Importance of Transplant Era on Post–Heart Transplant Predictive Models: A UNOS Cohort Analysis
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2024
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| _version_ | 1867013908822228992 |
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| author | Mickaël Lescroart Evan P. Kransdorf Maria Francesca Scuppa Jignesh K. Patel Guillaume Coutance |
| author_facet | Mickaël Lescroart Evan P. Kransdorf Maria Francesca Scuppa Jignesh K. Patel Guillaume Coutance Mickaël Lescroart Evan P. Kransdorf Maria Francesca Scuppa Jignesh K. Patel Guillaume Coutance |
| collection | Wiley Open Access |
| contents | Importance of Transplant Era on Post–Heart Transplant Predictive Models: A UNOS Cohort Analysis Mickaël Lescroart Evan P. Kransdorf Maria Francesca Scuppa Jignesh K. Patel Guillaume Coutance Clinical Transplantation ABSTRACTBackgroundThe application of posttransplant predictive models is limited by their poor statistical performance. Neglecting the dynamic evolution of demographics and medical practice over time may be a key issue.ObjectivesOur objective was to develop and validate era‐specific predictive models to assess whether these models could improve risk stratification compared to non–era‐specific models.MethodsWe analyzed the United Network for Organ Sharing (UNOS) database including first noncombined heart transplantations (2001–2018, divided into four transplant eras: 2001–2005, 2006–2010, 2011–2015, 2016–2018). The endpoint was death or retransplantation during the 1st‐year posttransplant. We analyzed the dynamic evolution of major predictive variables over time and developed era‐specific models using logistic regression. We then performed a multiparametric evaluation of the statistical performance of era‐specific models and compared them to non–era‐specific models in 1000 bootstrap samples (derivation set, 2/3; test set, 1/3).ResultsA total of 34 738 patients were included, 3670 patients (10.5%) met the composite endpoint. We found a significant impact of transplant era on baseline characteristics of donors and recipients, medical practice, and posttransplant predictive models, including significant interaction between transplant year and major predictive variables (total serum bilirubin, recipient age, recipient diabetes, previous cardiac surgery). Although the discrimination of all models remained low, era‐specific models significantly outperformed the statistical performance of non–era‐specific models in most samples, particularly concerning discrimination and calibration.ConclusionsEra‐specific models achieved better statistical performance than non–era‐specific models. A regular update of predictive models may be considered if they were to be applied for clinical decision‐making and allograft allocation. 10.1111/ctr.15403 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1111/ctr.15403 |
| format | Artículo Open Access |
| id | wiley_oa_10_1111_ctr_15403 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Importance of Transplant Era on Post–Heart Transplant Predictive Models: A UNOS Cohort Analysis Mickaël Lescroart Evan P. Kransdorf Maria Francesca Scuppa Jignesh K. Patel Guillaume Coutance Clinical Transplantation Importance of Transplant Era on Post–Heart Transplant Predictive Models: A UNOS Cohort Analysis Mickaël Lescroart Evan P. Kransdorf Maria Francesca Scuppa Jignesh K. Patel Guillaume Coutance Clinical Transplantation ABSTRACTBackgroundThe application of posttransplant predictive models is limited by their poor statistical performance. Neglecting the dynamic evolution of demographics and medical practice over time may be a key issue.ObjectivesOur objective was to develop and validate era‐specific predictive models to assess whether these models could improve risk stratification compared to non–era‐specific models.MethodsWe analyzed the United Network for Organ Sharing (UNOS) database including first noncombined heart transplantations (2001–2018, divided into four transplant eras: 2001–2005, 2006–2010, 2011–2015, 2016–2018). The endpoint was death or retransplantation during the 1st‐year posttransplant. We analyzed the dynamic evolution of major predictive variables over time and developed era‐specific models using logistic regression. We then performed a multiparametric evaluation of the statistical performance of era‐specific models and compared them to non–era‐specific models in 1000 bootstrap samples (derivation set, 2/3; test set, 1/3).ResultsA total of 34 738 patients were included, 3670 patients (10.5%) met the composite endpoint. We found a significant impact of transplant era on baseline characteristics of donors and recipients, medical practice, and posttransplant predictive models, including significant interaction between transplant year and major predictive variables (total serum bilirubin, recipient age, recipient diabetes, previous cardiac surgery). Although the discrimination of all models remained low, era‐specific models significantly outperformed the statistical performance of non–era‐specific models in most samples, particularly concerning discrimination and calibration.ConclusionsEra‐specific models achieved better statistical performance than non–era‐specific models. A regular update of predictive models may be considered if they were to be applied for clinical decision‐making and allograft allocation. 10.1111/ctr.15403 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Importance of Transplant Era on Post–Heart Transplant Predictive Models: A UNOS Cohort Analysis |
| topic | Clinical Transplantation |
| url | https://onlinelibrary.wiley.com/doi/10.1111/ctr.15403 |