Reeves, Ruth

Background: Statins have been associated with a reduced risk of primary liver cancer (PLC), primarily hepatocellular carcinoma (HCC). However, the optimal use for effective protection and whether benefits vary by patient characteristics remain unclear. We evaluated the association between statin use and PLC risk in metabolic-dysfunction-associated steatotic liver disease (MASLD), considering cumulative exposure and potential effect modifiers. Methods: We conducted a retrospective cohort study using the Veteran Affairs electronic health records. Patients with chronically elevated liver enzymes and metabolic dysfunction, without other chronic liver diseases, were identified between 2007 and 2009 and followed through 2019 for incident PLC. Statin exposure was assessed at baseline and during the follow-up, with dose standardization by LDL-lowering potency (simvastatin-equivalent units). Time to PLC was analyzed using Cox models adjusted for covariates, considering potential interactions. Results: Among 329,577 patients (92% male; median age 62 years), 0.82% developed PLC (median follow-up of 9.7 years). Baseline statin use showed a significantly lower PLC risk (adjusted hazard ratio 0.64; 95% CI, 0.57–0.71; p < 0.0001). No significant interaction was observed with age, sex, metabolic syndrome, or cirrhosis. Higher cumulative statin exposure demonstrated a dose-dependent risk reduction, remaining significant at simvastatin-equivalent doses > 15,561 mg annually after accounting for incident cirrhosis. Atorvastatin/rosuvastatin use provided comparable protection, despite different lipophilicity, and demonstrated stronger effects than others. Conclusions: In MASLD, statin therapy was associated with a dose-dependent PLC risk reduction. High-intensity therapy (simvastatin-equivalent > 40 mg daily) conferred substantial protection regardless of age, sex, insulin resistance, or cirrhosis, supporting a potential statin-based PLC chemoprevention in MASLD.

AbstractLarge observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC (Logical Observation Identifiers Names and Codes) codes. COVID-19 TestNorm was developed and evaluated using 568 test names collected from 8 healthcare systems. Our results show that it could achieve an accuracy of 97.4% on an independent test set. COVID-19 TestNorm is available as an open-source package for developers and as an online Web application for end users (https://clamp.uth.edu/covid/loinc.php). We believe that it will be a useful tool to support secondary use of EHRs for research on COVID-19.