Woong Kook

Background: Sacubitril/valsartan is known to reduce adverse renal outcomes and slow the decline in estimated glomerular filtration rate (eGFR) compared with renin‐angiotensin system (RAS) inhibitors across the entire spectrum of heart failure. However, whether these renoprotective effects differ by sex has not been elucidated. Thus, we aimed to evaluate whether the treatment effect of sacubitril/valsartan versus RAS inhibitors on renal outcomes differs by sex. Methods: Data sets from the PARADIGM‐HF (Prospective Comparison of Angiotensin Receptor‐Neprilysin Inhibitor With Angiotensin‐Converting Enzyme Inhibition to Determine Impact on Global Mortality and Morbidity in Heart Failure; ejection fraction ≤40%, n=8399) and PARAGON‐HF (Prospective Comparison of Angiotensin Receptor‐Neprilysin Inhibitor With Angiotensin Receptor Blocker Global Outcomes in Heart Failure With Preserved Ejection Fraction; ejection fraction ≥45%, n=4796) trials were integrated in a prespecified pooled analysis. We evaluated the treatment effect (sacubitril/valsartan versus RAS inhibitors) on the prespecified renal composite outcome (death from renal failure, end‐stage renal disease, or ≥50% reduction in eGFR) and changes in eGFR slope in female (n=4311) and male (n=8884) patients to determine whether sex modified the effect of sacubitril/valsartan on renal outcomes. Results: At baseline, women exhibited lower eGFR values than men (67.2±19.7 versus 72.6±20.4 mL/min/1.73 m ² , P P for interaction=0.60). Sacubitril/valsartan attenuated the decline in eGFR compared with RAS inhibitors similarly in women (−1.8 versus −2.2 mL/min/1.73 m ² per year, P =0.006) and men (−1.6 versus −2.3 mL/min/1.73 m ² per year, P P for interaction for difference in eGFR slopes=0.19). Conclusions: Sacubitril/valsartan significantly reduces the incidence of renal composite outcome and decelerates the decline in eGFR compared with RAS inhibitors. The renoprotective effects of this drug are uniformly observed in both women and men.

A graph can be regarded as an electrical network in which each edge is a resistor. This point of view relates combinatorial quantities, such as the number of spanning trees, to electrical ones such as effective resistance. The second and third authors have extended the combinatorics/electricity analogy to higher dimension and expressed the simplicial analogue of effective resistance as a ratio of weighted tree enumerators. In this paper, we first use that ratio to prove a new enumeration formula for color-shifted complexes, confirming a conjecture by Aalipour and the first author, and generalizing a result of Ehrenborg and van Willigenburg on Ferrers graphs. We then use the same technique to recover an enumeration formula for shifted complexes, first proved by Klivans and the first and fourth authors. In each case, we add facets one at a time, and give explicit expressions for simplicial effective resistances of added facets by constructing high-dimensional analogues of currents and voltages (respectively homological cycles and cohomological cocycles).

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.

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.