Erenumab in chronic migraine: Patient-reported outcomes in a randomized double-blind study.

OBJECTIVE: To determine the effect of erenumab, a human monoclonal antibody targeting the calcitonin gene-related peptide receptor, on health-related quality of life (HRQoL), headache impact, and disability in patients with chronic migraine (CM). METHODS: In this double-blind, placebo-controlled study, 667 adults with CM were randomized (3:2:2) to placebo or erenumab (70 or 140 mg monthly). Exploratory endpoints included migraine-specific HRQoL (Migraine-Specific Quality-of-Life Questionnaire [MSQ]), headache impact (Headache Impact Test-6 [HIT-6]), migraine-related disability (Migraine Disability Assessment [MIDAS] test), and pain interference (Patient-Reported Outcomes Measurement Information System [PROMIS] Pain Interference Scale short form 6b). RESULTS: Improvements were observed for all endpoints in both erenumab groups at month 3, with greater changes relative to placebo observed at month 1 for many outcomes. All 3 MSQ domains were improved from baseline with treatment differences for both doses exceeding minimally important differences established for MSQ-role function-restrictive (≥3.2) and MSQ-emotional functioning (≥7.5) and for MSQ-role function-preventive (≥4.5) for erenumab 140 mg. Changes from baseline in HIT-6 scores at month 3 were -5.6 for both doses vs -3.1 for placebo. MIDAS scores at month 3 improved by -19.4 days for 70 mg and -19.8 days for 140 mg vs -7.5 days for placebo. Individual-level minimally important difference was achieved by larger proportions of erenumab-treated participants than placebo for all MSQ domains and HIT-6. Lower proportions of erenumab-treated participants had MIDAS scores of severe (≥21) or very severe (≥41) or PROMIS scores ≥60 at month 3. CONCLUSIONS: Erenumab-treated patients with CM experienced clinically relevant improvements across a broad range of patient-reported outcomes. CLINICALTRIALSGOV IDENTIFIER: NCT02066415. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that for patients with CM, erenumab treatment improves HRQoL, headache impact, and disability

Methods for Estimating Kidney Disease Stage Transition Probabilities Using Electronic Medical Records

Chronic diseases are often described by stages of severity. Clinical decisions about what to do are influenced by the stage, whether a patient is progressing, and the rate of progression. For chronic kidney disease (CKD), relatively little is known about the transition rates between stages. To address this, we used electronic health records (EHR) data on a large primary care population, which should have the advantage of having both sufficient follow-up time and sample size to reliably estimate transition rates for CKD. However, EHR data have some features that threaten the validity of any analysis. In particular, the timing and frequency of labratory values and clinical measurements are not determined a priori by research investigators, but rather, depend on many factors, including the current health of the patient. We developed an approach for estimatating CKD stage transition rates using hidden Markov models (HMMs), when the level of information and observation time vary among individuals. To estimate the HMMs in a computationally manageable way, we used a “discretization” method to transform daily data into intervals of 30 days, 90 days, or 180 days. We assessed the accuracy and computation time of this method via simulation studies. We also used simulations to study the effect of informative observation times on the estimated transition rates. Our simulation results showed good performance of the method, even when missing data are non-ignorable. We applied the methods to EHR data from over 60,000 primary care patients who have chronic kidney disease (stage 2 and above). We estimated transition rates between six underlying disease states. The results were similar for men and women

Electronic Health Records and Population Health Research

Adoption of electronic health records (EHRs) by clinical practices and hospitals in the US has increased substantially since 2009, and offers opportunities for population health researchers to access rich structured and unstructured clinical data on large, diverse, and geographically distributed populations. However, because EHRs are intended for clinical and administrative use, the data must be curated for effective use in research. We describe EHRs, examine their use in population health research, and compare the strengths and limitations of these applications to traditional epidemiologic methods. To date, EHR data have primarily been used to validate prior findings, to study specific diseases and population subgroups, to examine environmental and social factors and stigmatized conditions, to develop and implement predictive models, and to evaluate natural experiments. Although primary data collection may provide more reliable data and better population retention, EHR-based studies are less expensive and require less time to complete. In addition, large patient samples that can be readily identified from EHR data enable researchers to evaluate simultaneously multiple risk factors and/or outcomes while maintaining study power. In addition to current advantages, improved capture of social, behavioral, environmental, and genetic data, and use of natural language processing, clinical biobanks, and personal sensing via smartphone should further enable EHR researchers to understand complex diseases with multifactorial etiologies. Integrating emerging technologies with clinical care could lead to innovative approaches to precision public health, reduce health care spending on individuals, and directly improve population health