Cardiovascular Risk in Patients With Treated Isolated Diastolic Hypertension and Isolated Low Diastolic Blood Pressure.

BACKGROUND: The prognosis of high or markedly low diastolic blood pressure (DBP) with normalized on-treatment systolic blood pressure on major adverse cardiovascular events (MACEs) is uncertain. This study examined whether treated isolated diastolic hypertension (IDH) and treated isolated low DBP (ILDBP) were associated with MACEs in patients with hypertension. METHODS AND RESULTS: A total of 7582 patients with on-treatment systolic blood pressure <130 mm Hg from SPRINT (Systolic Blood Pressure Intervention Trial) were categorized on the basis of average DBP: <60 mm Hg (n=1031; treated ILDBP), 60 to 79 mm Hg (n=5432), ≥80 mm Hg (n=1119; treated IDH). MACE risk was estimated using Cox proportional-hazards models. Among the SPRINT participants, median age was 67.0 years and 64.9% were men. Over a median follow-up of 3.4 years, 512 patients developed a MACE. The incidence of MACEs was 3.9 cases per 100 person-years for treated ILDBP, 1.9 cases for DBP 60 to 79 mm Hg, and 1.8 cases for treated IDH. Comparing with DBP 60 to 79 mm Hg, treated ILDBP was associated with an 1.32-fold MACE risk (hazard ratio [HR], 1.32, 95% CI, 1.05-1.66), whereas treated IDH was not (HR, 1.18 [95% CI, 0.87-1.59]). There was no effect modification by age, sex, atherosclerotic cardiovascular disease risk, or cardiovascular disease history (all P values for interaction >0.05). CONCLUSIONS: In this secondary analysis of SPRINT, among treated patients with normalized systolic blood pressure, excessively low DBP was associated with an increased MACE risk, while treated IDH was not. Further research is required for treated ILDBP management.

Right ventricular scalloping index as cardiac magnetic resonance-derived marker for diagnosis of arrhythmogenic right ventricular cardiomyopathy.

BACKGROUND: The cardiac magnetic resonance (CMR) evaluation of right ventricular (RV) morphologic abnormalities in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC) is subjective. Here, we aimed to use a quantitative index, the right ventricular scalloping index (RVSI), to standardize the measurement of RV free wall scalloping and aid in the imaging diagnosis. METHODS: We retrospectively included 15 patients with definite ARVC and 45 age- and sex-matched patients with idiopathic right ventricular outflow tract ventricular arrhythmia (RVOT-VA) as controls. The RVSI was measured from cine images on four-chamber view to evaluate its ability to distinguish between ARVC and RVOT-VA patients. Other cardiac functional parameters including strain analysis were also performed. RESULTS: The RVSI was significantly higher in the ARVC than RVOT-VA group (1.56 ± 0.23 vs 1.30 ± 0.08, p < 0.001). The diagnostic performance of the RVSI was superior to the RV global longitudinal, circumferential, and radial strains, RV ejection fraction, and RV end-diastolic volume index. The RVSI demonstrated high intraobserver and interobserver reliability (intraclass correlation coefficient, 0.94 and 0.96, respectively). RVSI was a strong discriminator between ARVC and RVOT-VA patients (area under curve [AUC], 0.91; 95% CI, 0.82-0.99). A cutoff value of RVSI ≥1.49 provided an accuracy of 90.0%, specificity of 97.8%, sensitivity of 66.7%, positive predictive value (PPV) of 90.9%, and a negative predictive value (NPV) of 89.8%. In a multivariable analysis, a family history of ARVC or sudden cardiac death (odds ratio, 38.71; 95% CI, 1.48-1011.05; p = 0.028) and an RVSI ≥1.49 (odds ratio, 64.72; 95% CI, 4.58-914.63; p = 0.002) remained predictive of definite ARVC. CONCLUSION: RVSI is a quantitative method with good performance for the diagnosis of definite ARVC.

Symptom-limited exercise capacity is associated with long-term survival.

