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Objective: Tobacco use/smoking for epidemiologic studies is often derived from electronic health record (EHR) data, which may be inaccurate. We previously compared smoking from the United States Veterans Health Administration (VHA) EHR clinical reminder data with survey data and found excellent agreement. However, the smoking clinical reminder items changed October 1, 2018. We sought to use the biomarker salivary cotinine (cotinine ≥30) to validate current smoking from multiple sources. Materials and Methods: We included 323 Veterans Aging Cohort Study participants with cotinine, clinical reminder, and self-administered survey smoking data from October 1, 2018 to September 30, 2019. We included International Classification of Disease (ICD)-10 codes F17.21 and Z72.0. Operating characteristics and kappa statistics were calculated. Results: Participants were mostly male (96%), African American (75%) and mean age was 63 years. Of those identified as currently smoking based on cotinine, 86%, 85%, and 51% were identified as currently smoking based on clinical reminder, survey, and ICD-10 codes, respectively. Of those identified as not currently smoking based on cotinine, 95%, 97%, and 97% were identified as not currently smoking based on clinical reminder, survey, and ICD-10 codes. Agreement with cotinine was substantial for clinical reminder (kappa = .81) and survey (kappa = .83), but only moderate for ICD-10 (kappa = .50). Discussion: To determine current smoking, clinical reminder, and survey agreed well with cotinine, whereas ICD-10 codes did not. Clinical reminders could be used in other health systems to capture more accurate smoking information. Conclusions: Clinical reminders are an excellent source for self-reported smoking status and are readily available in the VHA EHR.
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OBJECTIVE: To present the National Prion Disease Pathology Surveillance Center's (NPDPSC's) experience using CSF real-time quaking-induced conversion (RT-QuIC) as a diagnostic test, to examine factors associated with false-negative RT-QuIC results, and to investigate the impact of RT-QuICs on prion disease surveillance. METHODS: Between May 2015 and April 2018, the NPDPSC received 10,498 CSF specimens that were included in the study. Sensitivity and specificity analyses were performed on 567 autopsy-verified cases. Prion disease type, demographic characteristics, specimen color, and time variables were examined for association with RT-QuIC results. The effect of including positive RT-QuIC cases in prion disease surveillance was examined. RESULTS: The diagnostic sensitivity and specificity of RT-QuIC across all prion diseases were 90.3% and 98.5%, respectively. Diagnostic sensitivity was lower for fatal familial insomnia, Gerstmann-Sträussler-Scheinker disease, sporadic fatal insomnia, variably protease sensitive prionopathy, and the VV1 and MM2 subtypes of sporadic Creutzfeldt-Jakob disease. Individuals with prion disease and negative RT-QuIC results were younger and had lower tau levels and nonelevated 14-3-3 levels compared to RT-QuIC-positive cases. Sensitivity was high throughout the disease course. Some cases that initially tested RT-QuIC negative had a subsequent specimen test positive. Including positive RT-QuIC cases in surveillance statistics increased laboratory-based case ascertainment of prion disease by 90% over autopsy alone. CONCLUSIONS: RT-QuIC has high sensitivity and specificity for diagnosing prion diseases. Sensitivity limitations are associated with prion disease type, age, and related CSF diagnostic results. RT-QuIC greatly improves laboratory-based prion disease ascertainment for surveillance purposes. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that second-generation RT-QuIC identifies prion disease with a sensitivity of 90.3% and specificity of 98.5% among patients being screened for these diseases due to concerning symptoms.