Search

BackgroundCertain medications may increase the risk of death or death from specific causes (eg, sudden cardiac death), but these risks may not be identified in premarket randomized trials. Having the capacity to examine death in postmarket safety surveillance activities is important to the US Food and Drug Administration’s (FDA) mission to protect public health. Distributed networks of electronic health plan databases used by the FDA to conduct multicenter research or medical product safety surveillance studies often do not systematically include death or cause-of-death information. ObjectiveThis study aims to develop reusable, generalizable methods for linking multiple health plan databases with the Centers for Disease Control and Prevention’s National Death Index Plus (NDI+) data. MethodsWe will develop efficient administrative workflows to facilitate multicenter institutional review board (IRB) review and approval within a distributed network of 6 health plans. The study will create a distributed NDI+ linkage process that avoids sharing of identifiable patient information between health plans or with a central coordinating center. We will develop standardized criteria for selecting and retaining NDI+ matches and methods for harmonizing linked information across multiple health plans. We will test our processes within a use case comprising users and nonusers of antiarrhythmic medications. ResultsWe will use the linked health plan and NDI+ data sets to estimate the incidences and incidence rates of mortality and specific causes of death within the study use case and compare the results with reported estimates. These comparisons provide an opportunity to assess the performance of the developed NDI+ linkage approach and lessons for future studies requiring NDI+ linkage in distributed database settings. This study is approved by the IRB at Harvard Pilgrim Health Care in Boston, MA. Results will be presented to the FDA at academic conferences and published in peer-reviewed journals. ConclusionsThis study will develop and test a reusable distributed NDI+ linkage approach with the goal of providing tested NDI+ linkage methods for use in future studies within distributed data networks. Having standardized and reusable methods for systematically obtaining death and cause-of-death information from NDI+ would enhance the FDA’s ability to assess mortality-related safety questions in the postmarket, real-world setting. International Registered Report Identifier (IRRID)DERR1-10.2196/21811

Aim: The transition to the ICD‐10‐CM coding system has reduced the utility of hypoglycaemia algorithms based on ICD‐9‐CM diagnosis codes in real‐world studies of antidiabetic drugs. We mapped a validated ICD‐9‐CM hypoglycaemia algorithm to ICD‐10‐CM codes to create an ICD‐10‐CM hypoglycaemia algorithm and assessed its performance in identifying severe hypoglycaemia. Materials and Methods: We assembled a cohort of Medicare patients with DM and linked electronic health record (EHR) data to the University of North Carolina Health System and identified candidate severe hypoglycaemia events from their Medicare claims using the ICD‐10‐CM hypoglycaemia algorithm. We confirmed severe hypoglycaemia by EHR review and computed a positive predictive value (PPV) of the algorithm to assess its performance. We refined the algorithm by removing poor performing codes (PPV ≤0.5) and computed a Cohen's κ statistic to evaluate the agreement of the EHR reviews. Results: The algorithm identified 642 candidate severe hypoglycaemia events, and we confirmed 455 as true severe hypoglycaemia events, PPV of 0.709 (95% confidence interval: 0.672, 0.744). When we refined the algorithm, the PPV increased to 0.893 (0.862, 0.918) and missed <2.42% (<11) true severe hypoglycaemia events. Agreement between reviewers was high, κ = 0.93 (0.89, 0.97). Conclusions: We translated an ICD‐9‐CM hypoglycaemia algorithm to an ICD‐10‐CM version and found its performance was modest. The performance of the algorithm improved by removing poor performing codes at the trade‐off of missing very few severe hypoglycaemia events. The algorithm has the potential to be used to identify severe hypoglycaemia in real‐world studies of antidiabetic drugs. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the impact of a pediatric epilepsy care management intervention on emergency department visits, hospitalizations, and seizure freedom. Methods: We conducted a prospective observational study at a single academic medical center. Children with epilepsy with high risk of frequent emergency department use were enrolled in the intervention from January through May 2015, which included a baseline visit and follow-up support from a care management team. Controls selected from the same institution received standard of care. Baseline and follow-up information were collected from electronic health records and surveys (Family Impact Scale, Pediatric Epilepsy Medication Self-Management Questionnaire). Propensity score–weighted logistic regression compared emergency department visits, unplanned hospitalizations, and 3-month seizure freedom after 1 year in the intervention vs control groups. Results: A total of 56 children were enrolled in the intervention and 359 received standard of care. The intervention group was younger and had greater use of health services at baseline. When comparing the intervention to standard of care after 1 year, we found no significant difference in the risk of any emergency department visit (adjusted odds ratio [OR] 2.2, 95% confidence interval [CI] 0.6-8.5) or seizure freedom (adjusted OR 2.5, 95% CI 0.3-21.5). However, the risk of unplanned hospital admissions remained higher in the intervention group (adjusted OR 23.1, 95% CI 5.1-104). Conclusion: We did not find that children with epilepsy who received a care management intervention had less use of health services or better clinical outcomes after a year compared with controls. The study is limited by small sample size and nonrandomized study design. [ABSTRACT FROM AUTHOR]

Background: It is unknown whether observational studies evaluating the association between antidiabetic medications and mortality adequately account for frailty. Our objectives were to evaluate if frailty was a potential confounder in the relationship between antidiabetic medication regimen and mortality and how well administrative and clinical electronic health record (EHR) data account for frailty.Methods: We conducted a retrospective cohort study in a single Veterans Health Administration (VHA) healthcare system of 500 hospitalizations-the majority due to heart failure-of Veterans who received regular VHA care and initiated type 2 diabetes treatment from 2001 to 2008. We measured frailty using a modified frailty index (FI, >0.21 frail). We obtained antidiabetic medication regimen and time-to-death from administrative sources. We compared FI among patients on different antidiabetic regimens. Stepwise Cox proportional hazards regression estimated time-to-death by demographic, administrative, clinical EHR, and FI data.Results: Median FI was 0.22 (interquartile range 0.18, 0.27). Frailty differed across antidiabetic regimens (p < .001). An FI increase of 0.05 was associated with an increased risk of death (hazard ratio 1.45, 95% confidence interval 1.32, 1.60). Cox proportional hazards model for time-to-death including demographic, administrative, and clinical EHR data had a c-statistic of 0.70; adding FI showed marginal improvement (c-statistic 0.72).Conclusions: Frailty was associated with antidiabetic regimen and death, and may confound that relationship. Demographic, administrative, and clinical EHR data, commonly used to balance differences among exposure groups, performed moderately well in assessing risk of death, with minimal gain from adding frailty. Study design and analytic techniques can help minimize potential confounding by frailty in observational studies. [ABSTRACT FROM AUTHOR]