黄博平

中国医学科学院阜外医院

Association of Sex With Cardiovascular Outcomes in Heart Failure Patients With Obstructive or Central Sleep Apnea.

BACKGROUND:This study investigated the association of sex with cardiovascular outcomes in a prospective cohort of patients with heart failure (HF) with obstructive sleep apnea or central sleep apnea.METHODS AND RESULTS:Patients were screened for sleep apnea on admission using multichannel cardiopulmonary monitoring from May 2015 to July 2018. The primary outcome was a composite of cardiovascular death or unplanned hospitalization for worsening HF. Ultimately, 453 patients with HF with obstructive sleep apnea or central sleep apnea were included; 71 (15.7%) and 382 (84.3%) were women and men, respectively. During a median follow-up of 2.33 years, 248 (54.7%) patients experienced the primary outcome. In the overall population, after adjusting for potential confounders, women had an increased risk of the primary outcome (66.2% versus 52.6%; hazard ratio [HR], 1.47 [95% CI, 1.05-2.04]; P=0.024) and HF rehospitalization (62.0% versus 46.6%; HR, 1.55 [95% CI, 1.10-2.19]; P=0.013) compared with men but a comparable risk of cardiovascular death (21.1% versus 23.3%; HR, 0.78 [95% CI, 0.44-1.37]; P=0.383). Likewise, in patients with HF with obstructive sleep apnea, women had a higher risk of the primary outcome (81.8% versus 46.3%, HR, 2.37 [95% CI, 1.28-4.38]; P=0.006) and HF rehospitalization (81.8% versus 44.7%, HR, 2.46 [95% CI, 1.32-4.56], P=0.004). However, in patients with HF with central sleep apnea, there was no statistically significant difference between women and men.CONCLUSIONS:In hospitalized patients with HF, female sex was associated with an increased risk of the primary outcome and HF rehospitalization, especially in those with obstructive sleep apnea. Screening for sleep apnea should be emphasized to improve the prognosis.REGISTRATION:URL: https://www.clinicaltrials.gov. Unique identifier: NCT02664818.

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第一作者

Journal of the American Heart Association 2024

Sex differences in the associations between relative fat mass and all-cause and cardiovascular mortality: A population-based prospective cohort study.

BACKGROUND AND AIMS:The novel sex-specific anthropometric equation relative fat mass (RFM) is a new estimator of whole-body fat %. The study aimed to investigate the predictive role of RFM in cardiometabolic abnormalities, cardiovascular disease (CVD), all-cause and cardiovascular mortality, and explored potential sex differences.METHODS AND RESULTS:The study analyzed data from 26,754 adults in NHANES 1999-2010, with a median follow-up of 13.8 years. The correlation between RFM and body composition as well as fat distribution assessed by dual-energy X-ray absorptiometry was investigated. Weighted multivariable generalized linear models, Cox proportional hazards models and restricted cubic spline were applied to investigate the predictive role of RFM in metabolic markers, cardiovascular risk factors, CVD, all-cause and cardiovascular mortality. RFM exhibited a robust correlation with both whole-body fat % and trunk fat %. Higher RFM exhibited a stronger association with impaired glucose homeostasis, serum lipids, the incidence of hypertension, and coronary heart disease in males, while a stronger association with C-reactive protein in females. A U-shaped association between RFM and all-cause mortality was observed only in males. The hazard ratio (HR) of all-cause and cardiovascular mortality in males increased rapidly when RFM exceeded 30. However, in females, the HR of all-cause and cardiovascular mortality fluctuated until RFM exceeded 45, after which it increased rapidly.CONCLUSION:RFM was a sex-specific estimator for both general and central obesity, sex-specific differences in predicting cardiometabolic abnormalities and adverse events using RFM might be partially attributed to differences in body composition and fat distribution between sexes.

3.9
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Nutrition, metabolism, and cardiovascular diseases : NMCD 2024

Prevalence and prognostic importance of malnutrition, as assessed by four different scoring systems, in elder patients with heart failure.

