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

Development and verification of prediction models for preventing cardiovascular diseases

Objectives Cardiovascular disease (CVD) is one of the major causes of death worldwide. For improved accuracy of CVD prediction, risk classification was performed using national time-series health examination data. The data offers an opportunity to access deep learning (RNN-LSTM), which is widely known as an outstanding algorithm for analyzing time-series datasets. The objective of this study was to show the improved accuracy of deep learning by comparing the performance of a Cox hazard regression and RNN-LSTM based on survival analysis. Methods and findings We selected 361,239 subjects (age 40 to 79 years) with more than two health examination records from 2002–2006 using the National Health Insurance System-National Health Screening Cohort (NHIS-HEALS). The average number of health screenings (from 2002–2013) used in the analysis was 2.9 ± 1.0. Two CVD prediction models were developed from the NHIS-HEALS data: a Cox hazard regression model and a deep learning model. In an internal validation of the NHIS-HEALS dataset, the Cox regression model showed a highest time-dependent area under the curve (AUC) of 0.79 (95% CI 0.70 to 0.87) for in females and 0.75 (95% CI 0.70 to 0.80) in males at 2 years. The deep learning model showed a highest time-dependent AUC of 0.94 (95% CI 0.91 to 0.97) for in females and 0.96 (95% CI 0.95 to 0.97) in males at 2 years. Layer-wise Relevance Propagation (LRP) revealed that age was the variable that had the greatest effect on CVD, followed by systolic blood pressure (SBP) and diastolic blood pressure (DBP), in that order. Conclusion The performance of the deep learning model for predicting CVD occurrences was better than that of the Cox regression model. In addition, it was confirmed that the known risk factors shown to be important by previous clinical studies were extracted from the study results using LRP

Impact of multivessel versus single-vessel disease on the association between low diastolic blood pressure and mortality after acute myocardial infarction with revascularization

Background: Previous studies demonstrated a J-shaped relationship between low diastolic blood pressure (DBP) and adverse clinical outcomes in patients with acute myocardial infarction (AMI) that was sensitive to revascularization. Hypothesized herein, was that this relationship differs between patients with multivessel disease (MVD) and those with single-vessel disease due to differing degrees of myocardial ischemic burden. Methods: Among 9,983 AMI patients from the Korea Acute Myocardial Infarction Registry database who underwent percutaneous coronary intervention and were followed up for a median duration of 3.2 years, average on-treatment DBP was calculated at admission, discharge, and every scheduled visit and divided into these parameters: < 70 mmHg, 70–74 mmHg, 75–79 mmHg, and ≥ 80 mmHg. The relationship between average on-treatment DBP and clinical outcomes including all-cause death, cardiovascular (CV) death, non-CV death, and hospitalization for heart failure was analyzed using the Cox regression models adjusted for clinical covariates. Results: In patients with MVD, all-cause death (hazard ratio [HR]: 1.47; 95% confidence interval [CI]: 1.06–2.04, p = 0.012) and CV death (HR: 1.59; 95% CI: 1.02–2.46, p = 0.027) were significantly increased in patients with a DBP < 70 mmHg, showing a J-shaped relationship. However, these findings were not significant for single-vessel disease. On a sensitivity analysis excluding subjects with a baseline SBP < 120 mmHg, an increased risk of a low DBP < 70 mmHg remained in MVD. Conclusions: The J-shaped relationship between low DBP and adverse clinical outcomes in AMI patients who underwent revascularization persisted in MVD, which has a high ischemic burden. These high-risk patients require cautious treatment

Does Foreign direct investment mode choice affect the value of the investing firm?

This study provides direct evidence of the effect of foreign direct investment-mode choice (acquisitions vs. joint ventures) on the value of the investing firm. Evidence shows that joint ventures, on average, tend to be a value-creating investment mode when firms make initial investments in new foreign countries. On the other hand, acquisitions tend to be, on average, a value-creating investment mode when firms make subsequent investments in countries where they have operating experiences. The findings, consistent with previous research regarding the relation between mode choice and long-run performance of the ventures in foreign host countries, suggest that the strategic choice of investment mode is critical to creating value for investing firm.

Analysis Of Family Business Group Succession: Comparative Case Study On Six Korean Chaebols

A chaebol is a Korean business group with a unique organizational structure in which both ownership and control rights held by a family. As their production accounts for nearly fifty percent of Korea’s GDP and their power in the labor market, it is important to analyze the succession of chaebols, which is closely related to the sustainability of the business. This paper analyzes the six Korean chaebols’ successions to increase our understanding of the processes and outcomes of the family succession. Specifically, we employ the three-circle model, i.e., the ownership, family, and business system, to conduct a comparative case study. Our analysis suggests that succession that involves a large size of succession concentrated to only one successor and restructuring of business portfolio experiences higher post- performance. Also, the level of conflicts in the succession process was not found to have an effect on performance. Overall, our findings imply that the succession is a period available to the company to set a right course of actions for improving competitiveness