potter Ioannis A. Kakadiaris David A. Axelrod Faith Parsons Andrea M. Meinders Victoria Cassell Catherine Pulicken Zulqarnain Javed Paula K. Shireman Stefano Casarin A. L. Jonathan Gelfond Amy D. Waterman Clinical Transplantation ABSTRACT Background Transplant is the optimal treatment for kidney failure; however, disparities in access persist. We developed and validated risk indices to predict early dropout at key stages of the transplant‐seeking process not captured in national registries. Methods We included patients referred for kidney transplant at Houston Methodist Hospital between June 2016, and November 2023. We collected demographic, clinical, patient‐ and contextual‐level socioeconomic variables from electronic health records and publicly available census data. We used machine learning (ML) models to predict the characteristics of patients at higher risk of dropping out: (1) at referral (before starting evaluation), (2) in the process of evaluation (before waitlisting), and (3) during waitlisting (before receiving a transplant). Model performance was evaluated using AUROC. Results Of 4133 referred patients, 46% did not attend their first transplant evaluation visit. Of 2414 patients who were medically eligible for transplant and started evaluation, 54% did not become waitlisted. Of 2457 waitlisted patients, 31% became inactive on the waitlist. Higher risk patients were consistently older, obese, and socioeconomically disadvantaged, with stage‐specific differences: social factors—such as being single, unemployed, less educated, and living in high‐deprivation areas—and African American race dominated at referral (AUROC 0.79); clinical comorbidities and both African American and Hispanic ethnicity were prominent at evaluation (AUROC 0.71); and Hispanic ethnicity, smoking, and digital exclusion were key drivers at waitlisting (AUROC 0.76). Conclusion ML models effectively identified dropout risk at referral, evaluation, and waitlisting, enabling early identification of at‐risk patients. Targeted interventions could reduce disparities, improve evaluation completion, and increase transplant access. 10.1111/ctr.70325 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Developing and Validating Machine Learning‐Driven Risk Indices to Predict Patient Dropout During Referral, Evaluation, and Waitlisting for Kidney Transplant |
| topic | Clinical Transplantation |
| url | https://onlinelibrary.wiley.com/doi/10.1111/ctr.70325 |