The prognostic value of exercise capacity has been demonstrated in subjects with established cardiovascular diseases. We aim to evaluate the independence of exercise capacity measured by treadmill exercise test (TET) in predicting long-term outcomes among various comorbidities. This study was conducted from January 2003 to December 2012 in a tertiary medical center in Taiwan. Subjects referred for symptom-limited TET were recruited. Peak achieved metabolic equivalents (METs) were determined by treadmill grade and speed at peak exercise. The main outcomes were cardiovascular and all-cause mortality by linking to the National Death Registry. A total of 18,954 participants (57.8 ± 12.8 years, 62% men) achieved a mean peak METs of 9.2. Subjects in the lowest tertile of peak METs were older, had poorer renal function, lower hemoglobin, and more comorbidities. During a median follow-up of 4.3 years, there were 642 mortalities and 132 cardiovascular deaths. Peak METs significantly predicted cardiovascular death and all-cause mortality in the multivariable Cox regression models [hazard ratio (95% confidence intervals): 0.788 (0.660-0.940) and 0.835 (0.772-0.903), respectively]. The prognostic influence of peak METs consistently appeared in the subgroups, regardless of age, gender, body weight, comorbidities, use of beta-blockers, or the presence of exercise-induced ischemia. The fitness was more predictive of long-term outcomes in young or those with ischemic changes during TET (P for interaction: 0.035 and 0.018, respectively). The benefit of fitness was nonlinearly associated with long-term survival. The prognostic impacts of exercise capacity were universally observed in subjects with or without various comorbidities.

Association between spirometry pattern, left ventricular diastolic function, and mortality.

BACKGROUND: Spirometric abnormalities have been related to incident heart failure in general population, who generally have preserved left ventricular ejection fraction (LVEF). We aimed to investigate the association between spirometric indices, cardiac functions and clinical outcomes. METHODS: Subjects presenting with exertional dyspnoea and received spirometry and echocardiography were eligible for this study. Forced vital capacity (FVC) and forced expiratory volume in the first second (FEV1)/FVC ratio were measured to define the spirometry patterns: normal (FEV1/FVC ≥ 70%, FVC ≥ 80%), obstructive (FEV1/FVC < 70%, FVC ≥ 80%), restrictive pattern (FEV1/FVC ≥ 70%, FVC < 80%) and mixed (FEV1/FVC < 70%, FVC < 80%). The diastolic dysfunction index (DDi) was the counts of the indicators, including septal e' velocity <7 cm/s, septal E/e' > 15, pulmonary artery systolic pressure > 35 mmHg and left atrial dimension >40 mm. RESULTS: Among a total of 8669 participants (65.8 ± 16.3 years, 56% men), 3739 (43.1%), 829 (9.6%), 3050 (35.2%) and 1051 (12.1%) had normal, obstructive, restrictive and mixed spirometry pattern, respectively. Subjects with restrictive or mixed spirometry pattern had higher DDi and worse long-term survival than those with obstructive or normal ventilation. FVC but not FEV1/FVC was predictive of 5-year mortality, independent of age, sex, renal function, LVEF, DDi, body mass index, and comorbidities (hazard ratio, 95% confidence intervals: .981, .977-.985). Furthermore, there was an inverse nonlinear relationship between FVC and DDi, suggesting the declined FVC may mediate 43% of the prognostic hazard of left ventricular diastolic dysfunction. CONCLUSIONS: The restrictive spirometry pattern or the declined FVC was associated with left ventricular diastolic dysfunction, which aggravated the long-term mortality in the ambulatory dyspnoeic subjects.

Effects of bihemispheric transcranial direct current stimulation on motor recovery in subacute stroke patients: a double-blind, randomized sham-controlled trial.

