Development and External Validation of a Deep Learning Algorithm for Prognostication of Cardiovascular Outcomes

BACKGROUND AND OBJECTIVES: We aim to explore the additional discriminative accuracy of a deep learning (DL) algorithm using repeated-measures data for identifying people at high risk for cardiovascular disease (CVD), compared to Cox hazard regression. METHODS: Two CVD prediction models were developed from National Health Insurance Service-Health Screening Cohort (NHIS-HEALS): a Cox regression model and a DL model. Performance of each model was assessed in the internal and 2 external validation cohorts in Koreans (National Health Insurance Service-National Sample Cohort; NHIS-NSC) and in Europeans (Rotterdam Study). A total of 412,030 adults in the NHIS-HEALS; 178,875 adults in the NHIS-NSC; and the 4,296 adults in Rotterdam Study were included. RESULTS: Mean ages was 52 years (46% women) and there were 25,777 events (6.3%) in NHIS-HEALS during the follow-up. In internal validation, the DL approach demonstrated a C-statistic of 0.896 (95% confidence interval, 0.886-0.907) in men and 0.921 (0.908-0.934) in women and improved reclassification compared with Cox regression (net reclassification index [NRI], 24.8% in men, 29.0% in women). In external validation with NHIS-NSC, DL demonstrated a C-statistic of 0.868 (0.860-0.876) in men and 0.889 (0.876-0.898) in women, and improved reclassification compared with Cox regression (NRI, 24.9% in men, 26.2% in women). In external validation applied to the Rotterdam Study, DL demonstrated a C-statistic of 0.860 (0.824-0.897) in men and 0.867 (0.830-0.903) in women, and improved reclassification compared with Cox regression (NRI, 36.9% in men, 31.8% in women). CONCLUSIONS: A DL algorithm exhibited greater discriminative accuracy than Cox model approaches. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02931500.ope

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning

Objectives: To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. Background: Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. Methods: Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. Results: The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. Conclusions: An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level.ope

Machine learning insight into the role of imaging and clinical variables for the prediction of obstructive coronary artery disease and revascularization: An exploratory analysis of the CONSERVE study

Background: Machine learning (ML) is able to extract patterns and develop algorithms to construct data-driven models. We use ML models to gain insight into the relative importance of variables to predict obstructive coronary artery disease (CAD) using the Coronary Computed Tomographic Angiography for Selective Cardiac Catheterization (CONSERVE) study, as well as to compare prediction of obstructive CAD to the CAD consortium clinical score (CAD2). We further perform ML analysis to gain insight into the role of imaging and clinical variables for revascularization. Methods: For prediction of obstructive CAD, the entire ICA arm of the study, comprising 719 patients was used. For revascularization, 1,028 patients were randomized to invasive coronary angiography (ICA) or coronary computed tomographic angiography (CCTA). Data was randomly split into 80% training 20% test sets for building and validation. Models used extreme gradient boosting (XGBoost). Results: Mean age was 60.6 ± 11.5 years and 64.3% were female. For the prediction of obstructive CAD, the AUC was significantly higher for ML at 0.779 (95% CI: 0.672-0.886) than for CAD2 (0.696 [95% CI: 0.594-0.798]) (P = 0.01). BMI, age, and angina severity were the most important variables. For revascularization, the model obtained an overall area under the receiver-operation curve (AUC) of 0.958 (95% CI = 0.933-0.983). Performance did not differ whether the imaging parameters used were from ICA (AUC 0.947, 95% CI = 0.903-0.990) or CCTA (AUC 0.941, 95% CI = 0.895-0.988) (P = 0.90). The ML model obtained sensitivity and specificity of 89.2% and 92.9%, respectively. Number of vessels with ≥70% stenosis, maximum segment stenosis severity (SSS) and body mass index (BMI) were the most important variables. Exclusion of imaging variables resulted in performance deterioration, with an AUC of 0.705 (95% CI 0.614-0.795) (P <0.0001). Conclusions: For obstructive CAD, the ML model outperformed CAD2. BMI is an important variable, although currently not included in most scores. In this ML model, imaging variables were most associated with revascularization. Imaging modality did not influence model performance. Removal of imaging variables reduced model performance.ope

Demographics, treatment trends, and survival rate in incident pulmonary artery hypertension in Korea: A nationwide study based on the health insurance review and assessment service database

