Recurrence and Mortality Risks in Patients with First Incident Acute Stroke or Myocardial Infarction: A Longitudinal Study Using the Korean National Health Insurance Service Database.

Background: We aimed to identify the long-term risk of recurrence and mortality in patients who experienced acute ischemic stroke (AIS), acute myocardial infarction (AMI), or acute hemorrhagic stroke (AHS) using a population-level database. Methods: This retrospective cohort study included adults aged ≥55 years diagnosed with AIS, AMI, and AHS in the National Health Insurance Service Database between 2004 and 2007. The target outcomes were secondary AIS, AMI, AHS, and all-cause mortality. Predetermined covariates, such as age, sex, socioeconomic status, hypertension, diabetes, and dyslipidemia, were adjusted. Results: We included 151,181, 49,077, and 41,636 patients in the AIS, AHS, and AMI groups, respectively. The AMI (adjusted hazard ratio [aHR], 0.318; 95% confidence interval [CI], 0.306−0.330; p < 0.001) and AHS (aHR, 0.489; 95% CI, 0.472−0.506; p < 0.001) groups had a significantly lower risk of developing secondary AIS than the AIS group. The risk of developing secondary AMI was significantly lower in the AMI (aHR, 0.388; 95% CI, 0.348−0.433; p < 0.001) and AHS (aHR, 0.711; 95% CI, 0.640−0.790; p < 0.001) groups than in the AIS group. Initial AHS was a decisive risk factor for secondary AHS (aHR, 8.546; 95% CI, 8.218−8.887; p < 0.001). The AMI (aHR, 1.436; 95% CI, 1.412−1.461; p < 0.001) and AHS (aHR, 1.328; 95% CI, 1.309−1.348; p < 0.001) groups were associated with a significantly higher risk of long-term mortality than the AIS group. Conclusion: Our results elucidated that initial AIS was a significant risk factor for recurrent AIS and AMI; initial AHS was a decisive risk factor for developing secondary AHS. Further, AMI and AHS were more closely related to long-term mortality than AIS.

Potential role of bolus-tracking data of carotid CT angiography for atrial fibrillation prediction.

OBJECTIVES: Atrial fibrillation (AF), a significant cause of ischemic stroke, often goes undetected because of its asymptomatic nature. This study investigated whether the total bolus-tracking time (TTT) and average slope (AS) of a bolus-tracking graph could be used to predict AF. METHODS: This single-center, retrospective study included patients who underwent carotid CTA and a 24-h Holter test. TTT and the average degree of enhancement during bolus-tracking, derived from carotid CTA, were defined as variables of interest. All patients underwent transthoracic echocardiography. Left ventricular diastolic dysfunction and elevated left atrial pressure (LAP) were identified according to the guidelines of the 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging. RESULTS: The final cohort comprised 716 patients, 80 of whom presented with AF. The TTT of the AF group was significantly longer (23.8 ± 5.2 s) than that of the non-AF group (18.7 ± 2.8 s); p < 0.001. The AS of the bolus-tracking graph of the AF group was 0.80 ± 0.24, which was significantly lower than that of the non-AF group 1.38 ± 0.21 (p < 0.001). TTT was associated with a significantly higher risk of AF (odds ratio [OR]: 1.36; p < 0.001) and elevated LAP (OR: 1.46; p < 0.001). In contrast, the AS of the bolus-tracking graph was not significantly associated with either AF or an elevated LAP. CONCLUSION: TTT derived from bolus-tracking carotid CTA is an effective adjuvant tool for detecting AF related to left ventricular diastolic dysfunction and elevated LAP, confirmed using echocardiography. KEY POINTS: • Atrial fibrillation is not only a significant cause of ischemic stroke but is also often masked because of its atypical and asymptomatic features. • The total tracking time, derived from bolus tracking of carotid computed tomography angiography, may be an effective adjuvant tool for detecting undiagnosed atrial fibrillation and elevated left atrial pressure in patients.

Machine Learning-Based Cardiovascular Disease Prediction Model: A Cohort Study on the Korean National Health Insurance Service Health Screening Database.

BACKGROUND: This study proposes a cardiovascular diseases (CVD) prediction model using machine learning (ML) algorithms based on the National Health Insurance Service-Health Screening datasets. METHODS: We extracted 4699 patients aged over 45 as the CVD group, diagnosed according to the international classification of diseases system (I20-I25). In addition, 4699 random subjects without CVD diagnosis were enrolled as a non-CVD group. Both groups were matched by age and gender. Various ML algorithms were applied to perform CVD prediction; then, the performances of all the prediction models were compared. RESULTS: The extreme gradient boosting, gradient boosting, and random forest algorithms exhibited the best average prediction accuracy (area under receiver operating characteristic curve (AUROC): 0.812, 0.812, and 0.811, respectively) among all algorithms validated in this study. Based on AUROC, the ML algorithms improved the CVD prediction performance, compared to previously proposed prediction models. Preexisting CVD history was the most important factor contributing to the accuracy of the prediction model, followed by total cholesterol, low-density lipoprotein cholesterol, waist-height ratio, and body mass index. CONCLUSIONS: Our results indicate that the proposed health screening dataset-based CVD prediction model using ML algorithms is readily applicable, produces validated results and outperforms the previous CVD prediction models.

Characteristics of hypertension subtypes and treatment outcome among elderly Korean hypertensives.

There are limited data about characteristics of hypertension subtypes in Asian hypertensive patients and their impacts on treatment of hypertension. This prospective, multi-center, observational study evaluated 2439 hypertensive patients. (≥60 years) Inadequately controlled and drug-naïve patients were categorized into three hypertension subtypes (isolated systolic hypertension [ISH], combined systolic/diastolic hypertension [SDH], and isolated diastolic hypertension [IDH]), and proportions of each hypertension subtype were evaluated. After 6-month strict treatments, we compared the characteristics of patients who did not achieve target BP with those who did. In overall population, ISH was the most common subtype (53.2%; 1297/2439). However, in drug-naïve patients, SDH was the predominant hypertension subtype (59.6%; 260/436). Notably, the proportion of ISH was substantially lower than previously known data. Predictors associated with failure of reaching target BP were old age (>70 years), hypertension awareness, and baseline systolic blood pressure (≥160 mm Hg) for total patients. In drug naïve patients, hypertension awareness, ISH, and microalbuminuria were associated with treatment failure. These findings might have an impact on the evaluations and antihypertensive treatments of elderly Korean patients.