BACKGROUND AND AIMS:The lack of standard diagnostic criteria in elder patients with heart failure (HF) makes it challenging to diagnose and manage malnutrition. We aimed to explore the prevalence of malnutrition, its associations and prognostic significance among elder patients with HF using four different nutritional scoring systems.METHODS AND RESULTS:Consecutively presenting patients aged ⩾65 years, diagnosed with HF, and admitted to HF care unit of Fuwai Hospital CAMS&PUMC (Beijing, China) were assessed for nutritional indices. In total, 1371 patients were enrolled (59.4% men; mean age 72 years; median NT-proBNP 2343 ng/L). Using scores for the prognostic nutritional index (PNI) ≤38, controlling nutritional status (CONUT) score >4, geriatric nutritional risk index (GNRI) ≤91, and triglycerides, total cholesterol, and body weight index (TCBI) ≤1109, 10.4%, 18.3%, 9.2%, and 50.0% of patients had moderate or severe malnutrition, respectively. There was a strong association between worse scores and lower body mass index, more severe symptoms, atrial fibrillation, and anemia. The mortality over a median follow-up of 962 days (interquartile range (IQR): 903-1029 days) was 28.3% (n = 388). For those with moderate or severe condition, 1-year mortality was 35.2% for PNI, 28.3% for CONUT, 28.0% for GNRI, and 19.1% for TCBI. Malnutrition, defined by any of the included indices, showed added prognostic value when incorporated into a model and included preexisting prognostic factors (C-statistic: 0.711). However, defining malnutrition by the CONUT score yielded the most significant improvement in the prognostic predictive value (C-statistic: 0.721; p < 0.001).CONCLUSION:Malnutrition is prevalent among elder patients with HF and confers increased mortality risk. Among the nutritional scores studied, the CONUT score was most effective in predicting the mortality risk.CLINICAL TRIAL REGISTRATION:URL: ClinicalTrials.gov; Unique Identifier: NCT02664818.

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Nutrition, metabolism, and cardiovascular diseases : NMCD 2023

Predictive value of remnant cholesterol level for all-cause mortality in heart failure patients.

Background:Lower cholesterol levels are associated with increased mortality in heart failure (HF) patients. Remnant cholesterol corresponds to all cholesterol not found in high-density lipoprotein (HDL) and low-density lipoprotein (LDL). The prognostic role of remnant cholesterol in HF remains unknown.Objective:To reveal the relationship between the baseline remnant cholesterol level and all-cause mortality in HF patients.Methods:This study enrolled 2,823 patients hospitalized for HF. Kaplan-Meier analysis, Cox regression, C-statistic, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate the prognostic value of remnant cholesterol for all-cause mortality in HF.Results:The mortality rate was lowest in the fourth quartile of remnant cholesterol, which had an adjusted hazard ratio (HR) for death of 0.56 [HR: 0.39, 95% confidence interval (CI): 0.46-0.68, p < 0.001] relative to the first quartile. After adjustment, a one-unit increase in the level of remnant cholesterol was associated with a 41% decrease in the risk of all-cause mortality (HR: 0.59, 95% CI: 0.47-0.73, p < 0.001). A refinement in risk prediction was observed after adding remnant cholesterol quartile to the original model (ΔC-statistic = 0.010, 95% CI: 0.003-0.017; NRI = 0.036, 95% CI: 0.003-0.070; IDI = 0.025, 95% CI: 0.018-0.033; all p < 0.05).Conclusion:Low remnant cholesterol levels are associated with increased all-cause mortality in HF patients. The addition of the remnant cholesterol quartile improved the predictive value over traditional risk factors.Clinical Trial Registration:ClinicalTrials.gov, Unique Identifier: NCT02664818.

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Frontiers in cardiovascular medicine 2023

Combined use of right ventricular coupling and pulmonary arterial elastance as a comprehensive stratification approach for right ventricular function.

Right ventricular (RV)-pulmonary arterial uncoupling is the consequence of increased afterload and/or decreased RV contractility. However, the combination of arterial elastance (Ea) and end-systolic elastance (Ees)/Ea ratio to assess RV function is unclear. We hypothesized that the combination of both could comprehensively assess RV function and refine risk stratification. The median Ees/Ea ratio (0.80) and Ea (0.59 mmHg/mL) were used to classify 124 patients with advanced heart failure into four groups. RV systolic pressure differential was defined as end-systolic pressure (ESP) minus beginning-systolic pressure (BSP). Patients among different subsets showed dissimilar New York Heart Association functional class (V = 0.303, p = 0.010), distinct tricuspid annular plane systolic excursion/ pulmonary artery systolic pressure (mm/mmHg; 0.65 vs. 0.44 vs. 0.32 vs. 0.26, p < 0.001), and diverse prevalence of pulmonary hypertension (33.3% vs. 35% vs. 90% vs. 97.6%, p < 0.001). By multivariate analysis, Ees/Ea ratio (hazard ratio [HR] 0.225, p = 0.004) and Ea (HR 2.194, p = 0.003) were independently associated with event-free survival. Patients with Ees/Ea ratio greater than or equal to 0.80 and Ea less than 0.59 mmHg/mL had better outcomes (p < 0.05). In patients with Ees/Ea ratio greater than or equal to 0.80, those with Ea greater than or equal to 0.59 mmHg/mL had a higher adverse outcome risk (p < 0.05). Ees/Ea ratio less than or equal to 0.80 was associated with adverse outcomes, even when Ea was less than 0.59 mmHg/mL (p < 0.05). Approximately 86% of patients with ESP-BSP greater than 5 mmHg had an Ees/Ea ratio less than or equal to 0.80 and/or an Ea greater than or equal to 0.59 mmHg/mL (V = 0.336, p = 0.001). Combined use of Ees/Ea ratio and Ea could be a comprehensive approach to assessing RV function and predicting outcomes. An exploratory analysis demonstrated that Ees/Ea ratio and Ea might be roughly estimated based on RV systolic pressure differential.

