{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["5(3)"],"submitter":["Liu R"],"funding":["Stanford University","Roche"],"pubmed_abstract":["Patients with cancer may be given treatments that are not officially approved (off-label) or recommended by guidelines (off-guideline). Here we present a data science framework to systematically characterize off-label and off-guideline usages using real-world data from de-identified electronic health records (EHR). We analyze treatment patterns in 165,912 US patients with 14 common cancer types. We find that 18.6% and 4.4% of patients have received at least one line of off-label and off-guideline cancer drugs, respectively. Patients with worse performance status, in later lines, or treated at academic hospitals are significantly more likely to receive off-label and off-guideline drugs. To quantify how predictable off-guideline usage is, we developed machine learning models to predict which"],"journal":["Cell reports. Medicine"],"pagination":["101444"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10983036"],"repository":["biostudies-literature"],"pubmed_title":["Systematic analysis of off-label and off-guideline cancer therapy usage in a real-world cohort of 165,912 US patients."],"pmcid":["PMC10983036"],"pubmed_authors":["Zou J","Waliany S","McGough S","Huang Z","Rizzo S","Lin YG","Copping R","Liu R","Garmhausen MR","Neal J","Pal N","Wang L"],"additional_accession":[]},"is_claimable":false,"name":"Systematic analysis of off-label and off-guideline cancer therapy usage in a real-world cohort of 165,912 US patients.","description":"Patients with cancer may be given treatments that are not officially approved (off-label) or recommended by guidelines (off-guideline). Here we present a data science framework to systematically characterize off-label and off-guideline usages using real-world data from de-identified electronic health records (EHR). We analyze treatment patterns in 165,912 US patients with 14 common cancer types. We find that 18.6% and 4.4% of patients have received at least one line of off-label and off-guideline cancer drugs, respectively. Patients with worse performance status, in later lines, or treated at academic hospitals are significantly more likely to receive off-label and off-guideline drugs. To quantify how predictable off-guideline usage is, we developed machine learning models to predict which","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Mar","modification":"2025-05-29T19:27:57.643Z","creation":"2025-05-29T19:27:57.643Z"},"accession":"S-EPMC10983036","cross_references":{"pubmed":["38428426"],"doi":["10.1016/j.xcrm.2024.101444"]}}