Objectives: Predictive algorithms to inform risk management decisions are needed for patients with COVID-19, although the traditional risk scores have not been adequately assessed in Asian patients. We aimed to evaluate the performance of a COVID-19-specific prediction model, the 4C (Coronavirus Clinical Characterisation Consortium) Mortality Score, along with other conventional critical care risk models in Japanese nationwide registry data.Design: Retrospective cohort study.Setting and participants: Hospitalised patients with COVID-19 and cardiovascular disease or coronary risk factors from January to May 2020 in 49 hospitals in Japan.Main outcome measures: Two different types of outcomes, in-hospital mortality and a composite outcome, defined as the need for invasive mechanical ventilation and mortality.Results: The risk scores for 693 patients were tested by predicting in-hospital mortality for all patients and composite endpoint among those not intubated at baseline (n=659). The number of events was 108 (15.6%) for mortality and 178 (27.0%) for composite endpoints. After missing values were multiply imputed, the performance of the 4C Mortality Score was assessed and compared with three prediction models that have shown good discriminatory ability (RISE UP score, A-DROP score and the Rapid Emergency Medicine Score (REMS)). The area under the receiver operating characteristic curve (AUC) for the 4C Mortality Score was 0.84 (95% CI 0.80 to 0.88) for in-hospital mortality and 0.78 (95% CI 0.74 to 0.81) for the composite endpoint. It showed greater discriminatory ability compared with other scores, except for the RISE UP score, for predicting in-hospital mortality (AUC: 0.82, 95% CI 0.78 to 0.86). Similarly, the 4C Mortality Score showed a positive net reclassification improvement index over the A-DROP and REMS for mortality and over all three scores for the composite endpoint. The 4C Mortality Score model showed good calibration, regardless of outcome.Conclusions: The 4C Mortality Score performed well in an independent external COVID-19 cohort and may enable appropriate disposition of patients and allocation of medical resources.Trial registration numberUMIN000040598.

Background: Atrial fibrillation (AF) ablation targets improvement in quality of life (QoL). Data is scarce on predictors of QoL improvement following ablation. We aimed to investigate the clinical characteristics underlying differential response in QoL after AF ablation (with or without arrhythmia recurrence). Methods: All patients undergoing AF ablation (2013–2016) at our center were prospectively enrolled in a fully automated patient-reported outcomes registry. A large number of variables were collected including AF symptom severity scale and AF burden (as indicated by AF frequency and duration scores). Patients were divided into 3 groups based on self-report of QoL improvement: remarkable (super responders), mild/moderate, and unchanged or worse (nonresponders). Univariable and multivariable logistic regression models assessed clinical characteristics and QoL outcomes. Results: A total of 956 patients were included (25% females, mean age 63.9). Most patients (≈80%) were super responders (n=761), 138 (14.4%) had mild/moderate improvement, and 57 (5.9%) were nonresponders. The strongest predictors of remarkable QoL improvement were freedom of arrhythmia recurrence (OR, 2.42 [95% CI, 1.27–4.59], P =0.007) and lower AF burden at 12 months. Similarly, higher AF burden was significantly associated with clinical nonresponse. In patients with observed clinical recurrence-QoL mismatch, changes in AF burden at 12 months were the main predictors of QoL outcome, with lower burden predicting higher improvement in QoL and vice versa. Conclusions: Most patients derive significant QoL benefit from AF ablation. Freedom from arrhythmia recurrence and lower AF burden predict remarkable QoL improvement following ablation.