Analysis of the PARAGON-HF Study Results Using Win Ratio.

BACKGROUND: The PARAGON-HF study (Prospective Comparison of ARNI With ARB Global Outcomes in Heart Failure With Preserved Ejection Fraction) investigated the effect of sacubitril-valsartan in heart failure (HF) with preserved ejection fraction. The results, which were analyzed using conventional statistical methods, did not find a significant reduction in the primary composite end point of cardiovascular death and total hospitalization for HF. Recent clinical trials used win ratio statistics that enable the incorporation of multiple outcome aspects into the primary end point and can detect positive outcomes with fewer patients. In this study, we assessed the effect of sacubitril-valsartan on outcomes using the win ratio to analyze results from patients included in the PARAGON-HF study. METHODS: In the PARAGON-HF study, 4822 patients with HF with preserved ejection fraction were randomized either to sacubitril-valsartan or valsartan groups. In the present study, the primary outcome was a hierarchical composite of time to cardiovascular death, total number of hospitalization for HF, time to first hospitalization for HF, time to renal composite outcome, and change in the Kansas City Cardiomyopathy Questionnaire total symptom score at 8 months analyzed using a win ratio statistical model. RESULTS: Using this approach, we found that a greater number of patients who received sacubitril-valsartan experienced clinical benefits compared with those who received valsartan (win ratio, 1.13 [95% CI, 1.04-1.23]; P=0.005). This clinical advantage was evident in patients regardless of whether the left ventricular ejection fraction was above or below the median, that is, the left ventricular ejection fraction of 57%, and regardless of sex (Pinteraction=0.76 for the left ventricular ejection fraction and 0.73 for sex). CONCLUSIONS: Employing the innovative win ratio approach, sacubitril-valsartan demonstrated significant clinical benefits among patients with HF with preserved ejection fraction. Notably, this benefit was observed irrespective of left ventricular ejection fraction and sex. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01920711.

Risk factors based vessel-specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method: Results from the PARADIGM registry.

BACKGROUND AND HYPOTHESIS: The recently introduced Bayesian quantile regression (BQR) machine-learning method enables comprehensive analyzing the relationship among complex clinical variables. We analyzed the relationship between multiple cardiovascular (CV) risk factors and different stages of coronary artery disease (CAD) using the BQR model in a vessel-specific manner. METHODS: From the data of 1,463 patients obtained from the PARADIGM (NCT02803411) registry, we analyzed the lumen diameter stenosis (DS) of the three vessels: left anterior descending (LAD), left circumflex (LCx), and right coronary artery (RCA). Two models for predicting DS and DS changes were developed. Baseline CV risk factors, symptoms, and laboratory test results were used as the inputs. The conditional 10%, 25%, 50%, 75%, and 90% quantile functions of the maximum DS and DS change of the three vessels were estimated using the BQR model. RESULTS: The 90th percentiles of the DS of the three vessels and their maximum DS change were 41%-50% and 5.6%-7.3%, respectively. Typical anginal symptoms were associated with the highest quantile (90%) of DS in the LAD; diabetes with higher quantiles (75% and 90%) of DS in the LCx; dyslipidemia with the highest quantile (90%) of DS in the RCA; and shortness of breath showed some association with the LCx and RCA. Interestingly, High-density lipoprotein cholesterol showed a dynamic association along DS change in the per-patient analysis. CONCLUSIONS: This study demonstrates the clinical utility of the BQR model for evaluating the comprehensive relationship between risk factors and baseline-grade CAD and its progression.

Effects of catastrophic financial loss on suicide risk: evidence from Korean stock market crash in October 2008.

PURPOSE: The negative effect of catastrophic financial loss on suicide risk is widely perceived but hardly studied in-depth because of various difficulties in designing studies. We empirically investigated the effect utilizing the stock market crash event in October 2008 in South Korea. METHODS: We extracted stock market investor data from Korea Exchanges, and mortality data from Microdata Integrated Service of individuals aged 30-60 years. We calculated age-standardized monthly suicide rate per 100,000 persons according to sex and age, and developed intervention analysis with multiplicative seasonal ARIMA model to isolate the effect of the stock market crash on suicide rate. RESULTS: More than 11% of people aged 30-60 years were directly investing in stocks during stock market crash. In October 2008, both KOSPI and KOSDAQ indexes dropped by 22.67% and 30.14%, respectively. In November 2008, the suicide rate in males 30-60 years increased by > 40% compared to the expected levels if there had been no market crash, and in females aged 30-40 and 40-50 years, it increased by 101.84% and 74.81%, respectively. The effect appeared to persist in males, whereas it degenerated with time in females during our sampling period. Suicide was more pronounced in younger age groups and females. CONCLUSION: In this first in-depth study, the effect of catastrophic financial loss negatively affects suicide risk for an extended period, indicating health and financial authorities should provide a long-term financial and psychological support for people with extreme financial loss.

Predicting survival in heart failure: a risk score based on machine-learning and change point algorithm.

OBJECTIVE: Machine learning (ML) algorithm can improve risk prediction because ML can select features and segment continuous variables effectively unbiased. We generated a risk score model for mortality with ML algorithms in East-Asian patients with heart failure (HF). METHODS: From the Korean Acute Heart Failure (KorAHF) registry, we used the data of 3683 patients with 27 continuous and 44 categorical variables. Grouped Lasso algorithm was used for the feature selection, and a novel continuous variable segmentation algorithm which is based on change-point analysis was developed for effectively segmenting the ranges of the continuous variables. Then, a risk score was assigned to each feature reflecting nonlinear relationship between features and survival times, and an integer score of maximum 100 was calculated for each patient. RESULTS: During 3-year follow-up time, 32.8% patients died. Using grouped Lasso, we identified 15 highly significant independent clinical features. The calculated risk score of each patient ranged between 1 and 71 points with a median of 36 (interquartile range: 27-45). The 3-year survival differed according to the quintiles of the risk score, being 80% and 17% in the 1st and 5th quintile, respectively. In addition, ML risk score had higher AUCs than MAGGIC-HF score to predict 1-year mortality (0.751 vs. 0.711, P < 0.001). CONCLUSIONS: In East-Asian patients with HF, a novel risk score model based on ML and the new continuous variable segmentation algorithm performs better for mortality prediction than conventional prediction models. CLINICAL TRIAL REGISTRATION: Unique identifier: INCT01389843 https://clinicaltrials.gov/ct2/show/NCT01389843 .