BACKGROUND: Bihemispheric transcranial direct current stimulation (tDCS) of the primary motor cortex (M1) can simultaneously modulate bilateral corticospinal excitability and interhemispheric interaction. However, how tDCS affects subacute stroke recovery remains unclear. We investigated the effects of bihemispheric tDCS on motor recovery in subacute stroke patients. METHODS: We enrolled subacute inpatients who had first-ever ischemic stroke at subcortical regions and moderate-to-severe baseline Fugl-Meyer Assessment of Upper Extremity (FMA-UE) score 2-56. Participants between 14 and 28 days after stroke were double-blind, randomly assigned (1:1) to receive real (n = 13) or sham (n = 14) bihemispheric tDCS (with ipsilesional M1 anode and contralesional M1 cathode, 20 min, 2 mA) during task practice twice daily for 20 sessions in two weeks. Residual integrity of the ipsilesional corticospinal tract was stratified between groups. The primary efficacy outcome was the change in FMA-UE score from baseline (responder as an increase ≥ 10). The secondary measures included changes in the Action Research Arm Test (ARAT), FMA-Lower Extremity (FMA-LE) and explorative resting-state MRI functional connectivity (FC) of target regions after intervention and three months post-stroke. RESULTS: Twenty-seven participants completed the study without significant adverse effects. Nineteen patients (70%) had no recordable baseline motor-evoked potentials (MEP-negative) from the paretic forearm. Compared with the sham group, the real tDCS group showed enhanced improvement of FMA-UE after intervention (p < 0.01, effect size η2 = 0.211; responder rate: 77% vs. 36%, p = 0.031), which sustained three months post-stroke (p < 0.01), but not ARAT. Interestingly, in the MEP-negative subgroup analysis, the FMA-UE improvement remained but delayed. Additionally, the FMA-LE improvement after real tDCS was not significantly greater until three months post-stroke (p < 0.01). We found that the individual FMA-UE improvements after real tDCS were associated with bilateral intrahemispheric, rather than interhemispheric, FC strengths in the targeted cortices, while the improvements after sham tDCS were associated with predominantly ipsilesional FC changes after adjustment for age and sex (p < 0.01). CONCLUSIONS: Bihemispheric tDCS during task-oriented training may facilitate motor recovery in subacute stroke patients, even with compromised corticospinal tract integrity. Further studies are warranted for tDCS efficacy and network-specific neuromodulation. TRIAL REGISTRATION: This study is registered with ClinicalTrials.gov: (ID: NCT02731508).

Impaired renal function and mortalities in acute heart failure with different phenotypes.

AIMS: Impaired renal function (IRF) prevails in patients with acute heart failure. The study aimed to investigate the prevalence of on-admission IRF and its association with short-term and long-term mortalities in patients hospitalized for HF with reduced (HFrEF), mildly reduced (HFmrEF), and preserved (HFpEF) left ventricular ejection fraction (LVEF). METHODS: Patients hospitalized for acute heart failure were enrolled and stratified by LVEF into three phenotypes as HFpEF (≥50%), HFmrEF (40-49%), and HFrEF (<40%). IRF was defined as an estimated glomerular filtration rate of ≤60 mL/min/1.73m2 on admission. National Death Registry was linked for the identification of mortality. RESULTS: Of 2613 patients enrolled, 673 (25.7%) had HFrEF, 367 (14.0%) had HFmrEF, and 1573 (60.1%) had HFpEF, whereas IRF was prevalent among 63.7, 68.6, and 67.5% of them, respectively. IRF significantly correlated with higher long-term mortality in each phenotype of HF. However, IRF was associated with 90-day and 1-year mortality in subjects with HFrEF and HFmrEF, but not HFpEF. After accounting for age, gender, hypertension, diabetes, coronary artery disease, atrial fibrillation, stroke, serum sodium, de novo heart failure, date of enrolment, and systolic blood pressure <90 mmHg or use of inotropic agents, IRF remained related to 5-year mortality in patients with HFrEF (hazard ratio and 95% confidence interval: 1.346, 1.034-1.751), HFmrEF (2.210, 1.435-3.404), and HFpEF (1.493, 1.237-1.801). CONCLUSIONS: On-admission IRF was independently predictive of long-term mortality in patients hospitalized for HF, irrespective of HF phenotypes. Furthermore, IRF was also associated with short-term mortality in HFrEF and HFmrEF, but not in HFpEF.

Prognostic Role of Pulmonary Function in Patients With Heart Failure With Reduced Ejection Fraction.

