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This paper describes a case of an acute and fatal isoniazid-induced hepatotoxicity and provides a review of the literature. A 65-year-old female diagnosed with latent Mycobacterium tuberculosis infection was receiving oral isoniazid 300 mg daily. She was admitted to the hospital for epigastric and right sided flank pain of one-week duration. Laboratory results and imaging confirmed hepatitis. After ruling out all other possible causes, she was diagnosed with isoniazid-induced acute hepatitis (probable association by the Naranjo scale). After discharge, the patient was readmitted and suffered from severe coagulopathy, metabolic acidosis, acute kidney injury, hepatic encephalopathy, and cardiorespiratory arrest necessitating two rounds of cardiopulmonary resuscitation. Despite maximal hemodynamic support, the patient did not survive. A review of the literature, from several European countries and the United States of America, revealed a low incidence of mortality due to isoniazid-induced hepatotoxicity when used as a single agent for latent Mycobacterium tuberculosis infection. As for the management, the first step consists of withdrawing isoniazid and rechallenge is usually discouraged. Few treatment modalities have been proposed; however there is no robust evidence to support any of them. Routine monitoring for hepatotoxicity in patients receiving isoniazid is warranted to prevent morbidity and mortality.

Objective Observational studies can impact patient care but must be robust and reproducible. Nonreproducibility is primarily caused by unclear reporting of design choices and analytic procedures. This study aimed to: (1) assess how the study logic described in an observational study could be interpreted by independent researchers and (2) quantify the impact of interpretations’ variability on patient characteristics. Materials and Methods Nine teams of highly qualified researchers reproduced a cohort from a study by Albogami et al. The teams were provided the clinical codes and access to the tools to create cohort definitions such that the only variable part was their logic choices. We executed teams’ cohort definitions against the database and compared the number of subjects, patient overlap, and patient characteristics. Results On average, the teams’ interpretations fully aligned with the master implementation in 4 out of 10 inclusion criteria with at least 4 deviations per team. Cohorts’ size varied from one-third of the master cohort size to 10 times the cohort size (2159–63 619 subjects compared to 6196 subjects). Median agreement was 9.4% (interquartile range 15.3–16.2%). The teams’ cohorts significantly differed from the master implementation by at least 2 baseline characteristics, and most of the teams differed by at least 5. Conclusions Independent research teams attempting to reproduce the study based on its free-text description alone produce different implementations that vary in the population size and composition. Sharing analytical code supported by a common data model and open-source tools allows reproducing a study unambiguously thereby preserving initial design choices.

Background: Cleveland, Ohio, is home to three major hospital systems serving approximately 80% of the Northeast Ohio population. The Cleveland Clinic, University Hospitals Health System, and MetroHealth are direct competitors for primary and specialty care, and patient overlap between these systems is high. Fragmentation of health data that exist in silos at these health systems produces an overestimation of disease burden due to double and sometimes triple counting of patients. As a result, longitudinal population-based studies across the Cleveland patient population are impeded unless accurate and actionable clinically derived health data sets can be created.Methods: The Cleveland Institute for Computational Biology has developed the De-Duplicate and De-Identify Research Engine (DeDeRE) that, without any exchange of personal health identifiers (PHI) between health systems, will effectively de-duplicate the patients between one or more health entities. Results:The immediate utility of this software for cancer epidemiology is the increased accuracy in measuring cancer burden and the potential to perform longitudinal studies with de-duplicated, de-identified data sets.Conclusions: The DeDeRE software developed and tested here accomplishes its goals without exposing PHIs using a stateof-the-art, trusted privacy preservation network enabled by a hashbased matching algorithm.Impact: This paper will guide the reader through the functions currently developed in DeDeRE and how a healthcare organization (HCO) employing the release version of this technology can begin sharing data with one or more additional HCOs in a collaborative and noncompetitive manner to create a regional population health resource for cancer researchers.See all articles in this CEBP Focus section, "Modernizing Population Science."