Epidemiologic data regarding pulmonary arterial hypertension (PAH) have relied on registries from Western countries. We assessed the current status of PAH in the Korean population. The Health Insurance Review and Assessment Service (HIRA) claim database, which comprises nationwide medical insurance data of Koreans from 2008-2016, was assessed to determine the current status of PAH. Overall, 1,307 patients were newly diagnosed with PAH from 2008-2016 (0.0005%, annual incidence: 4.84 patients/1 million people/year). The mean age at diagnosis was 44±13 years (range 18-65) and patients were mostly women (n = 906, 69.3%). Cases of idiopathic PAH (51.6%) accounted for the largest proportion, followed by acquired PAH (APAH) associated with congenital heart disease (25.8%) and APAH with connective tissue disease (17.2%). Overall, 807 (61.7%) patients received a single PAH-specific treatment based on their last prescription, of which bosentan (50.6%) was the most frequently used. Only 240 (18.4%) patients received combination therapy, with the bosentan-beraprost combination (32.9%) being the most common. During the mean follow-up of 1.9 years, the 1-, 2-, 3-, and 5-year estimated survival rates were 85%, 62%, 54%, and 46%, respectively. The prevalence and incidence of PAH in the Korean population is currently comparable with that in previous registries. The 5-year survival rate was slightly higher in the Korean population than previously reported.ope

Glycemic control is independently associated with rapid progression of coronary atherosclerosis in the absence of a baseline coronary plaque burden: a retrospective case-control study from the PARADIGM registry

Background: The baseline coronary plaque burden is the most important factor for rapid plaque progression (RPP) in the coronary artery. However, data on the independent predictors of RPP in the absence of a baseline coronary plaque burden are limited. Thus, this study aimed to investigate the predictors for RPP in patients without coronary plaques on baseline coronary computed tomography angiography (CCTA) images. Methods: A total of 402 patients (mean age: 57.6 ± 10.0 years, 49.3% men) without coronary plaques at baseline who underwent serial coronary CCTA were identified from the Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging (PARADIGM) registry and included in this retrospective study. RPP was defined as an annual change of ≥ 1.0%/year in the percentage atheroma volume (PAV). Results: During a median inter-scan period of 3.6 years (interquartile range: 2.7-5.0 years), newly developed coronary plaques and RPP were observed in 35.6% and 4.2% of the patients, respectively. The baseline traditional risk factors, i.e., advanced age (≥ 60 years), male sex, hypertension, diabetes mellitus, hyperlipidemia, obesity, and current smoking status, were not significantly associated with the risk of RPP. Multivariate linear regression analysis showed that the serum hemoglobin A1c level (per 1% increase) measured at follow-up CCTA was independently associated with the annual change in the PAV (β: 0.098, 95% confidence interval [CI]: 0.048-0.149; P < 0.001). The multiple logistic regression models showed that the serum hemoglobin A1c level had an independent and positive association with the risk of RPP. The optimal predictive cut-off value of the hemoglobin A1c level for RPP was 7.05% (sensitivity: 80.0%, specificity: 86.7%; area under curve: 0.816 [95% CI: 0.574-0.999]; P = 0.017). Conclusion: In this retrospective case-control study, the glycemic control status was strongly associated with the risk of RPP in patients without a baseline coronary plaque burden. This suggests that regular monitoring of the glycemic control status might be helpful for preventing the rapid progression of coronary atherosclerosis irrespective of the baseline risk factors. Further randomized investigations are necessary to confirm the results of our study. Trial registration: ClinicalTrials.gov NCT02803411.ope

Population-based dementia prediction model using Korean public health examination data: A cohort study

The early identification and prevention of dementia is important for reducing its worldwide burden and increasing individuals' quality of life. Although several dementia prediction models have been developed, there remains a need for a practical and precise model targeted to middle-aged and Asian populations. Here, we used national Korean health examination data from adults (331,126 individuals, 40-69 years of age, mean age: 52 years) from 2002-2003 to predict the incidence of dementia after 10 years. We divided the dataset into two cohorts to develop and validate of our prediction model. Cox proportional hazards models were used to construct dementia prediction models for the total group and sex-specific subgroups. Receiver operating characteristics curves, C-statistics, calibration plots, and cumulative hazards were used to validate model performance. Discriminative accuracy as measured by C-statistics was 0.81 in the total group (95% confidence interval (CI) = 0.81 to 0.82), 0.81 in the male subgroup (CI = 0.80 to 0.82), and 0.81 in the female subgroup (CI = 0.80 to 0.82). Significant risk factors for dementia in the total group were age; female sex; underweight; current hypertension; comorbid psychiatric or neurological disorder; past medical history of cardiovascular disease, diabetes mellitus, or hypertension; current smoking; and no exercise. All identified risk factors were statistically significant in the sex-specific subgroups except for low body weight and current hypertension in the female subgroup. These results suggest that public health examination data can be effectively used to predict dementia and facilitate the early identification of dementia within a middle-aged Asian population.ope

Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database

Models for predicting acute myocardial infarction (AMI) at the prehospital stage were developed and their efficacy compared, based on variables identified from a nationwide systematic emergency medical service (EMS) registry using conventional statistical methods and machine learning algorithms. Patients in the EMS cardiovascular registry aged &gt;15 years who were transferred from the public EMS to emergency departments in Korea from January 2016 to December 2018 were enrolled. Two datasets were constructed according to the hierarchical structure of the registry. A total of 184,577 patients (Dataset 1) were included in the final analysis. Among them, 72,439 patients (Dataset 2) were suspected to have AMI at prehospital stage. Between the models derived using the conventional logistic regression method, the B-type model incorporated AMI-specific variables from the A-type model and exhibited a superior discriminative ability (p = 0.02). The models that used extreme gradient boosting and a multilayer perceptron yielded a higher predictive performance than the conventional logistic regression-based models for analyses that used both datasets. Each machine learning algorithm yielded different classification lists of the 10 most important features. Therefore, prediction models that use nationwide prehospital data and are developed with appropriate structures can improve the identification of patients who require timely AMI management.ope