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Clinical and translational science 2023

Incorporating inflammatory biomarkers into a prognostic risk score in patients with non-ischemic heart failure: a machine learning approach.

Objectives:Inflammation is involved in the mechanisms of non-ischemic heart failure (NIHF). We aimed to investigate the prognostic value of 21 inflammatory biomarkers and construct a biomarker risk score to improve risk prediction for patients with NIHF.Methods:Patients diagnosed with NIHF without infection during hospitalization were included. The primary outcome was defined as all-cause mortality and heart transplantations. We used elastic net Cox regression with cross-validation to select inflammatory biomarkers and construct the best biomarker risk score model. Discrimination, calibration, and reclassification were evaluated to assess the predictive value of the biomarker risk score.Results:Of 1,250 patients included (median age, 53 years, 31.9% women), 436 patients (34.9%) experienced the primary outcome during a median of 2.8 years of follow-up. The final biomarker risk score included high-sensitivity C-reactive protein-to-albumin ratio (CAR) and red blood cell distribution width-standard deviation (RDW-SD), both of which were 100% selected in 1,000 times cross-validation folds. Incorporating the biomarker risk score into the best basic model improved the discrimination (ΔC-index = 0.012, 95% CI 0.003-0.018) and reclassification (IDI, 2.3%, 95% CI 0.7%-4.9%; NRI, 17.3% 95% CI 6.4%-32.3%) in risk identification. In the cross-validation sets, the mean time-dependent AUC ranged from 0.670 to 0.724 for the biomarker risk score and 0.705 to 0.804 for the basic model with a biomarker risk score, from 1 to 8 years. In multivariable Cox regression, the biomarker risk score was independently associated with the outcome in patients with NIHF (HR 1.76, 95% CI 1.49-2.08, p < 0.001, per 1 score increase).Conclusions:An inflammatory biomarker-derived risk score significantly improved prognosis prediction and risk stratification, providing potential individualized therapeutic targets for NIHF patients.

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Frontiers in immunology 2023

Improved Prognostic Performance of Right Atrial Pressure-Corrected Cardiac Power Output in Pulmonary Hypertension and Heart Failure with Preserved Ejection Fraction.

Cardiac power output (CPO) is a powerful predictor of adverse outcomes in heart failure (HF). However, the original formula of CPO included the difference between mean arterial pressure and right atrial pressure (RAP). The prognostic performance of RAP-corrected CPO (CPORAP) remains unknown in heart failure with preserved ejection fraction (HFpEF). We studied 101 HF patients with a left ventricular ejection fraction > 40% who had pulmonary hypertension due to left heart disease. CPORAP was significantly more discriminating than CPO in predicting outcomes (Delong test, P = 0.004). Twenty-five (24.8%) patients presented with dis-concordantly high CPORAP and low CPO when stratified by the identified CPORAP threshold of 0.547 W and the accepted CPO threshold of 0.803 W. These patients had the lowest RAP, and their cumulative incidence was comparable with those with concordantly high CPO and CPORAP (P = 0.313). CPORAP might identify patients with right ventricular involvement, thereby providing better prognostic performance than CPO in HFpEF.

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Journal of cardiovascular translational research 2023

sST2 and Big ET-1 as Alternatives of Multi-Biomarkers Strategies for Prognosis Evaluation in Patients Hospitalized with Heart Failure.