Background Both ventilatory abnormalities and pulmonary hypertension (PH) are frequently observed in patients with heart failure with reduced ejection fraction. We aim to investigate the association between ventilatory abnormalities and PH in heart failure with reduced ejection fraction, as well as their prognostic impacts. Methods and Results A total of 440 ambulatory patients (age, 66.2±15.8 years; 77% men) with left ventricular ejection fraction ≤40% who underwent comprehensive echocardiography and spirometry were enrolled. Total lung capacity, forced vital capacity, and forced expiratory volume in the first second were obtained. Pulmonary arterial systolic pressure was estimated. PH was defined as a pulmonary arterial systolic pressure of >50 mm Hg. The primary end point was all-cause mortality at 5 years. Patients with PH had significantly reduced total lung capacity, forced vital capacity, and forced expiratory volume in the first second. During a median follow-up of 25.9 months, there were 111 deaths. After accounting for age, sex, body mass index, renal function, smoking, left ventricular ejection fraction, and functional capacity, total lung capacity (hazard ratio [HR] per 1 SD, 0.66; 95% CI per 1 SD, 0.46-0.96), forced vital capacity (HR per 1 SD, 0.64; 95% CI per 1 SD, 0.48-0.84), and forced expiratory volume in the first second (HR per 1 SD, 0.72; 95% CI per 1 SD, 0.53-0.98) were all significantly correlated with mortality in patients without PH. Kaplan-Meier curve demonstrated impaired pulmonary function, defined as forced expiratory volume in the first second ≤58% of predicted or forced vital capacity ≤65% of predicted, was associated with higher mortality in patients without PH (HR, 2.85; 95% CI, 1.66-4.89), but not in patients with PH (HR, 1.05; 95% CI, 0.61-1.82). Conclusions Ventilatory abnormality was more prevalent in patients with heart failure with reduced ejection fraction with PH than those without. However, such ventilatory defects were related to long-term survival only in patients without PH, regardless of their functional status.

A Novel Algorithm to Estimate the Significance Level of a Feature Interaction Using the Extreme Gradient Boosting Machine.

Recent studies have revealed the importance of the interaction effect in cardiac research. An analysis would lead to an erroneous conclusion when the approach failed to tackle a significant interaction. Regression models deal with interaction by adding the product of the two interactive variables. Thus, statistical methods could evaluate the significance and contribution of the interaction term. However, machine learning strategies could not provide the p-value of specific feature interaction. Therefore, we propose a novel machine learning algorithm to assess the p-value of a feature interaction, named the extreme gradient boosting machine for feature interaction (XGB-FI). The first step incorporates the concept of statistical methodology by stratifying the original data into four subgroups according to the two interactive features. The second step builds four XGB machines with cross-validation techniques to avoid overfitting. The third step calculates a newly defined feature interaction ratio (FIR) for all possible combinations of predictors. Finally, we calculate the empirical p-value according to the FIR distribution. Computer simulation studies compared the XGB-FI with the multiple regression model with an interaction term. The results showed that the type I error of XGB-FI is valid under the nominal level of 0.05 when there is no interaction effect. The power of XGB-FI is consistently higher than the multiple regression model in all scenarios we examined. In conclusion, the new machine learning algorithm outperforms the conventional statistical model when searching for an interaction.

Network meta-analysis and trial sequential analysis for atrial fibrillation patients receiving PCI or with ACS.

BACKGROUND: In patients with atrial fibrillation (AF) and acute coronary syndrome (ACS) or undergoing percutaneous coronary intervention (PCI), choosing the most appropriate antithrombotic treatment remains a dilemma. We aimed to compare the relative efficacy and safety outcomes of antithrombotic drugs in patients with AF after undergoing PCI or ACS. METHODS: Randomized controlled trials were systematically searched on PubMed, EMBASE, and the Cochrane Library. Five studies (11,532 patients) were included in the network meta-analysis. Trial sequential analysis (TSA) was performed to assess the reliability and conclusiveness of the meta-analysis comparing the dual antithrombotic therapy strategies with the triple antithrombotic therapy strategy. RESULTS: Compared with vitamin K antagonist + dual antiplatelet therapy, novel oral anticoagulant (NOAC) + P2Y12 inhibitor was associated with a significantly better trial-defined primary safety outcome (odds ratio: 0.53; 95% CI, 0.31-0.90) and the lowest probability of thrombolysis in myocardial infarction major bleeding and intracranial hemorrhage using the cumulative ranking technique. In patients omitting aspirin, TSA demonstrated conclusive evidence with significant decreases in all safety outcomes and inconclusive evidence with a nonsignificant increase in in-stent thrombosis (risk ratio: 1.32; TSA-adjusted 95% CI, 0.54-3.24) and myocardial infarction (risk ratio: 1.19; TSA-adjusted 95% CI, 0.84-1.68). CONCLUSIONS: In patients with AF receiving PCI or with ACS, NOAC + P2Y12 inhibitor was associated with the lowest bleeding risk but resulted in a statistically nonsignificant, numerically greater risk for stent thrombosis and myocardial infarction, suggesting that triple antithrombotic therapy should still be an option for certain patients at a high risk of stent thrombosis or myocardial infarction.