Risk of new-onset diabetes among patients treated with statins according to hypertension and gender: Results from a nationwide health-screening cohort

BACKGROUND: Statins have been known to increase the risk of incident type 2 diabetes mellitus (DM); however, other factors, especially hypertension, are also associated with DM development. OBJECTIVE: We investigated whether statin use increases the risk of DM and further analyzed whether the relation between statin use and incident DM differs according to the presence of hypertension and gender. METHODS: From a nationwide health-screening cohort, 40,164 participants with total cholesterol levels >/=eve mg/dL and without pre-diagnosed DM, cardiovascular disease, or cancer, who underwent a series of regular health check-ups, were enrolled. Statin users were defined as participants who were prescribed statins more than twice during 6 months. RESULTS: There were 17,798 statin non-users and 22,366 statin users. During 7.66+/-3.21 years of follow-up, incident DM developed in 5.68% of statin non-users and 7.64% of statin users. Among the entire study population, statin use was associated with new-onset DM after adjusting for clinical risk factors. In sub-analysis according to hypertension, statin use significantly increased the risk of incident DM only in normotensive patients [hazard ratio (HR) 1.31, 95% confidence interval (CI) 1.09 to 1.58, p = 0.004], and not in hypertensive patients (p>0.05). Furthermore, continuous statin use was strongly associated with new-onset DM in women, regardless of hypertension presence (all p0.05). CONCLUSIONS: Statin use increased the risk of new-onset DM only in normotensive patients and hypertensive women, suggesting that these groups should be more carefully monitored for the development of DM during the course of follow-up.ope

The relationship of insulin resistance estimated by triglyceride glucose index and coronary plaque characteristics

The triglyceride glucose (TyG) index is a useful surrogate marker for insulin resistance, which is an important risk factor for coronary artery disease (CAD). However, data on the relationship of the TyG index and coronary plaque characteristics are limited.This study included 2840 participants with near-normal renal function who underwent coronary computed tomography angiography. CAD was defined as the presence of any plaques, and obstructive CAD was defined as the presence of plaques with >/=50% stenosis. The relationship between the TyG index and noncalcified plaque (NCP), calcified or mixed plaque (CMP), and coronary artery calcium score (CACS) was evaluated.All participants were stratified into 4 groups based on the quartiles of the TyG index. The prevalence of CAD and obstructive CAD significantly increased with increasing quartiles. The risk for NCP and obstructive NCP was not different among all groups. However, compared with group I (lowest quartile), the risk for CMP was higher in groups III (odds ratio [OR]: 1.438) and IV (highest quartile) (OR: 1.895) (P 400 (OR: 1.448) (P < .05).The TyG index was independently associated with the presence and severity of CAD due to an increased risk for CMP.ope

Associations of changes in body mass index with all-cause and cardiovascular mortality in healthy middle-aged adults.

BACKGROUND: Conflicting data exist regarding the association of body mass index (BMI) changes with all-cause and cardiovascular (CV) mortality. The current study investigated the association between changes in BMI and all-cause, CV, and non-CV mortality in a large cohort of middle-aged adults. METHODS: A total of 379,535 adults over 40 years of age without pre-existing CV disease or cancer at baseline were enrolled to undergo a series of at least three health examinations of biennial intervals. Changes in BMI between baseline, midpoint follow-up, and final health examination during mean 9.3 years were defined according to the pattern of BMI change as follows: stable, sustained gain, sustained loss, and fluctuation. The relationship between BMI change category and mortality was assessed by multivariate Cox regression reporting hazard ratio (HR) with 95% confidence interval (95% CI). RESULTS: During a mean follow-up of 10.7 years for mortality, 12,378 deaths occurred from all causes, of which 2,114 were CV and 10,264 were non-CV deaths. Sustained BMI gain was associated with the lower risk of all-cause (HR 0.89, 95% CI: 0.83-0.95), CV (HR 0.84, 95% CI 0.72-0.98), and non-CV mortality (HR 0.90, 95% CI 0.84-0.96) compared with stable BMI. Conversely, sustained BMI loss (HR 1.25, 95% CI 1.19-1.32) and fluctuation (HR 1.13, 95% CI 1.08-1.19) displayed a higher risk of all-cause mortality compared with stable BMI, which was mainly attributable to the increase in non-CV mortality. CONCLUSION: Sustained BMI gains were associated with reduced risk of all-cause, CV, and non-CV mortality in middle-aged healthy adults.ope