Objective:To identify biomarkers with independent prognostic value and investigate the prognostic value of multiple biomarkers in combination in patients hospitalized with heart failure.Methods:A total of 884 consecutive patients hospitalized with heart failure from 2015 to 2017 were enrolled. Twelve biomarkers were measured on admission, and the relationships between biomarkers and outcomes were assessed.Results:During the median follow-up of 913 days, 291 patients (32.9%) suffered from primary endpoint events. Soluble suppression of tumorigenicity-2 (sST2) (per log [unit] increase, adjusted HR [95% CI]: 1.39 [1.13,1.72], P = 0.002) and big endothelin-1 (big ET-1) (per log [unit] increase, adjusted HR [95% CI]: 1.56 [1.23,1.97], P < 0.001) remained independent predictors of primary endpoint event after adjusting for other predictors including N-terminal pro-B-type natriuretic peptide (NT-proBNP) and high-sensitivity cardiac troponin T (hs-cTnT). Both sST2 (C-statistic: 0.810 vs 0.801, P = 0.005, and 0.832 vs 0.826, P = 0.024, respectively) and big ET-1 (C-statistic: 0.829 vs 0.801, P = 0.001, and 0.843 vs 0.826, P < 0.001, respectively) significantly improved the predictive value for primary endpoint event at 1 year and 3 years. However, only big ET-1 (C-statistic: 0.852 vs 0.846, P = 0.014) significantly improved the predictive value at 3 months when added to clinical predictors and known biomarkers. According to the number of elevated biomarkers (including NT-proBNP, hs-cTnT, sST2, and big ET-1), patients with three or more elevated biomarkers had a higher risk of primary endpoint event compared to those with two elevated biomarkers (P = 0.001), as well as in patients with two elevated biomarkers compared to those with one elevated biomarker (P = 0.004). However, the risk of primary endpoint event was comparable between patients with one elevated biomarker and those with no elevated biomarker (P = 0.582).Conclusion:Multiple biomarkers in combination could provide a better prognostic value than a single biomarker. sST2 and big ET-1 could act as alternatives of multi-biomarkers strategies for prognosis evaluation beyond NT-proBNP and hs-cTnT in patients hospitalized with heart failure.

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International journal of general medicine 2023

Machine Learning for Mortality Prediction in Patients With Heart Failure With Mildly Reduced Ejection Fraction.

Background Machine-learning-based prediction models (MLBPMs) have shown satisfactory performance in predicting clinical outcomes in patients with heart failure with reduced and preserved ejection fraction. However, their usefulness has yet to be fully elucidated in patients with heart failure with mildly reduced ejection fraction. This pilot study aims to evaluate the prediction performance of MLBPMs in a heart failure with mildly reduced ejection fraction cohort with long-term follow-up data. Methods and Results A total of 424 patients with heart failure with mildly reduced ejection fraction were enrolled in our study. The primary outcome was all-cause mortality. Two feature selection strategies were introduced for MLBPM development. The "All-in" (67 features) strategy was based on feature correlation, multicollinearity, and clinical significance. The other strategy was the CoxBoost algorithm with 10-fold cross-validation (17 features), which was based on the selection result of the "All-in" strategy. Six MLBPMs with 5-fold cross-validation based on the "All-in" and the CoxBoost algorithm with 10-fold cross-validation strategy were developed by the eXtreme Gradient Boosting, random forest, and support vector machine algorithms. The logistic regression model with 14 benchmark predictors was used as a reference model. During a median follow-up of 1008 (750, 1937) days, 121 patients met the primary outcome. Overall, MLBPMs outperformed the logistic model. The "All-in" eXtreme Gradient Boosting model had the best performance, with an accuracy of 85.4% and a precision of 70.3%. The area under the receiver-operating characteristic curve was 0.916 (95% CI, 0.887-0.945). The Brier score was 0.12. Conclusions The MLBPMs could significantly improve outcome prediction in patients with heart failure with mildly reduced ejection fraction, which would further optimize the management of these patients.

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Journal of the American Heart Association 2023

Big Endothelin-1 as a Predictor of Reverse Remodeling and Prognosis in Dilated Cardiomyopathy.

This study aimed to investigate the predictive value of Big endothelin-1(ET-1) for left ventricular reverse remodeling (LVRR) and prognosis in patients with dilated cardiomyopathy (DCM). Patients with DCM and a left ventricular ejection fraction (LVEF) ≤ 50% from 2008 to 2017 were included. LVRR was defined as the LVEF increased by at least 10% or follow-up LVEF increased to at least 50% with a minimum improvement of 5%; meanwhile, the index of left ventricular end-diastolic diameter (LVEDDi) decreased by at least 10% or LVEDDi decreased to ≤33 mm/m2. The composite outcome for prognostic analysis consisted of death and heart transplantations. Of the 375 patients included (median age 47 years, 21.1% female), 135 patients (36%) had LVRR after a median of 14 months of treatment. An independent association was found between Big ET-1 at baseline and LVRR in the multivariate model (OR 0.70, 95% CI 0.55-0.89, p = 0.003, per log increase). Big ET-1, body mass index, systolic blood pressure, diagnosis of type 2 diabetes mellitus (T2DM) and treatment with ACEI/ARB were significant predictors for LVRR after stepwise selection. Adding Big ET-1 to the model improved the discrimination (∆AUC = 0.037, p = 0.042 and reclassification (IDI, 3.29%; p = 0.002; NRI, 35%; p = 0.002) for identifying patients with LVRR. During a median follow-up of 39 (27-68) months, Big ET-1 was also independently associated with the composite outcome of death and heart transplantations (HR 1.45, 95% CI 1.13-1.85, p = 0.003, per log increase). In conclusion, Big ET-1 was an independent predictor for LVRR and had prognostic implications, which might help to improve the risk stratification of patients with DCM.

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Journal of clinical medicine 2023