Comparison between American Institute of Medicine Guidelines and Local Recommendation for Gestational Weight Gain in Taiwanese Primiparous Women.

OBJECTIVES: American Institute of Medicine (IOM) recommends different ranges of gestational weight gain (GWG) based on pre-pregnancy body mass index (BMI). In Taiwan, IOM guidelines are implemented concurrently with the local recommendation for GWG (10-14 kg). This study compared between the two sets of guidelines in relation to adverse perinatal outcomes. METHODS: We analyzed 31,653 primiparas with singletons from 2011 to 2016 annual National Breastfeeding Surveys. Logistic regressions for preterm birth, small for gestational age (SGA), large for gestational age (LGA), cesarean section and excessive postpartum weight retention (EPWR) were fitted separately for GWG categorized according to IOM and Taiwan ranges. Areas under the receiver-operator curves (AUC) and the predicted probabilities for each outcome were compared in each BMI group. RESULTS: AUC for both guidelines ranged within 0.51-0.73. Compared to Taiwan recommendation, IOM ranges showed lower probabilities of SGA for underweight (0.11-0.15 versus 0.14-0.18), of LGA for obese (0.12-0.15 versus 0.15-0.18), of EPWR for overweight (0.19-0.30 versus 0.27-0.39), and obese (0.15-0.22 versus 0.25-0.36); and higher probabilities of EPWR for underweight (0.17-0.33 versus 0.14-0.22). CONCLUSIONS FOR PRACTICE: Discriminative performance of IOM and Taiwan recommendations was poor for the five adverse birth outcomes, and no preference for either set of recommendations could be inferred from our results. In the absence of specific GWG guidelines, health care workers may provide inconsistent information to their patients. Future research is needed to explore optimal GWG ranges that can reliably predict locally relevant perinatal outcomes for mother and child.

Inadequate gestational weight gain contributes to increasing rates of low birth weight in Taiwan: 2011-2016 nationwide surveys.

OBJECTIVE: Low birth weight (LBW) is associated with adverse health outcomes. Incidence of LBW in Taiwan grew from 5% in 1997 to 8.4% in 2016. This study aims to identify the role of pre-pregnancy body mass index (BMI) and gestational weight gain (GWG) in LBW rate changes during 2011-2016. MATERIALS AND METHODS: We analyzed 66 135 postpartum women from 6 cross-sectional national surveys. Data were collected through telephone interviews with randomly selected mothers. Logistic regression was applied to assess contribution of maternal characteristics to LBW time changes. RESULTS: LBW increased from 5.3% to 7.0% during 2011-2016 (crude odds ratio (OR) = 1.04/year, p-value for trend = 0.001). Inadequate GWG increased from 27.9% to 41.5% (p-value for trend <0.001). Along with the increase in overweight (9.7%-11.1%) and obese (4.8%-7.4%), prevalence of underweight fluctuated between 16.0% and 17.8%. LBW increased in underweight group from 6.3% to 9.5% (crude OR = 1.09/year, p-value for trend<0.001). Adjustment for GWG attenuated odds ratio per year in total sample (adjusted OR = 1.03, p-value for trend = 0.04) and in underweight (adjusted OR = 1.08, p-value for trend = 0.002). CONCLUSIONS: Increasing percentage of women with inadequate GWG could contribute to LBW increase in Taiwan during 2011-2016, especially for the underweight. Prenatal advice on GWG should be individualized according to pre-pregnancy BMI.

The Optimal Machine Learning-Based Missing Data Imputation for the Cox Proportional Hazard Model.

An adequate imputation of missing data would significantly preserve the statistical power and avoid erroneous conclusions. In the era of big data, machine learning is a great tool to infer the missing values. The root means square error (RMSE) and the proportion of falsely classified entries (PFC) are two standard statistics to evaluate imputation accuracy. However, the Cox proportional hazards model using various types requires deliberate study, and the validity under different missing mechanisms is unknown. In this research, we propose supervised and unsupervised imputations and examine four machine learning-based imputation strategies. We conducted a simulation study under various scenarios with several parameters, such as sample size, missing rate, and different missing mechanisms. The results revealed the type-I errors according to different imputation techniques in the survival data. The simulation results show that the non-parametric "missForest" based on the unsupervised imputation is the only robust method without inflated type-I errors under all missing mechanisms. In contrast, other methods are not valid to test when the missing pattern is informative. Statistical analysis, which is improperly conducted, with missing data may lead to erroneous conclusions. This research provides a clear guideline for a valid survival analysis using the Cox proportional hazard model with machine learning-based imputations.

