Real-World Risk of Severe Cytopenias in Multiple Myeloma Patients Sequentially Treated with Immunomodulatory Drugs.

INTRODUCTION: Most multiple myeloma (MM) patients experience cytopenias, likely driven by both disease and treatment-related factors. Immunomodulatory agents (IMiDs), which form the backbone of most anti-myeloma regimens, are known to cause higher grade cytopenias. In this context, the impact of sequential IMiD treatments on cytopenia risk is unknown. METHODS: We evaluated the cumulative risks of severe cytopenias following second line of therapy (LOT) initiation in 5573 MM patients in the Flatiron Health database. Patients for whom both LOTs 1 and 2 contained IMiDs were considered "sequentially exposed"; those for whom neither contained IMiDs were "never exposed." RESULTS: For the neutropenia outcome, compared to the never exposed, the sequentially exposed had the highest 1-year risk (risk difference [RD] 12%), followed by those only recently exposed during LOT 2 (RD 8%), then by those with only past exposure during LOT 1 (RD 5%). A similar pattern was observed for leukopenia, but no meaningful differences were observed for anemia or thrombocytopenia. The associations between sequential exposure, versus never, with neutropenia and leukopenia were even stronger among those with a recent cytopenia history. CONCLUSION: Results suggest that sequential exposure to IMiDs is a risk factor for higher grade cytopenias. These findings have profound clinical implications in choosing newer LOTs with potential risks of cytopenia.

Influence of Incomplete Death Information on Cumulative Risk Estimates in United States Claims Data.

Administrative claims databases often do not capture date or fact of death, so studies using these data may inappropriately treat death as a censoring event-equivalent to other withdrawal reasons-rather than a competing event. We examined 1-, 3-, and 5-year inverse-probability-of-treatment-weighted cumulative risks of a composite cardiovascular outcome among 34,527 initiators of telmisartan (exposure) and ramipril (referent) ages ≥55 in Optum claims from 2003 to 2020. Differences in cumulative risks of the cardiovascular endpoint due to censoring of death (cause-specific), as compared to treating death as a competing event (sub-distribution), increased with greater follow-up time and older age, where event and mortality risks were higher. Among ramipril users (selected results), 5-year cause-specific and sub-distribution cumulative risk estimates per 100, respectively, were 16.4 (95% CI 15.3, 17.5) and 16.2 (95% CI 15.1, 17.3) among ages 55-64 (difference=0.2) and were 43.2 (95% CI 41.3, 45.2) and 39.7 (95% CI 37.9, 41.4) among ages ≥75 (difference=3.6). Plasmode simulation results demonstrated the differences in cause-specific versus sub-distribution cumulative risks to increase with increasing mortality rate. We suggest researchers consider the cohort's baseline mortality risk when deciding whether real-world data with incomplete death information can be used without concern.

Characterizing Fit-for-Purpose Real-World Data: An Assessment of a Mother-Infant Linkage in the Japan Medical Data Center Claims Database.

Purpose: Observational postapproval safety studies are needed to inform medication safety during pregnancy. Real-world databases can be valuable for supporting such research, but fitness for regulatory purpose must first be vetted. Here, we demonstrate a fit-for-purpose assessment of the Japan Medical Data Center (JMDC) claims database for pregnancy safety regulatory decision-making. Patients and Methods: The Duke-Margolis framework considers a database's fitness for regulatory purpose based on relevancy (capacity to answer the research question based on variable availability and a sufficiently sized, representative population) and quality (ability to validly answer the research question based on data completeness and accuracy). To assess these considerations, we examined descriptive characteristics of infants and pregnancies among females ages 12-55 years in the JMDC between January 2005 and March 2022. Results: For relevancy, we determined that critical data fields (maternal medications, infant major congenital malformations, covariates) are available. Family identification codes permitted linkage of 385,295 total mother-infant pairs, 57% of which were continuously enrolled during pregnancy. The prevalence of specific congenital malformation subcategories and maternal medical conditions were representative of the general population, but preterm births were below expectations (3.6% versus 5.6%) in this population. For quality, our methods are expected to accurately identify the complete set of mothers and infants with a shared health insurance plan. However, validity of gestational age information was limited given the high proportion (60%) of missing live birth delivery codes coupled with suppression of infant birth dates and inaccessibility of disease codes with gestational week information. Conclusion: The JMDC may be well suited for descriptive studies of pregnant people in Japan (eg, comorbidities, medication usage). More work is needed to identify a method to assign pregnancy onset and delivery dates so that in utero medication exposure windows can be defined more precisely as needed for many regulatory postapproval pregnancy safety studies.

