Published papers

Background: Although extensive substrate modification in addition to pulmonary vein isolation (PVI) has been recommended in catheter ablation for persistent atrial fibrillation (AF), recent randomized controlled trials have not demonstrated efficacy of such additional ablations. Methods and study design: The Osaka Cardiovascular Conference will conduct a multicenter, randomized, open-label trial aiming to examine whether PVI alone is non-inferior to PVI plus additional ablation such as linear ablation and/or complex fractionated atrial electrogram ablation in patients with persistent AF. The primary outcome is recurrence of AF documented by scheduled or symptom-driven electrocardiogram tests during a 1-year follow-up period after the index ablation. The key secondary endpoints include all-cause death, occurrence of symptomatic stroke, complications related to the procedure, and quality of life assessment using the 36-item Short-Form Health Survey. The clinical impact of the presence or absence of AF trigger foci, and their origins in cases with them, on the results of catheter ablation will also be investigated as an exploratory endpoint. A total of 512 patients will be enrolled and followed up to 1 year. Conclusions: The EARNEST-PVI trial is a randomized controlled trial designed to assess whether PVI alone is non-inferior to extended substrate ablation for patients with persistent AF undergoing a first catheter ablation.

AIMS: Previous studies could not demonstrate any benefit of more intensive ablation in addition to pulmonary vein isolation (PVI) including complex fractionated atrial electrogram (CFAE) and linear ablation for recurrence in the initial catheter ablation of persistent atrial fibrillation (AF). This study aimed to establish the non-inferiority of PVI alone to PVI plus these additional ablation strategies. METHODS AND RESULTS: Patients with persistent AF who underwent an initial catheter ablation (n = 512, long-standing persistent AF; 128 cases) were randomly assigned in a 1:1 ratio to either PVI alone (PVI-alone group) or PVI plus CFAE and/or linear ablation (PVI-plus group). After excluding 15 cases who did not receive procedures, we analysed 249 and 248 patients, respectively. The primary endpoint was recurrence of AF, atrial flutter, and/or atrial tachycardia, and the non-inferior margin was set at a hazard ratio of 1.43. In the PVI-plus group, 85.1% of patients had linear ablation and 15.3% CFAE ablation. After 12 months, freedom from the primary endpoint occurred in 71.3% of patients in the PVI-alone group and in 78.3% in the PVI-plus group [hazard ratio = 1.56 (95% confidence interval: 1.10-2.24), non-inferior P = 0.3062]. The procedure-related complication rates were 2.0% in the PVI-alone group and 3.6% in the PVI-plus group (P = 0.199). CONCLUSION: This randomized trial did not establish the non-inferiority of PVI alone to PVI plus linear ablation or CFAE ablation in patients with persistent AF, but implied that the PVI plus strategy was promising to improve the clinical efficacy (NCT03514693).

When models are built with missing data, an information criterion is needed to select the best model among the various candidates. Using a conventional information criterion for missing data may lead to the selection of the wrong model when data are not missing at random. Conventional information criteria implicitly assume that any subset of missing-at-random data is also missing at random, and thus the maximum likelihood estimator is assumed to be consistent; that is, it is assumed that the estimator will converge to the true value. However, this assumption may not be practical. In this paper, we develop an information criterion that works even for not-missing-at-random data, so long as the largest missing data set is missing at random. Simulations are performed to show the superiority of the proposed information criterion over conventional criteria.

Survival analysis is a set of methods for statistical inference concerning the time until the occurrence of an event. One of the main objectives of survival analysis is to evaluate the effects of different covariates on event time. Although the proportional hazards model is widely used in survival analysis, it assumes that the ratio of the hazard functions is constant over time. This assumption is likely to be violated in practice, leading to erroneous inferences and inappropriate conclusions. The accelerated failure time model is an alternative to the proportional hazards model that does not require such a strong assumption. Moreover, it is sometimes plausible to consider the existence of cured patients or long-term survivors. The survival regression models in such contexts are referred to as cure models. In this study, we consider the accelerated failure time cure model with frailty for uncured patients. Frailty is a latent random variable representing patients’ characteristics that cannot be described by observed covariates. This enables us to flexibly account for individual heterogeneities. Our proposed model assumes a shifted gamma distribution for frailty to represent uncured patients’ heterogeneities. We construct an estimation algorithm for the proposed model, and evaluate its performance via numerical simulations. Furthermore, as an application of the proposed model, we use a real dataset, Specific Health Checkups, concerning the onset of hypertension. Results from a model comparison suggest that the proposed model is superior to existing alternatives.

Evaluating classifications is crucial in statistics and machine learning, as it influences decision-making across various fields, such as patient prognosis and therapy in critical conditions. The Matthews correlation coefficient (MCC), also known as the phi coefficient, is recognized as a performance metric with high reliability, offering a balanced measurement even in the presence of class imbalances. Despite its importance, there remains a notable lack of comprehensive research on the statistical inference of MCC. This deficiency often leads to studies merely validating and comparing MCC point estimates—a practice that, while common, overlooks the statistical significance and reliability of results. Addressing this research gap, our paper introduces and evaluates several methods to construct asymptotic confidence intervals for the single MCC and the differences between MCCs in paired designs. Through simulations across various scenarios, we evaluate the finite-sample behavior of these methods and compare their performances. Furthermore, through real data analysis, we illustrate the potential utility of our findings in comparing binary classifiers, highlighting the possible contributions of our research in this field.

Evaluating the relationship between a response variable and explanatory variables is important to establish better statistical models. Concordance probability is one measure of this relationship and is often used in biomedical research. Concordance probability can be seen as an extension of the area under the receiver operating characteristic curve. In this study, we propose estimators of concordance probability for time-to-event data subject to double censoring. A doubly censored time-to-event response is observed when either left or right censoring may occur. In the presence of double censoring, existing estimators of concordance probability lack desirable properties such as consistency and asymptotic normality. The proposed estimators consist of estimators of the left-censoring and the right-censoring distributions as a weight for each pair of cases, and reduce to the existing estimators in special cases. We show the statistical properties of the proposed estimators and evaluate their performance via numerical experiments.