Comparative effectiveness of dimethyl fumarate in multiple sclerosis.

AIMS: To assess the effectiveness of dimethyl fumarate (DMF) on annual rate of relapse subject to treatment (ARRt) and disability progression in multiple sclerosis (MS) compared to injectable immunomodulators (IMM), teriflunomide (TERI) and fingolimob (FTY), in real-life setting. METHODS: A population-based cohort study was conducted using data of the French nationwide claims database, SNDS. All patients initiating IMM, TERI, FTY or DMF between 1 July 2015 and 12 December 2017, with 4.5 years of database history and 1-3.5 years of follow-up were included in this study. DMF patients were 1:1 matched to IMM, TERI or FTY using a high dimensional propensity score. Negative binomial regression and a logistic regression model were used to estimate the relative risk (RR ± [95% CI]) of ARRt and the odds ratio (OR ± [95% CI]) of disability progression, respectively. RESULTS: Overall, 9304 subjects were identified: 29.0% initiated DMF, 33.2% TERI, 5.6% FTY and 32.2% an IMM. The matched cohorts consisted of 1779 DMF-IMM patients, 1679 DMF-TERI patients, and 376 DMF-FTY patients. DMF significantly reduced ARRt compared to IMM (RR 0.72 [0.61-0.86]) and TERI (0.81 [0.68-0.96]) and did not show any significant difference when compared with FTY. The risk of the progression of MS-specific disability was not significantly different for any matched cohorts. CONCLUSION: DMF is associated with lower risk of treated relapse for patients with RRMS than other first-line RRMS agents (TERI and IIM).

Intra-database validation of case-identifying algorithms using reconstituted electronic health records from healthcare claims data.

BACKGROUND: Diagnosis performances of case-identifying algorithms developed in healthcare database are usually assessed by comparing identified cases with an external data source. When this is not feasible, intra-database validation can present an appropriate alternative. OBJECTIVES: To illustrate through two practical examples how to perform intra-database validations of case-identifying algorithms using reconstituted Electronic Health Records (rEHRs). METHODS: Patients with 1) multiple sclerosis (MS) relapses and 2) metastatic castration-resistant prostate cancer (mCRPC) were identified in the French nationwide healthcare database (SNDS) using two case-identifying algorithms. A validation study was then conducted to estimate diagnostic performances of these algorithms through the calculation of their positive predictive value (PPV) and negative predictive value (NPV). To that end, anonymized rEHRs were generated based on the overall information captured in the SNDS over time (e.g. procedure, hospital stays, drug dispensing, medical visits) for a random selection of patients identified as cases or non-cases according to the predefined algorithms. For each disease, an independent validation committee reviewed the rEHRs of 100 cases and 100 non-cases in order to adjudicate on the status of the selected patients (true case/ true non-case), blinded with respect to the result of the corresponding algorithm. RESULTS: Algorithm for relapses identification in MS showed a 95% PPV and 100% NPV. Algorithm for mCRPC identification showed a 97% PPV and 99% NPV. CONCLUSION: The use of rEHRs to conduct an intra-database validation appears to be a valuable tool to estimate the performances of a case-identifying algorithm and assess its validity, in the absence of alternative.