{"doi":"10.1111/bjh.17342","title":"A phenotypic risk score for predicting mortality in sickle cell disease","abstract":"Risk assessment for patients with sickle cell disease (SCD) remains challenging as it depends on an individual physician's experience and ability to integrate a variety of test results. We aimed to provide a new risk score that combines clinical, laboratory, and imaging data. In a prospective cohort of 600 adult patients with SCD, we assessed the relationship of 70 baseline covariates to all-cause mortality. Random survival forest and regularised Cox regression machine learning (ML) methods were used to select top predictors. Multivariable models and a risk score were developed and internally validated. Over a median follow-up of 4·3 years, 131 deaths were recorded. Multivariable models were developed using nine independent predictors of mortality: tricuspid regurgitant velocity, estimated right atrial pressure, mitral E velocity, left ventricular septal thickness, body mass index, blood urea nitrogen, alkaline phosphatase, heart rate and age. Our prognostic risk score had superior performance with a bias-corrected C-statistic of 0·763. Our model stratified patients into four groups with significantly different 4-year mortality rates (3%, 11%, 35% and 75% respectively). Using readily available variables from patients with SCD, we applied ML techniques to develop and validate a mortality risk scoring method that reflects the summation of cardiopulmonary, renal and liver end-organ damage. Trial Registration: ClinicalTrials.gov Identifier: NCT#00011648.","journal":"British Journal of Haematology","year":2021,"id":171388,"datarank":1.003478507994911,"base_score":3.295836866004329,"endowment":3.295836866004329,"self_citation_contribution":0.4943755299006494,"citation_network_contribution":0.5091029780942614,"self_endowment_contribution":0.4943755299006494,"citer_contribution":0.5091029780942614,"corpus_percentile":null,"corpus_rank":null,"citation_count":26,"citer_count":18,"citers_with_citation_signal":12,"citers_with_endowment":12,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9493,"is_data_producer":true,"deposit_databanks":{"ClinicalTrials.gov":["NCT00011648"]},"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":262681,"name":"Xin Tian","orcid":"0000-0003-1896-2462","position":1,"is_corresponding":false},{"id":706080,"name":"Yuan Gu","orcid":"0000-0001-6222-7241","position":2,"is_corresponding":false},{"id":706081,"name":"James Nichols","orcid":"0000-0002-1789-0818","position":3,"is_corresponding":false},{"id":283312,"name":"Stanislav Sidenko","orcid":null,"position":4,"is_corresponding":false},{"id":307520,"name":"Wen Li","orcid":"0000-0002-8805-2934","position":5,"is_corresponding":false},{"id":706872,"name":"Andrea Beri","orcid":null,"position":6,"is_corresponding":false},{"id":706873,"name":"W. Austin Layne","orcid":null,"position":7,"is_corresponding":false},{"id":706874,"name":"Darlene Allen","orcid":null,"position":8,"is_corresponding":false},{"id":85436,"name":"Colin O. Wu","orcid":"0000-0002-4514-0926","position":9,"is_corresponding":false},{"id":247166,"name":"Swee Lay Thein","orcid":"0000-0002-9835-6501","position":10,"is_corresponding":false},{"id":280938,"name":"Vandana Sachdev","orcid":"0000-0002-1168-5055","position":0,"is_corresponding":true}],"reference_count":34,"raw_metadata":null,"created_at":"2026-07-18T23:46:33.067299Z","pmid":"33506990","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":[]}