Estimated Familial Amyotrophic Lateral Sclerosis Proportion: A Literature Review and Meta-analysis.

Background and Objectives: Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disorder. Familial (fALS) cases are usually reported to constitute 5%-10% of all ALS cases; however, no recent literature review or meta-analysis of this proportion (referred to throughout as "proportion fALS") has been conducted. Our objective was to estimate the proportion fALS by geographic region and to assess the effect of study characteristics on the estimates. Methods: A comprehensive literature review was performed to identify all original studies reporting the number of fALS cases in an ALS cohort. The results were stratified by geographic region, study design (case series or population-based), and decade of study publication. Subgroup analyses were conducted according to family history criteria used to define fALS. We report pooled estimates of the proportion fALS from random-effects meta-analyses when >2 studies are available and I2 is < 90%; weighted averages and ranges are otherwise presented. Results: The overall pooled proportion fALS based on a total 165 studies was 8% (0%, 71%). The proportion fALS was 9% (0%, 71%) among 107 case series and 5% (4%, 6%) among 58 population-based studies. Among population-based studies, proportion fALS by geographic region was 6% (5%, 7%; N = 37) for Europe, 5% (3%, 7%; N = 5) for Latin America, and 5% (4%, 7%; N = 12) for North America. Criteria used to define fALS were reported by 21 population-based studies (36%), and proportion fALS was 5% (4%, 5%; N = 9) for first-degree relative, 7% (4%, 11%; N = 4) for first or second-degree relative, and 11% (N = 1) for more distant ALS family history. Population-based studies published in the 2000s or earlier generated a lower pooled proportion fALS than studies published in the 2010s or later. Discussion: The results suggest that variability in the reported proportion fALS in the literature may be, in part, due to the differences in geography, study design, fALS definition, and decade of case ascertainment. Few studies outside of European ancestral populations were available. The proportion fALS was marginally higher among case series compared with population-based studies, likely because of referral bias. Criteria used to define fALS were largely unreported. Consensus criteria for fALS and additional population-based studies in non-European ancestral populations are needed.

Epidemiology beyond its limits.

In 1995, journalist Gary Taubes published an article in Science titled "Epidemiology faces its limits," which questioned the utility of nonrandomized epidemiologic research and has since been cited more than 1000 times. He highlighted numerous examples of research topics he viewed as having questionable merit. Studies have since accumulated for these associations. We systematically evaluated current evidence of 53 example associations discussed in the article. Approximately one-quarter of those presented as doubtful are now widely viewed as causal based on current evaluations of the public health consensus. They include associations between alcohol consumption and breast cancer, residential radon exposure and lung cancer, and the use of tanning devices and melanoma. This history should inform current debates about the reproducibility of epidemiologic research results.

Assessing Techniques for Quantifying the Impact of Bias Due to an Unmeasured Confounder: An Applied Example.

