To investigate associations between isoniazid for latent tuberculosis and risk of severe hepatitis, affecting patients with rheumatoid arthritis or ankylosing spondylitis whose treatment includes tumor necrosis factor inhibitors. Our self-controlled case series study analyzed Taiwan’s National Health Insurance Database from 2003 to 2015 to identify RA or AS patients, aged ≥ 20 years, receiving TNF inhibitors and a 9-month single isoniazid treatment. The outcome of interest was hospitalization due to severe hepatitis. We defined risk periods by isoniazid exposure (days): 1–28, 29–56, 57–84, 85–168, 169–252, and 253–280. To compare risk of severe hepatitis in exposed and non-exposed periods, we performed conditional Poisson regressions to generate incidence rate ratios (IRR) and 95% confidence intervals, with adjustment of patients’ baseline covariates including age, sex, HBV, HCV and related medication. Of 54,267 RA patients and 137,889 AS patients identified between 2000 and 2015, 11,221 (20.7%) RA and 4,208 (3.1%) AS patients underwent TNFi therapy, with 722 (5%) receiving isoniazid for latent tuberculosis. We identified 31 incident cases (4.3%) of hospitalization due to severe hepatitis. Of these hospitalization events, 5 occurred in the exposed periods, 25 occurred in the INH unexposed periods, and 1 occurred in the pre-exposure period. Compared with non-exposure, the risk of severe hepatitis was higher in exposed periods (incidence rate ratio [IRR]: 5.1, 95% CI: 1.57–16.55), especially 57–84 days (IRR: 17.29, 95% CI: 3.11–96.25) and 85–168 days (IRR:10.55, 95% CI: 1.90–58.51). The INH related fatal hepatotoxicity was not identified in our study. Our findings suggest an association between risk of severe hepatitis and exposure to isoniazid in patients with RA or AS under TNFi therapy, particularly within the exposed period 57–168 days. A close monitoring of liver function is mandatory to minimize the risk, especially within the first 6 months after initiation of 9 months isoniazid.
Purpose Development and evaluation of a drug-safety signal detection system integrating data-mining tools in longitudinal data is essential. This study aimed to construct a new triage system using longitudinal data for drug-safety signal detection, integrating data-mining tools, and evaluate adaptability of such system. Patients and Methods Based on relevant guidelines and structural frameworks in Taiwan’s pharmacovigilance system, we constructed a triage system integrating sequence symmetry analysis (SSA) and tree-based scan statistics (TreeScan) as data-mining tools for detecting safety signals. We conducted an exploratory analysis utilizing Taiwan’s National Health Insurance Database and selecting two drug classes (sodium-glucose co-transporter-2 inhibitors (SGLT2i) and non-fluorinated quinolones (NFQ)) as chronic and episodic treatment respectively, as examples to test feasibility of the system. Results Under the proposed system, either cohort-based or self-controlled mining with SSA and TreeScan was selected, based on whether the screened drug had an appropriate comparator. All detected alerts were further classified as known adverse drug reactions (ADRs), events related to other causes or potential signals from the triage algorithm, building on existing drug labels and clinical judgement. Exploratory analysis revealed greater numbers of signals for NFQ with a relatively low proportion of known ADRs; most were related to indication, patient characteristics or bias. No safety signals were found. By contrast, most SGLT2i signals were known ADRs or events related to patient characteristics. Four were potential signals warranting further investigation. Conclusion The proposed system facilitated active and systematic screening to detect and classify potential safety signals. Countries with real-world longitudinal data could adopt it to streamline drug-safety surveillance.
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