{"doi":"10.1371/journal.pone.0240039","title":"Predicting the 10-year risk of death from other causes in men with localized prostate cancer using patient-reported factors: Development of a tool","abstract":"OBJECTIVE: To develop a tool for estimating the 10-year risk of death from other causes in men with localized prostate cancer. SUBJECTS AND METHODS: We identified 2,425 patients from the Surveillance Epidemiology and End Results-Medicare Health Outcomes Survey database, age <80, newly diagnosed with clinical stage T1-T3a prostate cancer from 1/1/1998-12/31/2009, with follow-up through 2/28/2013. We developed a Fine and Gray competing-risks model for 10-year other cause mortality considering age, patient-reported comorbid medical conditions, component scores and items of the SF-36 Health Survey, activities of daily living, and sociodemographic characteristics. Model discrimination and calibration were compared to predictions from Social Security life table mortality risk estimates. RESULTS: Over a median follow-up of 7.7 years, 76 men died of prostate-specific causes and 465 died of other causes. The strongest predictors of 10-year other cause mortality risk included increasing age at diagnosis, higher approximated Charlson Comorbidity Index score, worse patient-reported general health (fair or poor vs. excellent-good), smoking at diagnosis, and marital status (all other vs. married) (all p<0.05). Model discrimination improved over Social Security life tables (c-index of 0.70 vs. 0.59, respectively). Predictions were more accurate than predictions from the Social Security life tables, which overestimated risk in our population. CONCLUSIONS: We provide a tool for estimating the 10-year risk of dying from other causes when making decisions about treating prostate cancer using pre-treatment patient-reported characteristics.","journal":"PLoS ONE","year":2020,"id":69482,"datarank":1.2442795576898702,"base_score":3.091042453358316,"endowment":3.091042453358316,"self_citation_contribution":0.4636563680037475,"citation_network_contribution":0.7806231896861227,"self_endowment_contribution":0.4636563680037475,"citer_contribution":0.7806231896861227,"corpus_percentile":83.50738763827647,"corpus_rank":2133,"citation_count":21,"citer_count":20,"citers_with_citation_signal":17,"citers_with_endowment":17,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.7101,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":370055,"name":"Gordon FitzGerald","orcid":null,"position":1,"is_corresponding":false},{"id":368349,"name":"Mara M. Epstein","orcid":"0000-0001-7906-4856","position":2,"is_corresponding":false},{"id":246153,"name":"Jeroan J. Allison","orcid":"0000-0003-4472-2112","position":3,"is_corresponding":false},{"id":370056,"name":"Mitchell H. Sokoloff","orcid":null,"position":4,"is_corresponding":false},{"id":368350,"name":"John E. Ware","orcid":"0000-0002-0744-2149","position":5,"is_corresponding":false},{"id":368348,"name":"Daniel M. Frendl","orcid":"0000-0002-3571-6610","position":0,"is_corresponding":true}],"reference_count":35,"raw_metadata":null,"created_at":"2026-07-18T21:42:48.813846Z","pmid":"33284845","pmcid":"PMC7721137","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":[]}