Relative Contributions of Mixed Pathologies to Cognitive and Functional Symptoms in Brain Donors Exposed to Repetitive Head Impacts.

OBJECTIVE: Exposure to repetitive head impacts (RHI) is associated with later-life cognitive symptoms and neuropathologies, including chronic traumatic encephalopathy (CTE). Cognitive decline in community cohorts is often due to multiple pathologies; however, the frequency and contributions of these pathologies to cognitive impairment in people exposed to RHI are unknown. Here, we examined the relative contributions of 13 neuropathologies to cognitive symptoms and dementia in RHI-exposed brain donors. METHODS: Neuropathologists examined brain tissue from 571 RHI-exposed donors and assessed for the presence of 13 neuropathologies, including CTE, Alzheimer disease (AD), Lewy body disease (LBD), and transactive response DNA-binding protein 43 (TDP-43) inclusions. Cognitive status was assessed by presence of dementia, Functional Activities Questionnaire, and Cognitive Difficulties Scale. Spearman rho was calculated to assess intercorrelation of pathologies. Additionally, frequencies of pathological co-occurrence were compared to a simulated distribution assuming no intercorrelation. Logistic and linear regressions tested associations between neuropathologies and dementia status and cognitive scale scores. RESULTS: The sample age range was 18-97 years (median = 65.0, interquartile range = 46.0-76.0). Of the donors, 77.2% had at least one moderate-severe neurodegenerative or cerebrovascular pathology. Stage III-IV CTE was the most common neurodegenerative disease (43.1%), followed by TDP-43 pathology, AD, and hippocampal sclerosis. Neuropathologies were intercorrelated, and there were fewer unique combinations than expected if pathologies were independent (p < 0.001). The greatest contributors to dementia were AD, neocortical LBD, hippocampal sclerosis, cerebral amyloid angiopathy, and CTE. INTERPRETATION: In this sample of RHI-exposed brain donors with wide-ranging ages, multiple neuropathologies were common and correlated. Mixed neuropathologies, including CTE, underlie cognitive impairment in contact sport athletes. ANN NEUROL 2024;95:314-324.

Switching patterns of immediate-release forms of generic mixed amphetamine salts products among privately and publicly insured individuals aged 15-64 years in the United States, 2013-2019.

PURPOSE: Immediate-release forms of generic mixed amphetamine salts (MAS) have been the subject of passive surveillance reports signaling lack of effectiveness. We examined switching patterns that might suggest whether long-term users of specific MAS are more likely to switch away or switch back after use of the MAS of interest in the FDA's Sentinel Distributed Database. METHODS: We required at least 60-day continuous supply of selected MAS grouped by Abbreviated New Drug Application (ANDA) to describe patterns of switching away from and to generics approved under the ANDAs of interest among individuals ages 15-64 years with attention deficit hyperactivity disorder or narcolepsy during 2013-2019. RESULTS: We observed the greatest number of treatment episodes for ANDA 040422 (n = 525 771), followed by ANDA 202424 (n = 181 693), ANDA 040439 (n = 62 363), ANDA 040440 (n = 21 143), and ANDA 040480 (n = 8792). Of those with switches away from their original ANDA, episodes initiated on generic products under ANDA 040422 (48.6%) and ANDA 202424 (43.0%) were most likely to switch back, while those initiated on generic product under ANDA 040480 were least likely (24.1%). Of those episodes with switches to a generic under an ANDA of interest, about one-third (range 27.1% to 37.0%) switched back to the same product. These switches back had a median time to switch of about 30 days. CONCLUSIONS: These descriptive analyses, although subject to limitations, did not suggest increased switching away or switching back after use of the generics of interest. Continued post-marketing surveillance is warranted.

Monitoring Drug Safety in Pregnancy with Scan Statistics: A Comparison of Two Study Designs.

BACKGROUND: Traditional surveillance of adverse infant outcomes following maternal medication exposures relies on pregnancy exposure registries, which are often underpowered. We characterize the statistical power of TreeScan, a data mining tool, to identify potential signals in the setting of perinatal medication exposures and infant outcomes. METHODS: We used empirical data to inform background incidence of major congenital malformations and other birth conditions. Statistical power was calculated using two probability models compatible with TreeScan, Bernoulli and Poisson, while varying the sample size, magnitude of the risk increase, and incidence of a specified outcome. We also simulated larger referent to exposure matching ratios when using the Bernoulli model in the setting of fixed N:1 propensity score matching. Finally, we assessed the impact of outcome misclassification on power. RESULTS: The Poisson model demonstrated greater power to detect signals than the Bernoulli model across all scenarios and suggested a sample size of 4,000 exposed pregnancies is needed to detect a twofold increase in risk of a common outcome (approximately 8 per 1,000) with 85% power. Increasing the fixed matching ratio with the Bernoulli model did not reliably increase power. An outcome definition with high sensitivity is expected to have somewhat greater power to detect signals than an outcome definition with high positive predictive value. CONCLUSIONS: Use of the Poisson model with an outcome definition that prioritizes sensitivity may be optimal for signal detection. TreeScan is a viable method for surveillance of adverse infant outcomes following maternal medication use.

