Effect of sacubitril/valsartan on investigator‐reported ventricular arrhythmias in <scp>PARADIGM‐HF</scp>

Curtain, James P; Jackson, Alice; Shen, Li; Jhund, Pardeep S.; Docherty, Kieran F.; Petrie, Mark C; Castagno, Davide; Desai, Akshay S.; Rohde, Luís Eduardo Paim; Lefkowitz, Martin; Rouleau, Jean‐Lucien; Zile, Michael R.; Solomon, Scott D.; Swedberg, Karl; Packer, Milton; McMurray, John J.V.

doi:10.1002/ejhf.2419

Cited by 26 publications

(15 citation statements)

References 44 publications

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“…A post‐hoc analysis of the Prospective Comparison of ARNI with ACEI to Determine Impact on Global Mortality and Morbidity in Heart Failure (PARADIGM‐HF) trial analysed the effects of sacubitril/valsartan, compared to enalapril, on the incidence of ventricular arrhythmias. Sacubitril/valsartan reduced the risk of ventricular arrhythmias, as well as the composite of ventricular arrhythmia, implantable cardioverter defibrillator shock or resuscitated cardiac arrest, especially in patients with an ischaemic aetiology 36 …”

Section: Guideline‐directed Medical Therapymentioning

confidence: 99%

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March 2022 at a glance: focus on medical therapy, prevention and comorbidities

Tomasoni

Adamo

Metra

2022

European J of Heart Fail

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Prevention of heart failure (HF) has been recently reviewed. 1 Banerjee et al. 2 performed a systematic analysis of randomized controlled trials (RCTs) and observational studies to identify risk factors for HF. Among 92 risk factors analysed, hypertension, stable and unstable angina, myocardial infarction, diabetes, smoking and obesity as well as atrial arrhythmias, cancer and heavy alcohol intake, though not supported by RCTs, were associated with a higher rate of incident HF. In the 5 years prior to HF diagnosis, 44.3% of patients had ≥4 risk factors. These data are consistent with recent studies focused on risk factors for HF. 1,3,4 Assessment of left atrial (LA) function has a major role in the management of HF patients. 5,6 A recent study investigated echocardiographic parameters related to LA function and their predictive value for incident HF in a large general population, without atrial fibrillation or significant valvular heart disease (VHD) at baseline. Out of 1951 participants, 187 (10%) developed HF. After multivariable adjustment, only the minimum LA volume indexed to body surface area was found to be an independent predictor of incident HF. 7 CardiomyopathiesCardiomyopathies represent a heterogeneous group of cardiac diseases causing HF. 22,23 Appropriate aetiological characterization might improve treatment strategies and prognosis. 22,24 In this issue, a position paper from the Heart Failure Association (HFA) and the Working Group on Myocardial Function of the European Society

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Section: Guideline‐directed Medical Therapymentioning

confidence: 99%

“…Sacubitril/valsartan reduced the risk of ventricular arrhythmias, as well as the composite of ventricular arrhythmia, implantable cardioverter defibrillator shock or resuscitated cardiac arrest, especially in patients with an ischaemic aetiology. 36…”

Section: Guideline-directed Medical Therapymentioning

confidence: 99%

March 2022 at a glance: focus on medical therapy, prevention and comorbidities

Tomasoni

Adamo

Metra

2022

European J of Heart Fail

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“…Should it not be that the non‐use of such strongly preventative medications be considered a precipitant of subsequent HF hospitalization? Likewise, among the effects of these medications are reductions in arrhythmias, 13 prevention of worsening kidney function, 14 and lowering of blood pressure, 15 benefits which directly address many of the worsening HF precipitants perceived by investigators in REPORT‐HF. Thus, while perturbations in haemodynamics and renal function certainly require urgent attention when patients present with worsening HFrEF, it may be important to consider absence of comprehensive disease‐modifying medical therapy as the frequent root cause of the clinical deterioration.…”

Section: Prevention Of Worsening Chronic Heart Failurementioning

confidence: 99%

Prioritizing prevention of de novo and worsening chronic heart failure

Bhatt

Fonarow

Greene

2022

European J of Heart Fail

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“…Kawashima [ 3 ] showed that there was a significant interaction in mortality between treatment effect (percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG)) and the presence or absence of heavily calcified lesions (HCLs) (P interaction = 0.005). Another study [ 4 ] estimated the risk of death related to ventricular arrhythmia in time-updated models. This study examined the interaction between heart failure etiology and the effect of sacubitril/valsartan.…”

Section: Introductionmentioning

confidence: 99%

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

Guo

Chang

2022

IJERPH

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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.

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Effect of sacubitril/valsartan on investigator‐reported ventricular arrhythmias in PARADIGM‐HF

Cited by 26 publications

References 44 publications

March 2022 at a glance: focus on medical therapy, prevention and comorbidities

March 2022 at a glance: focus on medical therapy, prevention and comorbidities

Prioritizing prevention of de novo and worsening chronic heart failure

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

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