宮田 雅美

OBJECTIVES: To develop a comprehensive machine learning model incorporating various clinical factors, including frailty and comorbidities, to predict 30-day readmission and mortality risk in patients with chronic obstructive pulmonary disease (COPD). METHODS: This retrospective cohort study used electronic health records (EHR) from Fujita Health University Hospital (2004-2019) for 1294 patients with COPD and 3499 hospitalization or death events. The EHR contained longitudinal patient data (demographics, diagnoses, test results, clinical records). We developed two eXtreme Gradient Boosting models, the comprehensive Top64 and practical 11-feature models. We compared these with the Comorbidity, Obstruction, Dyspnea, and Previous Exacerbations index (CODEX) model, a widely used tool for predicting hospital readmission or death in patients with COPD. The area under the receiver operating characteristic curve (AUC) with 95% confidence interval (CI), sensitivity, and specificity were used to evaluate the model performance. RESULTS: The Top64 (AUC: 0.769, 95% CI: 0.747-0.791) and practical 11-feature (AUC: 0.746, 95% CI: 0.730-0.762) models performed better than the CODEX model (AUC: 0.587, 95% CI: 0.563-0.611). The Top64 model showed 0.978 sensitivity and 0.341 specificity, and the practical 11-feature model achieved 0.955 sensitivity and 0.361 specificity. The calibration curves showed good agreement between the observed and predicted results for both models. CONCLUSIONS: A machine learning approach based on clinical data readily available from the EHR performed better than existing models in predicting 30-day readmission and mortality risks in patients with COPD. A comprehensive risk prediction tool may enhance individualized care strategies and improve patient outcomes in COPD management.

BACKGROUND: Although several guidelines provide dosing recommendations for antidepressants based on patients' genetic information, pharmacogenetic testing for antidepressant use is rarely routinely performed in Japan. To clarify the clinical impact of pharmacogenetic testing, this study estimated the potential drug-gene interactions for first-time antidepressant treatment in Japanese patients with major depressive disorder. METHODS: This study retrospectively included Japanese patients who were registered for depressive episodes (F32, International Classification of Diseases, Tenth Revision) and initiated on antidepressants between July 2022 and March 2023. Antidepressant prescription rates were calculated using a nationwide hospital-based database (Medical Data Vision Co., Ltd). The incidence of actionable drug-gene interactions was estimated by multiplying the first-time prescription rate of each relevant antidepressant by the frequency of its corresponding actionable phenotype. RESULTS: A total of 3,197 patients were included in the analysis. Escitalopram was the most frequently prescribed antidepressant (18.7%, n = 597), followed by mirtazapine (17.5%, n = 561), and sertraline (15.4%, n = 493). Of the patients receiving their first treatment of major depressive disorder, 56.5% (n = 1,807) were prescribed a drug with actionable pharmacogenetic implications, and 26.4% (n = 844) were estimated to have required actionable therapeutic recommendations. The highest incidence of actionable drug-gene interactions was observed in escitalopram and CYP2C19 pairs (12.4%, n = 398). For sertraline and CYP2C19 or CYP2B6 pairs, the incidence was 11.0% (n = 352). Among all antidepressants, paroxetine had the highest incidence of actionable drug-gene interactions related to CYP2D6 at 1.8% (n = 56); this interaction was rarely observed with other antidepressants (<1%). CONCLUSIONS: We estimated that one in four Japanese patients with major depressive disorder who were prescribed first-time antidepressants had actionable drug-gene interactions. These results suggest that pre-emptive pharmacogenetic testing in the treatment of major depressive disorder could have important clinical implications.

Favipiravir is an oral broad-spectrum inhibitor of viral RNA-dependent RNA polymerase that is approved for treatment of influenza in Japan. We conducted a prospective, randomized, open-label, multicenter trial of favipiravir for the treatment of COVID-19 at 25 hospitals across Japan. Eligible patients were adolescents and adults admitted with COVID-19 who were asymptomatic or mildly ill and had an Eastern Cooperative Oncology Group (ECOG) performance status of 0 or 1. Patients were randomly assigned at a 1:1 ratio to early or late favipiravir therapy (in the latter case, the same regimen starting on day 6 instead of day 1). The primary endpoint was viral clearance by day 6. The secondary endpoint was change in viral load by day 6. Exploratory endpoints included time to defervescence and resolution of symptoms. Eighty-nine patients were enrolled, of whom 69 were virologically evaluable. Viral clearance occurred within 6 days in 66.7% and 56.1% of the early and late treatment groups (adjusted hazard ratio [aHR], 1.42; 95% confidence interval [95% CI], 0.76 to 2.62). Of 30 patients who had a fever (≥37.5°C) on day 1, times to defervescence were 2.1 days and 3.2 days in the early and late treatment groups (aHR, 1.88; 95% CI, 0.81 to 4.35). During therapy, 84.1% developed transient hyperuricemia. Favipiravir did not significantly improve viral clearance as measured by reverse transcription-PCR (RT-PCR) by day 6 but was associated with numerical reduction in time to defervescence. Neither disease progression nor death occurred in any of the patients in either treatment group during the 28-day participation. (This study has been registered with the Japan Registry of Clinical Trials under number jRCTs041190120.).