Ancestry-specific and transethnic GWASs of 25(OH)D confirmed findings in GC and DHCR7 for African and Hispanic American samples and revealed findings near KIF4B, ANO6/ARID2, and HTR2A. The biological mechanisms that link these regions with 25(OH)D metabolism warrant further investigation.
OBJECTIVE: To examine user uptake and experience with a clinical chatbot that automates hereditary cancer risk triage by collecting personal and family cancer history in routine women's health care settings. METHODS: We conducted a multicenter, retrospective observational study of patients who used a web-based chatbot before routine care appointments to assess their risk for hereditary breast and ovarian cancer, Lynch syndrome, and adenomatous polyposis syndromes. Outcome measures included uptake and completion of the risk-assessment and educational section of the chatbot interaction and identification of hereditary cancer risk as evaluated against National Comprehensive Cancer Network criteria. RESULTS: Of the 95,166 patients invited, 61,070 (64.2%) engaged with the clinical chatbot. The vast majority completed the cancer risk assessment (89.4%), and most completed the genetic testing education section (71.4%), indicating high acceptability among those who opted to engage. The mean duration of use was 15.4 minutes (SD 2 hours, 56.2 minutes) when gaps of inactivity longer than 5 minutes were excluded. A personal history of cancer was reported by 19.1% (10,849/56,656) and a family history of cancer was reported by 66.7% (36,469/54,652) of patients who provided the relevant information. One in four patients (14,850/54,547) screened with the chatbot before routine care appointments met National Comprehensive Cancer Network criteria for genetic testing. Among those who were tested, 5.6% (73/1,313) had a disease-causing pathogenic variant. CONCLUSION: A chatbot digital health tool can help identify patients at high risk for hereditary cancer syndromes before routine care appointments. This scalable intervention can effectively provide cancer risk assessment, engage patients with educational information, and facilitate a path toward preventive genetic testing. FUNDING SOURCE: Implementation of the chatbot in clinics was funded by industry support from commercial genetic testing laboratories Ambry, Invitae, and Progenity.
Key Points Question Does combined disease testing provide improved diagnostic yield and clinical utility for patients with a suspected genetic cardiomyopathy or arrhythmia? Findings In this cohort study of 4782 patients with a suspected genetic cardiomyopathy or arrhythmia, combined cardiomyopathy and arrhythmia testing revealed clinically relevant variants in 1 in 5 patients, and 66.0% of patients with positive findings had potential clinical management implications. The combined testing approach captured 10.9% of patients who would have been missed if genetic testing had been restricted to a specific suspected disease subtype. Meaning This study’s findings suggest that combined cardiomyopathy and arrhythmia genetic testing is able to identify genetic etiologies associated with these diseases that can inform patient management.
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