The Comprehensive Machine Learning Analytics for Heart Failure.

Background: Early detection of heart failure is the basis for better medical treatment and prognosis. Over the last decades, both prevalence and incidence rates of heart failure have increased worldwide, resulting in a significant global public health issue. However, an early diagnosis is not an easy task because symptoms of heart failure are usually non-specific. Therefore, this study aims to develop a risk prediction model for incident heart failure through a machine learning-based predictive model. Although African Americans have a higher risk of incident heart failure among all populations, few studies have developed a heart failure risk prediction model for African Americans. Methods: This research implemented the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, support vector machine, random forest, and Extreme Gradient Boosting (XGBoost) to establish the Jackson Heart Study's predictive model. In the analysis of real data, missing data are problematic when building a predictive model. Here, we evaluate predictors' inclusion with various missing rates and different missing imputation strategies to discover the optimal analytics. Results: According to hundreds of models that we examined, the best predictive model was the XGBoost that included variables with a missing rate of less than 30 percent, and we imputed missing values by non-parametric random forest imputation. The optimal XGBoost machine demonstrated an Area Under Curve (AUC) of 0.8409 to predict heart failure for the Jackson Heart Study. Conclusion: This research identifies variations of diabetes medication as the most crucial risk factor for heart failure compared to the complete cases approach that failed to discover this phenomenon.

Impact of dietary intake of sodium and potassium on short-term blood pressure variability.

BACKGROUND: Dietary Approaches to Stop Hypertension (DASH)-Sodium trial showed that dietary sodium and potassium affect blood pressure (BP). We aimed to investigate whether dietary sodium and potassium affect short-term BP variability (BPV) in addition to BP. METHODS: A total of 343 participants from the DASH-Sodium trial (age 48.4 ±â€Š9.7, 42.5% men) and 323 individuals from the Jackson Heart Study (JHS) (age 56.7 ±â€Š11.2, 30.7% men) with satisfactory ambulatory BP monitoring records and 24-h urine collection were included. Average real variability (ARV) was calculated as a measure of short-term BPV. RESULTS: By estimating dietary intake from urinary excretion, we observed that higher urinary sodium-to-potassium ratio was significantly associated with higher diastolic ARV in both studies. Among the DASH-Sodium trial, potassium-rich DASH diet alone had insignificant effect on both systolic (-0.1 ±â€Š1.7 mmHg, P = 0.343) or diastolic ARV (-0.2 ±â€Š1.5 mmHg, P = 0.164), whereas combined DASH diet and low sodium intake significantly reduced both systolic (8.5 ±â€Š1.6 vs. 8.9 ±â€Š1.7 mmHg, P = 0.032) and diastolic ARV (7.5 ±â€Š1.5 vs. 7.8 ±â€Š1.6 mmHg, P = 0.025) as compared with control diet and high sodium intake. As the reduction of systolic ARV was majorly derived from the change of mean SBP, diastolic ARV was significantly determined by urinary sodium-to-potassium ratio (ß coefficient ±â€Šstandard error: 0.012 ±â€Š0.004; P = 0.006) after adjusting for age, sex, smoking, mean DBP, BMI, and race. CONCLUSION: Dietary sodium and potassium can jointly modulate short-term BPV in addition to BP. Combined DASH diet and low sodium intake may reduce systolic and diastolic ARV via different mechanisms.

Family-based gene-environment interaction using sequence kernel association test (FGE-SKAT) for complex quantitative traits.