PURPOSE: To compare the magnitude of bias due to unmeasured confounding estimated from various techniques in an applied example. PATIENTS AND METHODS: We examined the association between dibutyl phthalate (DBP) and incident estrogen receptor (ER)-positive breast cancer in a Danish nationwide cohort (N=1,122,042). Cox regression analyses were adjusted for age and active drug compounds contributing to DBP exposure. We estimated the hazard ratios (HRs) that would have been observed had one of the DBP sources been unmeasured and calculated the strength of confounding by comparing to the fully adjusted HR. We performed a quantitative bias analysis (QBA) of the "unmeasured" confounder, using external information to specify the bias parameters. Upper bounds on the bias were estimated and E-values were calculated. RESULTS: The adjusted HR for incident ER-positive breast cancer among women with high-level (≥10,000 cumulative milligrams) versus no DBP exposure was 2.12 (95% confidence interval 1.12 to 4.05). Removing each DBP source in isolation resulted in negligible change in the HR. The bias estimates from the QBA ranged from 1.00 to 1.01. The estimated maximum impact of unmeasured confounding ranged from 1.01 to 1.51. E-values ranged from 3.46 to 3.68. CONCLUSION: The impact of bias due to simulated unmeasured confounding was negligible, in part, because the unmeasured variable was not independent of controlled variables. When a suspected confounder cannot be measured in the study data, our exercise suggests that QBA is the most informative method for assessing the impact. E-values may best be reserved for situations where uncontrolled confounding emanates from an unknown confounder.

Risk Factors for Heart Failure with Preserved or Reduced Ejection Fraction Among Medicare Beneficiaries: Application of Competing Risks Analysis and Gradient Boosted Model.

BACKGROUND: The differential impact of various demographic characteristics and comorbid conditions on development of heart failure (HF) with preserved (pEF) and reduced ejection fraction (rEF) is not well studied among the elderly. METHODS: Using Medicare claims data linked to electronic health records, we conducted an observational cohort study of individuals ≥65 years of age without HF. A Cox proportional hazards model accounting for competing risk of HFrEF and HFpEF incidence was constructed. A gradient-boosted model (GBM) assessed the relative influence (RI) of each predictor in the development of HFrEF and HFpEF. RESULTS: Among 138,388 included individuals, 9701 developed HF (incidence rate = 20.9 per 1000 person-years). Males were more likely to develop HFrEF than HFpEF (HR = 2.07, 95% CI: 1.81-2.37 vs. 1.11, 95% CI: 1.02-1.20, P for heterogeneity <0.01). Atrial fibrillation and pulmonary hypertension had stronger associations with the risk of HFpEF (HR = 2.02, 95% CI: 1.80-2.26 and 1.66, 95% CI: 1.23-2.22) while cardiomyopathy and myocardial infarction were more strongly associated with HFrEF (HR = 4.37, 95% CI: 3.21-5.97 and 1.94, 95% CI: 1.23-3.07). Age was the strongest predictor across all HF subtypes with RI from GBM >35%. Atrial fibrillation was the most influential comorbidity for the development of HFpEF (RI = 8.4%) while cardiomyopathy was the most influential comorbidity for the development of HFrEF (RI = 20.7%). CONCLUSION: These findings of heterogeneous relationships between several important risk factors and heart failure types underline the potential differences in the etiology of HFpEF and HFrEF.

Risk of Uveitis in Patients With Inflammatory Bowel Disease on Immunosuppressive Drug Therapy.

Background: Inflammatory bowel disease (IBD) patients may develop anterior uveitis. Methods: An observational cohort of IBD patients followed new users of (1) tumor necrosis factor inhibitor versus nonbiologic agents or (2) adalimumab versus infliximab until occurrence of anterior uveitis or treatment change/discontinuation. Cox-proportional hazards models estimated hazard ratios in propensity score-matched cohorts of Crohn disease or ulcerative colitis patients. Results: No statistically significant differences in the risk of uveitis were observed between initiators of nonbiologics and tumor necrosis factor inhibitor. Effect estimates for adalimumab versus infliximab were highly imprecise due to limited outcomes. Conclusions: Uveitis risk was not different between IBD patients treated with immunosuppressives.

Development and Preliminary Validation of a Medicare Claims-Based Model to Predict Left Ventricular Ejection Fraction Class in Patients With Heart Failure.

