{"doi":"10.1093/jnci/djad053","title":"Risk prediction of hepatitis B or C or HIV among newly diagnosed cancer patients","abstract":"BACKGROUND: Screening for viral infection in cancer patients is inconsistent. A mechanism to readily identify cancer patients at increased risk of existing or prior viral infection could enhance screening efforts while reducing costs. METHODS: We identified factors associated with increased risk of past or chronic hepatitis virus B, hepatitis virus C, or HIV infection before initiation of systemic cancer therapy. Data were from a multicenter prospective cohort study of 3051 patients with newly diagnosed cancer (SWOG-S1204) enrolled between 2013 and 2017. Patients completed a survey with questions pertaining to personal history and behavioral, socioeconomic, and demographic risk factors for viral hepatitis or HIV. We derived a risk model to predict the presence of viral infection in a random set of 60% of participants using best subset selection. The derived model was validated in the remaining 40% of participants. Logistic regression was used. RESULTS: A model with 7 risk factors was identified, and a risk score with 4 levels was constructed. In the validation cohort, each increase in risk level was associated with a nearly threefold increased risk of viral positivity (odds ratio = 2.85, 95% confidence interval = 2.26 to 3.60, P < .001). Consistent findings were observed for individual viruses. Participants in the highest risk group (with >3 risk factors), comprised of 13.4% of participants, were 18 times more likely to be viral positive compared with participants with no risk factors (odds ratio = 18.18, 95% confidence interval = 8.00 to 41.3, P < .001). CONCLUSIONS: A risk-stratified screening approach using a limited set of questions could serve as an effective strategy to streamline screening for individuals at increased risk of viral infection.","journal":"JNCI Journal of the National Cancer Institute","year":2023,"id":388011,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8913,"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":571530,"name":"Cathee Till","orcid":"0000-0002-6801-8333","position":1,"is_corresponding":false},{"id":589399,"name":"Jessica Hwang","orcid":"0000-0002-5896-4182","position":2,"is_corresponding":false},{"id":433836,"name":"Kathryn B. Arnold","orcid":"0000-0001-8162-7179","position":3,"is_corresponding":false},{"id":268627,"name":"Michael LeBlanc","orcid":"0000-0001-7192-2860","position":4,"is_corresponding":false},{"id":107125,"name":"Dawn L. Hershman","orcid":"0000-0001-8807-153X","position":5,"is_corresponding":false},{"id":355457,"name":"Scott D. Ramsey","orcid":"0000-0001-5972-8280","position":6,"is_corresponding":false},{"id":274181,"name":"Joseph M. Unger","orcid":"0000-0002-5191-0317","position":0,"is_corresponding":true}],"reference_count":24,"raw_metadata":null,"created_at":"2026-07-19T01:18:13.977537Z","pmid":"36946291","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":[]}