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BackgroundThe clinical research enterprise is not producing the evidence decision makers arguably need in a timely and cost effective manner; research currently involves the use of labor-intensive parallel systems that are separate from clinical care. The emergence of pragmatic clinical trials (PCTs) poses a possible solution: these large-scale trials are embedded within routine clinical care and often involve cluster randomization of hospitals, clinics, primary care providers, etc. Interventions can be implemented by health system personnel through usual communication channels and quality improvement infrastructure, and data collected as part of routine clinical care. However, experience with these trials is nascent and best practices regarding design operational, analytic, and reporting methodologies are undeveloped.MethodsTo strengthen the national capacity to implement cost-effective, large-scale PCTs, the Common Fund of the National Institutes of Health created the Health Care Systems Research Collaboratory (Collaboratory) to support the design, execution, and dissemination of a series of demonstration projects using a pragmatic research design.ResultsIn this article, we will describe the Collaboratory, highlight some of the challenges encountered and solutions developed thus far, and discuss remaining barriers and opportunities for large-scale evidence generation using PCTs.ConclusionA planning phase is critical, and even with careful planning, new challenges arise during execution; comparisons between arms can be complicated by unanticipated changes. Early and ongoing engagement with both health care system leaders and front-line clinicians is critical for success. There is also marked uncertainty when applying existing ethical and regulatory frameworks to PCTS, and using existing electronic health records for data capture adds complexity.

Electronic medical record (EMR) systems provide easy access to radiology reports and offer great potential to support quality improvement efforts and clinical research. Harnessing the full potential of the EMR requires scalable approaches such as natural language processing (NLP) to convert text into variables used for evaluation or analysis. Our goal was to determine the feasibility of using NLP to identify patients with Type 1 Modic endplate changes using clinical reports of magnetic resonance (MR) imaging examinations of the spine. Identifying patients with Type 1 Modic change who may be eligible for clinical trials is important as these findings may be important targets for intervention. Four annotators identified all reports that contained Type 1 Modic change, using N = 458 randomly selected lumbar spine MR reports. We then implemented a rule-based NLP algorithm in Java using regular expressions. The prevalence of Type 1 Modic change in the annotated dataset was 10%. Results were recall (sensitivity) 35/50 = 0.70 (95% confidence interval (C.I.) 0.52–0.82), specificity 404/408 = 0.99 (0.97–1.0), precision (positive predictive value) 35/39 = 0.90 (0.75–0.97), negative predictive value 404/419 = 0.96 (0.94–0.98), and F1-score 0.79 (0.43–1.0). Our evaluation shows the efficacy of rule-based NLP approach for identifying patients with Type 1 Modic change if the emphasis is on identifying only relevant cases with low concern regarding false negatives. As expected, our results show that specificity is higher than recall. This is due to the inherent difficulty of eliciting all possible keywords given the enormous variability of lumbar spine reporting, which decreases recall, while availability of good negation algorithms improves specificity. [ABSTRACT FROM AUTHOR]