BACKGROUND: Ejection fraction (EF) class is an important predictor of treatment response in heart failure (HF); however, administrative claims databases lack information on EF, limiting their usefulness in clinical and health services research of HF. METHODS AND RESULTS: We linked Medicare claims data to electronic medical records containing EF measurements for a cohort of 11 073 patients with HF from 2 academic medical centers. A a claims-based model predicting EF class was constructed using data from center 1 ("training sample") and validated using data from center 2 ("testing sample). Linear and logistic regression models with least absolute square shrinkage operator and Bayesian information criteria were developed to select the relevant predictor variables out of the total 57 candidate variables in the training sample. Higher accuracy was noted in the testing sample with models classifying patients into 2 EF classes (reduced EF <0.45) versus preserved EF (≥0.45) when compared with classifying patients into 3 EF classes (reduced, <0.40, moderately reduced, 0.40-0.49, or preserved, ≥0.50). In the testing sample, the most efficient model had 35 predictors and resulted in 83% of patients being correctly classified (95% CI, 82%-84%). The model had positive predictive value of 0.73 (95% CI, 0.68-0.78) and 0.84 (95% CI, 0.83-0.86) and sensitivity of 0.29 (95% CI, 0.25-0.32) and 0.97 (95% CI, 0.97-0.98) for reduced and preserved EF, respectively. In addition to HF-specific diagnosis codes, other factors including age, sex, medication use, and comorbidities, such as myocardial infarction and valve disorders, were important discriminators between EF classes. CONCLUSIONS: The claims-based model developed in this study may be used to identify patient subgroups with specific EF class in studies evaluating the health outcomes, utilization patterns, and cost, of HF patients in routine care when EF measurements are not available.

Effect of a Remotely Delivered Tailored Multicomponent Approach to Enhance Medication Taking for Patients With Hyperlipidemia, Hypertension, and Diabetes: The STIC2IT Cluster Randomized Clinical Trial.

Importance: Approximately half of patients with chronic conditions are nonadherent to prescribed medications, and interventions have been only modestly effective. Objective: To evaluate the effect of a remotely delivered multicomponent behaviorally tailored intervention on adherence to medications for hyperlipidemia, hypertension, and diabetes. Design, Setting, and Participants: Two-arm pragmatic cluster randomized controlled trial at a multispecialty group practice including participants 18 to 85 years old with suboptimal hyperlipidemia, hypertension, or diabetes disease control, and who were nonadherent to prescribed medications for these conditions. Interventions: Usual care or a multicomponent intervention using telephone-delivered behavioral interviewing by trained clinical pharmacists, text messaging, pillboxes, and mailed progress reports. The intervention was tailored to individual barriers and level of activation. Main Outcomes and Measures: The primary outcome was medication adherence from pharmacy claims data. Secondary outcomes were disease control based on achieved levels of low-density lipoprotein cholesterol, systolic blood pressure, and hemoglobin A1c from electronic health records, and health care resource use from claims data. Outcomes were evaluated using intention-to-treat principles and multiple imputation for missing values. Results: Fourteen practice sites with 4078 participants had a mean (SD) age of 59.8 (11.6) years; 45.1% were female. Seven sites were each randomized to intervention or usual care. The intervention resulted in a 4.7% (95% CI, 3.0%-6.4%) improvement in adherence vs usual care but no difference in the odds of achieving good disease control for at least 1 (odds ratio [OR], 1.10; 95% CI, 0.94-1.28) or all eligible conditions (OR, 1.05; 95% CI, 0.91-1.22), hospitalization (OR, 1.02; 95% CI, 0.78-1.34), or having a physician office visit (OR, 1.11; 95% CI, 0.91-1.36). However, intervention participants were significantly less likely to have an emergency department visit (OR, 0.62; 95% CI, 0.45-0.85). In as-treated analyses, the intervention was associated with a 10.4% (95% CI, 8.2%-12.5%) increase in adherence, a significant increase in patients achieving disease control for at least 1 eligible condition (OR, 1.24; 95% CI, 1.03-1.50), and nonsignificantly improved disease control for all eligible conditions (OR, 1.18; 95% CI, 0.99-1.41). Conclusions and Relevance: A remotely delivered multicomponent behaviorally tailored intervention resulted in a statistically significant increase in medication adherence but did not change clinical outcomes. Future work should focus on identifying which groups derive the most clinical benefit from adherence improvement efforts. Trial Registration: ClinicalTrials.gov identifier: NCT02512276.

Time to Filling of New Prescriptions for Chronic Disease Medications Among a Cohort of Elderly Patients in the USA.