After the genome-wide association studies (GWAS) era, whole-genome sequencing is highly engaged in identifying the association of complex traits with rare variations. A score-based variance-component test has been proposed to identify common and rare genetic variants associated with complex traits while quickly adjusting for covariates. Such kernel score statistic allows for familial dependencies and adjusts for random confounding effects. However, the etiology of complex traits may involve the effects of genetic and environmental factors and the complex interactions between genes and the environment. Therefore, in this research, a novel method is proposed to detect gene and gene-environment interactions in a complex family-based association study with various correlated structures. We also developed an R function for the Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT), which is freely available as supplementary material for easy GWAS implementation to unveil such family-based joint effects. Simulation studies confirmed the validity of the new strategy and the superior statistical power. The FGE-SKAT was applied to the whole genome sequence data provided by Genetic Analysis Workshop 18 (GAW18) and discovered concordant and discordant regions compared to the methods without considering gene by environment interactions.

Extensions of the distributed lag non-linear model (DLNM) to account for cumulative mortality.

The effects of meteorological factors on health outcomes have gained popularity due to climate change, resulting in a general rise in temperature and abnormal climatic extremes. Instead of the conventional cross-sectional analysis that focuses on the association between a predictor and the single dependent variable, the distributed lag non-linear model (DLNM) has been widely adopted to examine the effect of multiple lag environmental factors health outcome. We propose several novel strategies to model mortality with the effects of distributed lag temperature measures and the delayed effect of mortality. Several attempts are derived by various statistical concepts, such as summation, autoregressive, principal component analysis, baseline adjustment, and modeling the offset in the DLNM. Five strategies are evaluated by simulation studies based on permutation techniques. The longitudinal climate and daily mortality data in Taipei, Taiwan, from 2012 to 2016 were implemented to generate the null distribution. According to simulation results, only one strategy, named MVDLNM, could yield valid type I errors, while the other four strategies demonstrated much more inflated type I errors. With a real-life application, the MVDLNM that incorporates both the current and lag mortalities revealed a more significant association than the conventional model that only fits the current mortality. The results suggest that, in public health or environmental research, not only the exposure may post a delayed effect but also the outcome of interest could provide the lag association signals. The joint modeling of the lag exposure and the delayed outcome enhances the power to discover such a complex association structure. The new approach MVDLNM models lag outcomes within 10 days and lag exposures up to 1 month and provide valid results.

A novel cross-validation strategy for artificial neural networks using distributed-lag environmental factors.

In recent years, machine learning methods have been applied to various prediction scenarios in time-series data. However, some processing procedures such as cross-validation (CV) that rearrange the order of the longitudinal data might ruin the seriality and lead to a potentially biased outcome. Regarding this issue, a recent study investigated how different types of CV methods influence the predictive errors in conventional time-series data. Here, we examine a more complex distributed lag nonlinear model (DLNM), which has been widely used to assess the cumulative impacts of past exposures on the current health outcome. This research extends the DLNM into an artificial neural network (ANN) and investigates how the ANN model reacts to various CV schemes that result in different predictive biases. We also propose a newly designed permutation ratio to evaluate the performance of the CV in the ANN. This ratio mimics the concept of the R-square in conventional statistical regression models. The results show that as the complexity of the ANN increases, the predicted outcome becomes more stable, and the bias shows a decreasing trend. Among the different settings of hyperparameters, the novel strategy, Leave One Block Out Cross-Validation (LOBO-CV), demonstrated much better results, and the lowest mean square error was observed. The hyperparameters of the ANN trained by the LOBO-CV yielded the minimum number of prediction errors. The newly proposed permutation ratio indicates that LOBO-CV can contribute up to 34% of the prediction accuracy.

Feasibility of the transcatheter mitral valve repair for patients with severe mitral regurgitation and endangered heart failure.

