Nucleic acid amplification technologies allow for the development of highly sensitive and specific diagnostic assays. The capacity to amplify and detect analyte targets, which may be present in a clinical sample as a single copy; is characteristic of many of these amplification technologies. NASBA is an isothermal method of nucleic acid amplification with such capability, and is particularly well suited for the amplification of RNA analytes. NASBA utilizes the coordinated activities of three enzymes (AMV-RT, RNase H, T7 RNA polymerase), and two oligonucleotide primers which are specific for the analyte target. The amplification process is part of a total system which includes a versatile nucleic acid isolation procedure, and powerful detection methodology. In this report, the development of NASBA technology for the detection of human Retrovirus RNA will be discussed. Specifically, a qualitative NASBA assay for the RNA of HTLV I, and a quantitative NASBA assay for HIV-1 will be described.
Background: Suicide is a leading cause of death worldwide and results in a large number of person years of life lost. There is an opportunity to evaluate whether administrative health care system data and machine learning can quantify suicide risk in a clinical setting. Methods: The objective was to compare the performance of prediction models that quantify the risk of death by suicide within 90 days of an ED visit for parasuicide with predictors available in administrative health care system data. The modeling dataset was assembled from 5 administrative health care data systems. The data systems contained nearly all of the physician visits, ambulatory care visits, inpatient hospitalizations, and community pharmacy dispenses, of nearly the entire 4.07 million persons in Alberta, Canada. 101 predictors were selected, and these were assembled for each of the 8 quarters (2 years) prior to the quarter of death, resulting in 808 predictors in total for each person. Prediction model performance was validated with 10-fold cross-validation. Findings: The optimal gradient boosted trees prediction model achieved promising discrimination (AUC: 0.88) and calibration that could lead to clinical applications. The 5 most important predictors in the optimal gradient boosted trees model each came from a different administrative health care data system. Interpretation: The combination of predictors from multiple administrative data systems and the combination of personal and ecologic predictors resulted in promising prediction performance. Further research is needed to develop prediction models optimized for implementation in clinical settings. Funding: There was no funding for this study.
BackgroundBecause of symptom overlap, there is uncertainty about the validity of depression rating scales in neurologic populations. The objectives of this study were to evaluate the validity of the Patient Health Questionnaire-9 (PHQ-9) for detecting Diagnostic and Statistical Manual–defined major depressive episodes in people with neurologic conditions.MethodsParticipants were recruited from outpatient clinics for multiple sclerosis, epilepsy, migraine, Parkinson disease, and stroke for this cross-sectional study. Participants were administered a questionnaire (this included the PHQ-9), chart review, and a follow-up telephone interview. The Structured Clinical Interview for Depression was used as the reference standard for psychiatric diagnoses. The performance of PHQ-9 was analyzed using sensitivity, specificity, diagnostic odds ratios (DORs), and receiver operator curve analysis.ResultsAll neurologic subpopulations had a specificity greater than 78% and sensitivity greater than 79% at a cut-point of 10. Using a random-effects model, the I-squared value was 13.7%, and Tau2 was 0.05, showing homogeneity across the neurologic subpopulations. The pooled DOR was 25.3 (95% confidence interval [CI] 14.9–42.8). Meta-analytic analysis found that for sensitivity, the pooled estimate was 90% (95% CI 81–97), and for specificity, it was 85% (95% CI 79–90).ConclusionsDespite theoretical concerns about its validity, the PHQ-9 performed well at its standard cut-point of 10. Consistent with the literature, being able to use a validated, brief tool that is available publicly should improve case finding of depression in neurologic populations. When considering clinical practicality along with the findings of this analyzed, this study confirmed that the PHQ-9 is valid in a general outpatient neurologic population.
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