Medications and conditions associated with weight loss in patients prescribed semaglutide based on real-world data.

OBJECTIVE: Approved by the Food and Drug Administration (FDA) in 2017 for diabetes and in 2021 for weight loss, semaglutide has seen widespread use among individuals who aim to lose weight. The aim of this study was to evaluate weight loss and the influence of clinical factors on semaglutide patients in real-world clinical practice. METHODS: Using data from 10 health systems within the Greater Plains Collaborative (a PCORnet Clinical Research Network), nearly 4000 clinical factors encompassing demographic, diagnosis, and prescription information were extracted for semaglutide patients. A gradient-boosting, machine-learning classifier was developed for weight-loss prediction and identification of the most impactful factors via SHapley Additive exPlanations (SHAP) value extrapolation. RESULTS: A total of 3555 eligible patients (539 of whom were observed 52 weeks following exposure) from March 2017 to April 2022 were studied. On average, individuals lost 4.44% (male individuals, 3.66%; female individuals, 5.08%) of their initial weight. History of diabetes mellitus diagnosis was associated with less weight loss, whereas prediabetes and linaclotide use were associated with more pronounced weight loss. CONCLUSIONS: Weight loss in patients prescribed semaglutide from real-world evidence was strong but attenuated compared with previous clinical trials. Machine-learning analysis of electronic health record data identified factors that warrant further research and consideration when tailoring weight-loss therapy.

Electronic health record data quality variability across a multistate clinical research network.

Background: Electronic health record (EHR) data have many quality problems that may affect the outcome of research results and decision support systems. Many methods have been used to evaluate EHR data quality. However, there has yet to be a consensus on the best practice. We used a rule-based approach to assess the variability of EHR data quality across multiple healthcare systems. Methods: To quantify data quality concerns across healthcare systems in a PCORnet Clinical Research Network, we used a previously tested rule-based framework tailored to the PCORnet Common Data Model to perform data quality assessment at 13 clinical sites across eight states. Results were compared with the current PCORnet data curation process to explore the differences between both methods. Additional analyses of testosterone therapy prescribing were used to explore clinical care variability and quality. Results: The framework detected discrepancies across sites, revealing evident data quality variability between sites. The detailed requirements encoded the rules captured additional data errors with a specificity that aids in remediation of technical errors compared to the current PCORnet data curation process. Other rules designed to detect logical and clinical inconsistencies may also support clinical care variability and quality programs. Conclusion: Rule-based EHR data quality methods quantify significant discrepancies across all sites. Medication and laboratory sources are causes of data errors.

Tailoring Rule-Based Data Quality Assessment to the Patient-Centered Outcomes Research Network (PCORnet) Common Data Model (CDM).

Individual researchers and research networks have developed and applied different approaches to assess the data quality of electronic health record (EHR) data. A previously published rules-based method to evaluate the data quality of EHR data provides deeper levels of data quality analysis. To examine the effectiveness and generalizability of the rule-based framework, we reprogrammed and translated published rule templates to operate against the PCORnet Common Data Model and executed them against a database for a single center of the Greater Plains Collaborative (GPC) PCORnet Clinical Research Network. The framework detected additional data errors and logical inconsistencies not revealed by current data quality procedures. Laboratory and medication data were more vulnerable to errors. Hemolyzed samples in the emergency department and metformin prescribing in ambulatory clinics are further described to illustrate application of specific rule-based findings by researchers to engage their health systems in evaluating healthcare delivery and clinical quality concerns.

Retrospective data suggests that the higher prevalence of benign paroxysmal positional vertigo in individuals with type 2 diabetes is mediated by hypertension.

OBJECTIVE: Benign Paroxysmal Positional Vertigo (BPPV) has been linked to comorbidities like diabetes and hypertension. However, the relationship between type 2 diabetes (DM) and BPPV is unclear. The purpose of this retrospective study was to examine the relationship between DM and BPPV in the presence of known contributors like age, gender and hypertension. METHODS: A retrospective review of the records of 3933 individuals was categorized by the specific vestibular diagnosis and for the presence of type 2 DM and hypertension. As the prevalence of BPPV was higher in people with type 2 DM compared to those without DM, multivariable logistic regressions were used to identify variables predictive of BPPV. The relationship between type 2 DM, hypertension and BPPV was analyzed using mediation analysis. RESULTS: BPPV was seen in 46% of individuals with type 2 DM, compared to 37% of individuals without DM (p< 0.001). Forty two percent of the association between type 2 DM and BPPV was mediated by hypertension, and supported hypertension as a complete mediator in the relationship between type 2 DM and BPPV. CONCLUSIONS: Hypertension may provide the mediating pathway by which diabetes affects the vestibular system. Individuals with complaints of dizziness, with comorbidities including hypertension and diabetes, may benefit from a screening for BPPV.