{"doi":"10.1111/petr.14525","title":"Pediatric risk to orthotopic heart transplant (PRO) score: Insights from United Network for Organ Sharing (UNOS) waitlist mortality findings","abstract":"BACKGROUND: Pediatric heart transplant candidates on the waitlist have the highest mortality rate among all solid organ transplants. A risk score incorporating a candidate's individual risk factors may better predict mortality on the waitlist and optimize organ allocation to the sickest of those awaiting transplant. METHODS: Using the United Network for Organ Sharing (UNOS) database, we evaluated a total of 5542 patients aged 0-18 years old on the waitlist for a single, first time, heart transplant from January 2010 to June 2019. We performed a univariate analysis on two-thirds (N = 3705) of these patients to derive the factors most associated with waitlist mortality or delisting secondary to deterioration within 1 year. Those with a p <0.2 underwent a multivariate analysis and the resulting factors were used to build a prediction model using the Fine-Grey model analysis. This predictive scoring model was then validated on the remaining one-third of the patients (N = 1852). RESULTS: The Pediatric Risk to OHT (PRO) scoring model utilizes the following unique patient variables: blood type, diagnosis of congenital heart disease, weight, presence of ventilator support, presence of inotropic support, extracorporeal membrane oxygenation (ecmo) status, creatinine level, and region. A higher score indicates an increased risk of mortality. The PRO score had a predictive strength of 0.762 as measured by area under the ROC curve at 1 year. CONCLUSION: The PRO score is an improved predictive model with the potential to better assess mortality for patients awaiting heart transplant.","journal":"Pediatric Transplantation","year":2023,"id":373092,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9022,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1133980,"name":"Holly Wilhalme","orcid":"0000-0002-8434-6548","position":1,"is_corresponding":false},{"id":1133981,"name":"Anila Chaudhary","orcid":"0000-0002-8203-7821","position":2,"is_corresponding":false},{"id":659314,"name":"Krystal Karunungan","orcid":null,"position":3,"is_corresponding":false},{"id":767068,"name":"Juan Alejos","orcid":"0000-0002-7653-6114","position":4,"is_corresponding":false},{"id":760946,"name":"Neeraj Srivastava","orcid":"0000-0002-5417-2786","position":5,"is_corresponding":false},{"id":1133979,"name":"Stephanie A. Raymundo","orcid":"0000-0003-1275-905X","position":0,"is_corresponding":true}],"reference_count":13,"raw_metadata":null,"created_at":"2026-07-19T01:16:02.217046Z","pmid":"37439081","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}