Importance: Nasal congestion occurring after continuous positive airway pressure (CPAP) treatment initiation impairs CPAP adherence. Allergic rhinitis is associated with worsening nasal congestion in patients who are exposed to nonallergic triggers. Use of CPAP presents potential nonallergic triggers (eg, humidity, temperature, pressure, and airflow)., Objective: To compare nasal congestion among CPAP users with allergic rhinitis, nonallergic rhinitis, and no rhinitis. We hypothesize that CPAP patients with baseline allergic rhinitis are more likely to experience a worsening of nasal congestion (or less improvement in nasal congestion) compared with patients with no baseline rhinitis., Design, Setting, and Participants: This prospective cohort study included consecutive patients newly diagnosed with obstructive sleep apnea in a tertiary sleep center who were using CPAP therapy 3 months after diagnosis. Baseline rhinitis status was assigned as allergic rhinitis, nonallergic rhinitis, or no rhinitis, based on questionnaire responses and past allergy testing. Data were collected from 2004 to 2008 and analyzed from July 2019 to February 2020., Main Outcomes and Measures: At baseline before CPAP exposure and again 3 months later, subjective nasal congestion was measured with the Nasal Obstruction Symptom Evaluation (NOSE) scale and a visual analog scale (VAS), each scored from 0 to 100 (100 = worst congestion). Changes in nasal congestion were tested over 3 months for the whole cohort, within each rhinitis subgroup (paired t test), and between rhinitis subgroups (multivariate linear regression)., Results: The study cohort comprised 102 participants, of whom 61 (60%) were male and the mean (SD) age was 50 (13). The study included 23 (22.5%) participants with allergic rhinitis, 67 (65.7%) with nonallergic rhinitis, and 12 (11.8%) with no rhinitis. Nasal congestion improved from baseline to 3 months in the whole cohort (mean [SD] NOSE score, 38 [26] to 27 [23], mean [SD] change, -10 [23]; 95% CI, -15 to -6; mean [SD] VAS score, 41 [27] to 32 [28]; mean [SD] change, -10 [26]; 95% CI, [-15 to -4]) and in each rhinitis subgroup. Adjusted improvement in nasal congestion at 3 months was significantly less in the allergic rhinitis subgroup compared with the no rhinitis subgroup (positive difference means less improvement) compared with baseline: NOSE score 14 (95% CI, 1 to 28) and VAS score 15 (95% CI, 0 to 30)., Conclusions and Relevance: Initiation of CPAP was associated with improved subjective nasal congestion, but less improvement in patients with baseline allergic rhinitis. Baseline allergic rhinitis may predict which patients are more vulnerable to potential congestive effects of CPAP.

Importance: With the increasing emphasis on economic evaluations, there is a need for additional methods of measuring patient utility in the obstructive sleep apnea population., Objective: To develop and validate a utility scoring algorithm for a sleep apnea-specific quality-of-life instrument., Design, Setting, and Participants: Development and validation were conducted at 2 tertiary referral sleep centers and associated sleep clinics and included patients with newly diagnosed obstructive sleep apnea from a randomized clinical trial and an associated observational cohort study. Baseline participants were randomly divided into a model development group (60%) and a cross-validation group (40%)., Main Outcomes and Measures: Utility scoring of the Symptoms of Nocturnal Obstruction and Related Events (SNORE-25) was mapped from the SF-6D utility index through multiple linear regression in the development sample using the Akaike information criterion to determine the best model., Results: A total of 500 participants (development, n = 300; validation, n = 200) were enrolled; the analyzed sample of 500 participants included 295 men (59%), and the mean (SD) age was 48.6 (12.8) years, with a range of 18 to 90 years. The mean (SD) SF-6D utility among participants with untreated sleep apnea was 0.61 (0.08; range, 0.40-0.85) with similar utility across sleep apnea severity groups. The best-fit model (the SNORE Utility Index) was the natural log conversion of the instrument subscales (r2 = 0.32 in the development sample). The SNORE Utility Index retained this association within the validation sample (r2 = 0.33)., Conclusions and Relevance: The SNORE Utility Index provides a validated, disease-specific, preference-weighted utility instrument that can be used in future studies of patients with obstructive sleep apnea.

Objective: To compare 2 composite indices of sleep apnea disease burden with the commonly used apnea-hypopnea index with regard to baseline measurement of subjective and objective disease burden., Study Design: Cross-sectional study., Setting: Tertiary academic medical center sleep laboratory., Subjects and Methods: Patients with suspected diagnosis of sleep apnea undergoing first diagnostic polysomnography. Subjective data were collected via validated questionnaires; objective data were obtained by standardized physical examination, chart extraction, and polysomnography. Four subjective (patient-reported) disease burden measures and 3 objective (anatomic and physiologic) disease burden measures were used for validation. Associations between composite indices or apnea-hypopnea index and these 7 construct validation measures were compared using bootstrapped correlation coefficients., Results: Two hundred sixteen subjects were included in the final analysis. Both composite disease burden indices showed clinically important or nearly important associations with 3 of 4 subjective validation measures and all 3 objective validation measures, whereas the apnea-hypopnea index was associated only with the objective validation measures., Conclusion: Composite indices of sleep apnea disease burden may capture the breadth of baseline sleep apnea disease burden, particularly subjective disease burden, better than the apnea-hypopnea index.