Novel methods for pregnancy drug safety surveillance in the FDA Sentinel System.

PURPOSE: It is a priority of the US Food and Drug Administration (FDA) to monitor the safety of medications used during pregnancy. Pregnancy exposure registries and cohort studies utilizing electronic health record data are primary sources of information but are limited by small sample sizes and limited outcome assessment. TreeScan™, a statistical data mining tool, can be applied within the FDA Sentinel System to simultaneously identify multiple potential adverse neonatal and infant outcomes after maternal medication exposure. METHODS: We implemented TreeScan using the Sentinel analytic tools in a cohort of linked live birth deliveries and infants nested in the IBM MarketScan® Research Database. As a case study, we compared first trimester fluoroquinolone use and cephalosporin use. We used the Bernoulli and Poisson TreeScan statistics with compatible propensity score-based study designs for confounding control (matching and stratification) and used multiple propensity score models with various strategies for confounding control to inform best practices. We developed a hierarchical outcome tree including major congenital malformations and outcomes of gestational length and birth weight. RESULTS: A total of 1791 fluoroquinolone-exposed and 8739 cephalosporin-exposed mother-infant pairs were eligible for analysis. Both TreeScan analysis methods resulted in single alerts that were deemed to be due to uncontrolled confounding or otherwise not warranting follow-up. CONCLUSIONS: In this implementation of TreeScan using Sentinel analytic tools, we did not observe any new safety signals for fluoroquinolone use in the first trimester. TreeScan, with tailored or high-dimensional propensity scores for confounding control, is a valuable tool in addition to current safety surveillance methods for medications used during pregnancy.

Phosphodiesterase type 5 inhibitor use among pregnant and reproductive-age women in the United States.

PURPOSE: To assess the prevalence and potential indications of PDE5 inhibitor use among pregnant and reproductive-age women in the United States. METHODS: We identified women 15 to 50 years with a livebirth from January 2001 through March 2018 in Sentinel Database. We assessed the prevalence of PDE5 inhibitor use prior to and during pregnancy by trimester, identified potential on- and off-label indications using predefined diagnosis codes recorded within 90 days before the estimated last menstrual period through delivery. Separately, we used data from IQVIA's National Prescription Audit and Total Patient Tracker to estimate the dispensed prescriptions for PDE5 inhibitors and the number of patients with PDE5 inhibitor prescriptions. RESULTS: We identified approximately 3.3 million pregnancies during 2001 to 2018, 96 of which had PDE5 inhibitor use during pregnancy. Prevalence of PDE5 inhibitor use was 2.61, 0.62, and 0.62 per 100, 000 live-born pregnancies during the first, second, or third trimesters, respectively. Among women exposed to a PDE5 inhibitor from 90 days before conception to the end of pregnancy, 25.0%, 31.1%, and 15.5% had a diagnosis code for fetal growth restriction, preeclampsia, and pulmonary arterial hypertension. In IQVIA data, an estimated 223, 000 prescriptions from July 2015 through June 2018 and 58, 000 women received prescriptions for PDE5 inhibitors in 2017, of whom approximately 15, 000 (26%) were aged 15 to 50 years. CONCLUSION: We found a low prevalence of PDE5 inhibitor use in pregnant and reproductive-age women. Given the very low prevalence of use and the inconsistency of neonatal mortality data across STRIDER centers, the risk to public health is low at present.

Development of an algorithm to detect methotrexate wrong frequency error using computerized health care data.

PURPOSE: We validated an algorithm to detect frequency errors in computerized healthcare data and estimated the incidence of these errors in an integrated healthcare system. METHODS: We applied Sentinel System analytic tools on the electronic health records of Kaiser Permanente, Northern California, January 1, 2010, through May 30, 2015,to identify rheumatoid arthritis (RA) patients with new use of methotrexate (365-day baseline period). We identified potential methotrexate frequency errors using ICD-9 code 995.20 (adverse drug event), Current Procedural Terminology (CPT) code 96409 for injection of leucovorin and prescription refill patterns. We performed chart review to confirm the frequency errors, assessed performance for detecting frequency errors, and estimated the incidence of chart-confirmed errors. RESULTS: The study included 24,529 methotrexate dispensings among 3,668 RA patients. Among these, 722 (3%) had one dispensing and 23,807 (97.1%) had ≥2 dispensings during 1-year follow-up period. We flagged 653 (2.7%) with a potential medication error (46 with one dispensing and 607 with ≥2 dispensings). We sampled 94 for chart review, and confirmed three methotrexate errors. All three confirmed frequency errors involved a first methotrexate dispensing followed by injected rescue therapy, leucovorin, (positive predictive value, 60%; 95% confidence interval [CI], 15-95%). No potential errors were found among patients with ≥2 dispensings. We estimated the frequency error incidence among one methotrexate dispensing to be 0.4% (95%CI, 0.1% to 1.2%). CONCLUSION: Rescue therapy is a specific indicator of methotrexate overdose among first methotrexate dispensings. This method is generalizable to other medications with serious adverse events treated with antidotes.

