External validation and comparison of risk score models in pediatric heart transplants

Pediatric heart transplant (PHT) patients have the highest waitlist mortality of solid organ transplants, yet more than 40% of viable hearts are unutilized. A tool for risk prediction could impact these outcomes. This study aimed to compare and validate the PHT risk score models (RSMs) in the literature.

[1]  S. Deshpande,et al.  Interplay between donor and recipient factors impacts outcomes after pediatric heart transplantation: An analysis from the united network for organ sharing database , 2020, Pediatric transplantation.

[2]  A. Dipchand,et al.  Review of interactions between high‐risk pediatric heart transplant recipients and marginal donors including utilization of risk score models , 2020, Pediatric transplantation.

[3]  Anna Goldenberg,et al.  Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review , 2020, Hepatology.

[4]  A. Israni,et al.  OPTN/SRTR 2018 Annual Data Report: Heart , 2020, American Journal of Transplantation.

[5]  D. Morales,et al.  Pondering Higher Risk Pediatric Heart Donors: Can We Use More? , 2019, The Annals of thoracic surgery.

[6]  W. Dreyer,et al.  A Recipient Risk Prediction Tool for Short Term Mortality after Pediatric Heart Transplantation. , 2019, Transplantation.

[7]  B. Meiser,et al.  The International Thoracic Organ Transplant Registry of the International Society for Heart and Lung Transplantation: Twenty-second pediatric heart transplantation report - 2019; Focus theme: Donor and recipient size match. , 2019, The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation.

[8]  C. Fraser,et al.  Children’s Heart Assessment Tool for Transplantation (CHAT) Score: A Novel Risk Score Predicts Survival After Pediatric Heart Transplantation , 2019, World journal for pediatric & congenital heart surgery.

[9]  D. Goldsman,et al.  Using machine learning and an ensemble of methods to predict kidney transplant survival , 2019, PloS one.

[10]  J. Tweddell,et al.  The Number of Refusals for Donor Organ Quality Does Not Impact Heart Transplant Outcomes in Children. , 2017, The Annals of thoracic surgery.

[11]  J. Tweddell,et al.  Implications and outcomes of cardiac grafts refused by pediatric centers but transplanted by adult centers , 2017, The Journal of thoracic and cardiovascular surgery.

[12]  Benjamin I. P. Rubinstein,et al.  Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation , 2017, Transplantation.

[13]  J. Tweddell,et al.  Pediatric Heart Donor Assessment Tool (PH-DAT): A novel donor risk scoring system to predict 1-year mortality in pediatric heart transplantation. , 2017, The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation.

[14]  J. Schold,et al.  Measuring Transplant Center Performance: the Goals Are Not Controversial but the Methods and Consequences Can Be , 2017, Current Transplantation Reports.

[15]  A. Israni,et al.  OPTN/SRTR 2015 Annual Data Report: Heart , 2017, American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons.

[16]  Z. Obermeyer,et al.  Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. , 2016, The New England journal of medicine.

[17]  M. Desai,et al.  Development and validation of a major adverse transplant event (MATE) score to predict late graft loss in pediatric heart transplantation. , 2016, The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation.

[18]  A. Israni,et al.  Developing Statistical Models to Assess Transplant Outcomes Using National Registries: The Process in the United States. , 2016, Transplantation.

[19]  S. Russell,et al.  MELD-XI Score Predicts Early Mortality in Patients After Heart Transplantation. , 2015, The Annals of thoracic surgery.

[20]  A. Atz,et al.  Validation of a Simple Score to Determine Risk of Early Rejection After Pediatric Heart Transplantation. , 2015, JACC. Heart failure.

[21]  C. Canter,et al.  Predicting graft loss by 1 year in pediatric heart transplantation candidates: an analysis of the Pediatric Heart Transplant Study database. , 2015, Circulation.

[22]  J. Jefferies,et al.  Pediatric heart transplant waiting list mortality in the era of ventricular assist devices. , 2014, The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation.

[23]  K. Gauvreau,et al.  A Risk‐Prediction Model for In‐Hospital Mortality After Heart Transplantation in US Children , 2012, American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons.

[24]  W. Morrow,et al.  Improved Survival While Waiting and Risk Factors for Death in Pediatric Patients Listed for Cardiac Transplantation , 2016, Pediatric Cardiology.

[25]  L. Lund,et al.  The Registry of the International Society for Heart and Lung Transplantation: Nineteenth Pediatric Lung and Heart-Lung Transplantation Report-2016; Focus Theme: Primary Diagnostic Indications for Transplant. , 2016, The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation.

[26]  Cecka Jm.  Calculated PRA (CPRA): the new measure of sensitization for transplant candidates. , 2010 .

[27]  A. Moskowitz,et al.  Predicting survival among high-risk pediatric cardiac transplant recipients: an analysis of the United Network for Organ Sharing database. , 2008, The Journal of thoracic and cardiovascular surgery.