Artificial Intelligence Tool for Optimizing Eligibility Screening for Clinical Trials in a Large Community Cancer Center.

PURPOSE: Less than 5% of patients with cancer enroll in clinical trials, and 1 in 5 trials are stopped for poor accrual. We evaluated an automated clinical trial matching system that uses natural language processing to extract patient and trial characteristics from unstructured sources and machine learning to match patients to clinical trials. PATIENTS AND METHODS: Medical records from 997 patients with breast cancer were assessed for trial eligibility at Highlands Oncology Group between May and August 2016. System and manual attribute extraction and eligibility determinations were compared using the percentage of agreement for 239 patients and 4 trials. Sensitivity and specificity of system-generated eligibility determinations were measured, and the time required for manual review and system-assisted eligibility determinations were compared. RESULTS: Agreement between system and manual attribute extraction ranged from 64.3% to 94.0%. Agreement between system and manual eligibility determinations was 81%-96%. System eligibility determinations demonstrated specificities between 76% and 99%, with sensitivities between 91% and 95% for 3 trials and 46.7% for the 4th. Manual eligibility screening of 90 patients for 3 trials took 110 minutes; system-assisted eligibility determinations of the same patients for the same trials required 24 minutes. CONCLUSION: In this study, the clinical trial matching system displayed a promising performance in screening patients with breast cancer for trial eligibility. System-assisted trial eligibility determinations were substantially faster than manual review, and the system reliably excluded ineligible patients for all trials and identified eligible patients for most trials.

Antiemetic guideline consistency and incidence of chemotherapy-induced nausea and vomiting in US community oncology practice: INSPIRE Study.

PURPOSE: Consensus guidelines for preventing chemotherapy-induced nausea and vomiting (CINV) are variably implemented in practice. The purpose of this study was to evaluate the impact of guideline-consistent/guideline-inconsistent CINV prophylaxis (GCCP/GICP) on the incidence of no CINV after cycle 1 of highly or moderately emetogenic chemotherapy (HEC or MEC). PATIENTS AND METHODS: This prospective observational study enrolled chemotherapy-naive adult outpatients who received single-day HEC or MEC at four oncology practice networks, all using electronic health record (EHR) systems, in Georgia, Tennessee, and Florida. Results from the Multinational Association of Supportive Care in Cancer Antiemesis Tool, a validated tool to measure CINV, administered 5 to 8 days postchemotherapy, were merged with EHR data. The primary end point, no CINV, defined as no emesis and no clinically significant nausea (score < 3 on 0-10 scale), was compared between cohorts using logistic regression. RESULTS: A total of 1,295 patients were enrolled (mean age, 59.3 years; 70.0% female; 35.5% HEC). The overall prevalence of GCCP was 57.3%. When corticosteroids were prescribed on days 2 to 4 after all HEC, GCCP for HEC increased from 28.7% to 89.8%; when NK1-receptor antagonists were prescribed after all MEC, GCCP for MEC increased from 73.1% to 97.8%. Over 5 days postchemotherapy, the incidence of no CINV was significantly higher in the GCCP cohort than the GICP cohort (53.4% v 43.8%; P < .001). The adjusted odds of no CINV with GCCP was 1.31 (95% CI, 1.07 to 1.69; P = .037). CONCLUSION: Increased adherence to antiemetic guidelines could significantly reduce the incidence of CINV after HEC and MEC.