Few recent large-scale studies have evaluated the risks and benefits of continuing oral anticoagulant (OAC) therapy after catheter ablation (CA) for atrial fibrillation (AF). This study evaluated the status of continuation of OAC therapy and the association between continuation of OAC therapy and thromboembolic and bleeding events according to the CHADS₂ score. This retrospective study included data from the Japanese nationwide administrative claims database of patients who underwent CA for AF between April 2014 and March 2021. Patients without AF recurrence assessed by administrative data of the treatment modalities were divided into two groups according to continuation of OAC therapy 6 months after the index CA. The primary outcomes were thromboembolism and major bleeding after a landmark period of 6 months. After inverse probability of treatment weighting analysis, the association between OAC continuation and outcomes was determined according to the CHADS₂ score. Among 231 374 patients included, 69.7%, 21.6%, and 8.7% had CHADS₂ scores of ≤1, 2, and ≥3, respectively. Of these, 71% continued OAC therapy at 6 months. The OAC continuation rate was higher in the high CHADS₂ score group than that in the low CHADS₂ score group. Among all patients, 2451 patients (0.55 per 100 person-years) had thromboembolism and 2367 (0.53 per 100 person-years) had major bleeding. In the CHADS₂ score ≤1 group, the hazard ratio of the continued OAC group was 0.86 [95% confidence interval (CI): 0.74–1.01, P = .06] for thromboembolism and was 1.51 (95% CI: 1.27–1.80, P < .001) for major bleeding. In the CHADS₂ score ≥3 group, the hazard ratio of the continued OAC group was 0.61 (95% CI: 0.46–0.82, P = .001) for thromboembolism and was 1.05 (95% CI: 0.71–1.56, P = 0.81) for major bleeding. This observational study suggests that the benefits and risks of continuing OAC therapy after CA for AF differ based on the patient’s CHADS₂ score. The risk of major bleeding due to OAC continuation seems to outweigh the risk reduction of thromboembolism in patients with lower thromboembolic risk. Status of the continuation of OAC therapy and the association between the continuation of OAC therapy and thromboembolic and bleeding events according to the CHADS₂ score. AF, atrial fibrillation; CA, catheter ablation; HR, hazard ratio; OAC, oral anticoagulant.

Aims/Introduction: Although the epidemiological relationship between hypoglycemia and increased risk of acute coronary syndrome (ACS) has been well established, the time period for increased risk of ACS after a severe hypoglycemic episode remains unknown. The present study aimed to determine the ACS risk after a severe hypoglycemic episode.Materials and Methods: We carried out a retrospective population‐based cohort study based on national claims data in Japan. We retrieved data of diabetes patients aged ≥35 years collected from April 2014 to March 2016. The absolute risk of ACS was defined as the occurrence of an emergency percutaneous coronary intervention after a severe hypoglycemic episode.Results: In total, data of 7,909,626 patients were included in the analysis. The absolute risk of ACS was 2.9 out of 1,000 person‐years in all patients. ACS risk in patients with severe hypoglycemic episodes was 3.0 out of 1,000 person‐years. Severe hypoglycemic episodes increased the absolute risk of ACS in patients aged ≥70 years, but not in patients aged <70 years. The absolute risk of ACS was 10.6 out of 1,000 person‐years within 10 days of a severe hypoglycemic episode. There was a significant trend between shorter duration after an episode and higher ACS risk.Conclusions: Severe hypoglycemia was associated with an increased risk of ACS in elderly diabetes patients. ACS risk increased with a shorter period after a severe hypoglycemic episode, suggesting that severe hypoglycemia leads to an increased risk of ACS in diabetes patients. These findings show that it is important to avoid severe hypoglycemia while treating diabetes, particularly in elderly patients.

Aim: This study aimed to determine whether there is an association between influenza and new‐onset type 1 diabetes.Materials and methods: This population‐based retrospective cohort study used data from the National Database of Health Insurance Claims and Specific Health Check‐ups of Japan. Influenza was defined based on drug prescriptions and the onset of type 1 diabetes was defined using specific medical codes indicating a diagnosis of type 1 diabetes. The incidence rate ratio of new‐onset type 1 diabetes within 180 days after an influenza diagnosis was calculated and it was compared with that at other times using Poisson regression and generalized estimating equations. Sensitivity analyses were performed to confirm the robustness of this finding.Results: The data of 10,400 patients with new‐onset type 1 diabetes were analyzed, including 2,196 (952 male 1,244 female) patients diagnosed with influenza between 1 September 2014 and 31 August 2017. Although only patients with type 1 diabetes were included, adjusted analysis showed that individuals had a 1.3‐fold (95% confidence interval: 1.15–1.46) higher risk of developing type 1 diabetes in the first 180 days after influenza diagnosis than that at other times.Conclusions: In this Japanese population‐based cohort, the risk of new‐onset type 1 diabetes may increase after the diagnosis of influenza. These results, which must be confirmed in other populations, suggest that influenza may be a causal factor for new‐onset type 1 diabetes. The molecular mechanisms underlying the potential etiological relationship between influenza and type 1 diabetes should be elucidated.

Background: The National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) is a comprehensive database of health insurance claims data under Japan’s National Health Insurance system. The NDB uses two types of personal identification variables (referred to in the database as “ID1” and “ID2”) to link the insurance claims of individual patients. However, the information entered against these ID variables is prone to change for several reasons, such as when claimants find or change employment, or due to variations in the spelling of their name. In the present study, we developed a new patient-matching technique that improves upon the existing system of using ID1 and ID2 variables. We also sought to validate a new personal ID variable (ID0) that we propose in order to enhance the efficiency of patient matching in the NDB database. Methods: Our study targeted data from health insurance claims filed between April 2013 and March 2016 for hospitalization, combined diagnostic procedures, outpatient treatment, and dispensing of prescription medication. We developed a new patient-matching algorithm based on the ID1 and ID2 variables, as well as variables for treatment date and clinical outcome. We then attempted to validate our algorithm by comparing the number of patients identified by patient matching with the current ID1 variable and our proposed ID0 variable against the estimated patient population as of 1 October 2015. Results: The numbers of patients in each sex and age group that were identified with the ID0 variable were lower than those identified using the ID1 variable. By using the ID0 variable, we were able to reduce the number of duplicate records for male and female patients by 5.8% and 6.4%, respectively. The numbers of children, adults older than 75 years, and women of reproductive age identified using the ID1 patient-matching variable were all higher than their corresponding estimates. Conversely, the numbers of these patients identified with the ID0 patient-matching variable were all within their corresponding estimates. Conclusion: Our findings show that the proposed ID0 variable delivers more precise patient-matching results than the existing ID1 variable. The ID0 variable is currently the best available technique for patient matching in the NDB database. Future patient population estimates should therefore rely on the ID0 variable instead of the ID1 variable.

This study clarified the age of death in patients with or without diabetes using the largest health insurance database in Japan. This population‐based retrospective cohort study was performed using the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) data. The ages of death between people with and without diabetes were compared. A total of 142,277,986 patients (74,488,962 women and 67,789,024 men) over 6 years, including 4,647,016 females, and 6,507,817 males with diabetes, were included. 2,786,071 females and 2,975,876 males died over 6 years, including 652,699 females and 954,655 males with diabetes. The average age of death in patients with diabetes was 2.6 years less than that of patients without diabetes. This descriptive epidemiological study illustrated the difference in age at death of patients with and without diabetes.