BACKGROUND: The transcatheter edge-to-edge mitral valve repair using MitraClip has been a safe and effective treatment for severe mitral regurgitation (MR). In patients with severe MR and cardiogenic shock under hemodynamic supporting devices, emergent surgical mitral valve interventions carry extremely high risk for peri-operative morbidities and mortalities. The feasibility and efficacy of emergent MitraClip to rescue patients in critical conditions remains elucidate. METHODS: Patients with severe MR and high or prohibitive surgical risks were referred for MitraClip procedures. Emergent MitraClip were conducted in patients with unstable hemodynamics and under mechanical or inotropic support. The hemodynamic measures, transthoracic echocardiography, transesophageal echocardiography, and blood tests were performed before MitraClip procedures. Procedural success was defined as having mild mitral regurgitation immediately after MitraClip, and patients were free from in-hospital mortality. Clinical and echocardiographic outcomes were followed by telephones and clinics. RESULTS: Among 50 consecutive patients (74.7 ± 11.2 years, 74% male), 8 emergent MitraClip procedures were conducted to rescue patients with cardiogenic shock. Extracorporeal membrane oxygenations were used in 2 patients and intra-aortic balloon pump were applied in 4 patients (50%). Compare to those who underwent elective procedures, patients underwent emergent MitraClip had higher surgical risk profile (EuroSCORE II 34.8% vs 5.1% and STS score 19.7% vs 5.1%), poorer renal function and higher right atrial pressure. There was no peri-procedural death, myocardial infarction, stroke or any adverse events requiring emergent cardiac surgery in both groups. Mild mitral regurgitation was achieved in 87.5% patients from the emergent group and 95.2% patients in the elective group (P = 0.514). The Kaplan-Meier analysis showed patients who underwent emergent procedures have poorer long-term survival rate as compare to those who received elective procedures. (P value = 0.008). CONCLUSION: When open-heart surgery is not feasible, trans-catheter mitral valve repair is an alternative way to rescue patients in cardiogenic shock status.

A novel machine learning strategy for model selections - Stepwise Support Vector Machine (StepSVM).

An essential aspect of medical research is the prediction for a health outcome and the scientific identification of important factors. As a result, numerous methods were developed for model selections in recent years. In the era of big data, machine learning has been broadly adopted for data analysis. In particular, the Support Vector Machine (SVM) has an excellent performance in classifications and predictions with the high-dimensional data. In this research, a novel model selection strategy is carried out, named as the Stepwise Support Vector Machine (StepSVM). The new strategy is based on the SVM to conduct a modified stepwise selection, where the tuning parameter could be determined by 10-fold cross-validation that minimizes the mean squared error. Two popular methods, the conventional stepwise logistic regression model and the SVM Recursive Feature Elimination (SVM-RFE), were compared to the StepSVM. The Stability and accuracy of the three strategies were evaluated by simulation studies with a complex hierarchical structure. Up to five variables were selected to predict the dichotomous cancer remission of a lung cancer patient. Regarding the stepwise logistic regression, the mean of the C-statistic was 69.19%. The overall accuracy of the SVM-RFE was estimated at 70.62%. In contrast, the StepSVM provided the highest prediction accuracy of 80.57%. Although the StepSVM is more time consuming, it is more consistent and outperforms the other two methods.

A statistical predictive model consistent within a 5-year follow-up period for patients with acute heart failure.

BACKGROUND: Acute heart failure (AHF) is a major and rapidly growing health problem responsible for millions of hospitalizations annually. Due to a high proportion of in-hospital mortality and postdischarge rehospitalization and mortality, a prompt strategy for risk stratification and subsequently tailored therapy is desirable to help improve clinical outcomes. The AHEAD (A: atrial fibrillation; H: hemoglobin; E: elderly; A: abnormal renal parameters; D: diabetes mellitus) and AHEAD-U (A: atrial fibrillation; H: hemoglobin; E: elderly; A: abnormal renal parameters; D: diabetes mellitus, U: uric acid) are popular prognostic scoring systems. However, only a specific follow-up period is considered in these systems, and whether their predictive capability is still accurate in a significantly shorter or longer follow-up period is not known. METHODS: In this research, we adapted extensive statistical approaches based on the Cox model to explore consistent risk factors in various follow-up durations. Results showed that six factors, namely, hemoglobin level, age, sodium level, blood urea nitrogen level, atrial fibrillation, and high-density lipoprotein level could be used to establish a new prognostic model, which was referred to as HANBAH. For a simple clinical application, the HANBAH scoring system, with scores from 0 to 6, was developed using several statistical models. RESULTS: Based on an evaluation using the conventional statistical approaches, such as the Akaike information criterion, concordance statistic, and Cox area under the curve, the HANBAH scoring system consistently outperformed other strategies in predicting short- and long-term mortality. Notably, an independent replication study also revealed similar results. In addition, a modern machine learning technique using the support vector machine confirmed its superior performance. CONCLUSION: The use of the HANBAH scoring system, which is a clinically friendly tool, was proposed, and its efficacy in predicting the mortality rates of patients with AHF regardless of the follow-up duration was independently validated.