Overall and cause-specific mortality in the Sentinel system: A power analysis.

PURPOSE: Mortality data within the Sentinel Death Tables remain generally uncharacterized. Assessment of mortality data within Sentinel will help inform its utility for medical product safety studies. METHODS: To determine if Sentinel contains sufficient all-cause and cause-specific mortality events to power postmarketing safety studies. We calculated crude rates of all-cause mortality and suicide and proportional mortality from suicide from 2004 to 2012 in seven Sentinel data partners. Results were stratified by data partner, sex, age group, and calendar year and compared with national estimates from Centers for Disease Control and Prevention Wide-ranging ONline Data for Epidemiologic Research. We performed sample size estimations for all-cause mortality and 10 leading causes of death. RESULTS: We observed 479 694 deaths, including 5811 suicides, during 68 million person-years of follow-up. Pooled mean death and suicide rates in the data partners were 710 and 8.6 per 100 000 person-years, respectively (vs 810 and 11.8 nationally). The mean proportional mortality from suicide among the data partners was 1.2%, compared with 1.5% nationally. National trends of decreasing overall mortality and increasing proportional mortality for suicide were reflected within Sentinel. We estimated that detecting hazard ratios of 1.25 and 3 would require 16 442 and 460 exposed patients, respectively, for overall mortality, and 1.3 million and 37 411, respectively, for suicide. CONCLUSIONS: This was the first study to investigate mortality data in the Sentinel death tables. We found that all-cause mortality appeared well powered for use as a safety outcome and cause-specific mortality outcomes may be adequately powered in certain circumstances. Further investigation into the quality of the Sentinel death data is needed.

Early impact of the ICD-10-CM transition on selected health outcomes in 13 electronic health care databases in the United States.

PURPOSE: To describe the consistency in the frequency of 5 health outcomes across the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and Tenth Revision, Clinical Modification (ICD-10-CM) eras in the US. METHODS: We examined the incidence of 3 acute conditions (acute myocardial infarction [AMI], angioedema, ischemic stroke) and the prevalence of 2 chronic conditions (diabetes, hypertension) during the final 5 years of the ICD-9-CM era (January 2010-September 2015) and the first 15 months of the ICD-10-CM era (October 2015-December 2016) in 13 electronic health care databases in the Sentinel System. For each health outcome reviewed during the ICD-10-CM era, we evaluated 4 definitions, including published algorithms derived from other countries, as well as simple-forward, simple-backward, and forward-backward mapping using the General Equivalence Mappings. For acute conditions, we also compared the incidence between April to December 2014 and April to December 2016. RESULTS: The analyses included data from approximately 172 million health plan members. While the incidence or prevalence of AMI and hypertension performed similarly across the 2 eras, the other 3 outcomes did not demonstrate consistent trends for some or all the ICD-10-CM definitions assessed. CONCLUSIONS: When using data from both the ICD-9-CM and ICD-10-CM eras, or when using results from ICD-10-CM data to compare to results from ICD-9-CM data, researchers should test multiple ICD-10-CM outcome definitions as part of sensitivity analysis. Ongoing assessment of the impact of ICD-10-CM transition on identification of health outcomes in US electronic health care databases should occur as more data accrue.

Assessing the impact of the new ICD-10-CM coding system on pharmacoepidemiologic studies-An application to the known association between angiotensin-converting enzyme inhibitors and angioedema.

PURPOSE: To replicate the well-established association between angiotensin-converting enzyme inhibitors versus beta blockers and angioedema in the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) era. METHODS: We conducted a retrospective, inception cohort study in a large insurance database formatted to the Sentinel Common Data Model. We defined study periods spanning the ICD-9-CM era only, ICD-10-CM era only, and ICD-9-CM and ICD-10-CM era and conducted simple-forward mapping (SFM), simple-backward mapping (SBM), and forward-backward mapping (FBM) referencing the General Equivalence Mappings to translate the outcome (angioedema) and covariates from ICD-9-CM to ICD-10-CM. We performed propensity score (PS)-matched and PS-stratified Cox proportional hazards regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: In the ICD-9-CM and ICD-10-CM eras spanning April 1 to September 30 of 2015 and 2016, there were 152 017 and 145 232 angiotensin-converting enzyme inhibitor initiators and 115 073 and 116 652 beta-blocker initiators, respectively. The PS-matched HR was 4.19 (95% CI, 2.82-6.23) in the ICD-9-CM era, 4.37 (2.92-6.52) in the ICD-10-CM era using SFM, and 4.64 (3.05-7.07) in the ICD-10-CM era using SBM and FBM. The PS-matched HRs from the mixed ICD-9-CM and ICD-10-CM eras ranged from 3.91 (2.69-5.68) to 4.35 (3.33-5.70). CONCLUSION: The adjusted HRs across different diagnostic coding eras and the use of SFM versus SBM and FBM produced numerically different but clinically similar results. Additional investigations as ICD-10-CM data accumulate are warranted.