BACKGROUND: Data on primary nonadherence remains sparse, due to a lack of data resources that combine information on medication prescribing and dispensing. In addition, previous work on primary nonadherence has used follow-up periods ranging from 30 days up to 18 months, making results difficult to compare. OBJECTIVE: To evaluate the prevalence and predictors of primary nonadherence by measuring time until filling in a cohort of elderly patients. DESIGN: Retrospective cohort study of new prescription episodes. PATIENTS: Data comes from a linked database of electronic health records and claims for patients aged ≥ 65 years enrolled in Medicare Parts A, B, and D during 2007-2014. We identified patients receiving a new prescription for a chronic disease medication with continuous Medicare enrollment for 180 days prior to the index prescription order and no fills or orders for the medication during this period. MAIN MEASURES: Time until filling of the index prescription for up to 1 year. KEY RESULTS: In 32,586 new medication orders, the majority (75%; 95% confidence interval [CI] 74-75%) of new prescriptions were filled within 7 days, 81% (81-82%) were filled within 30 days, and 91% (91-92%) were filled within 1 year. The rate and timing of dispensing were similar across therapeutic areas. Timing of initial filling within 7 days or within 30 days could be predicted with moderate accuracy (C-statistics = 0.70-0.74). Patients with > 5 current medications on hand at the time of the index prescription and average out-of-pocket medication costs < $5 filled 89% of prescriptions within 7 days. Patients with no current medications and out-of-pocket costs > $50 filled only 25% of prescriptions within 7 days. CONCLUSIONS: Nearly 20% of patients do not fill a new chronic disease prescription within 30 days. Patients with fewer recent fills and higher out-of-pocket costs are at higher risk of primary nonadherence.

Association of Osteoporosis Medication Use After Hip Fracture With Prevention of Subsequent Nonvertebral Fractures: An Instrumental Variable Analysis.

Importance: Osteoporosis medication treatment is recommended after hip fracture, yet contemporary estimates of rates of initiation and clinical benefit in the patient population receiving routine care are not well documented. Objectives: To report osteoporosis treatment initiation rates between January 1, 2004, and September 30, 2015, and to estimate the risk reduction in subsequent nonvertebral fractures associated with treatment initiation in patients with hip fracture. Design, Setting, and Participants: In this cohort study, data from a commercial insurance claims database from the United States were analyzed. Patients 50 years and older who had a hip fracture and were not receiving treatment with osteoporosis medications before their fracture were included. Exposure: Prescription dispensing of an osteoporosis medication within 180 days of a hip fracture hospitalization. Main Outcomes and Measures: Each initiation episode was matched with 10 nonuse episodes on person-time after the index hip fracture event to preclude immortal time bias and followed up for the outcome of nonvertebral fracture until change in exposure or a censoring event. An instrumental variable analysis using 2-stage residual inclusion method was conducted using calendar year, specialist access, geographical variation in prescribing patterns, and hospital preference. Results: Among 97 169 patients with a hip fracture identified, the mean (SD) age was 80.2 (10.8) years, and 64 164 (66.0%) were women. A continuous decline over the study years was observed in osteoporosis medication initiation rates from 9.8% (95% CI, 9.0%-10.6%) in 2004 to 3.3% (95% CI, 2.9%-3.8%) in 2015. In the effectiveness analyses, the hospital preference instrumental variable had a stronger association with treatment (pseudo R2 = 0.20) than the other 3 instrumental variables (specialist access: pseudo R2 = 0.04; calendar year: pseudo R2 = 0.05; and geographic variation: pseudo R2 = 0.07). Instrumental variable analysis with hospital preference suggested a rate difference of 4.2 events (95% CI, 1.1-7.3) per 100 person-years in subsequent fractures associated with osteoporosis treatment initiation compared with nonuse in an additive hazard model. Conclusions and Relevance: Low rates of osteoporosis treatment initiation after a hip fracture in recent years were observed. Clinically meaningful reduction in subsequent nonvertebral fracture rates associated with treatment suggests that improving prescriber adherence to guidelines and patient adherence to prescribed regimens may result in notable public health benefit.