Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 12 appraisals
PloS one
A novel dynamic Optuna hybrid Harris Hawks Optimization approach for classification of CAD
Coronary Artery Disease (CAD) is a leading cause of mortality worldwide and is primarily associated with atherosclerotic plaque formation, resulting in coronary artery stenosis. The accurate prediction of CAD using deep learning models is often constrained by the limitations of conventional optimization techniques, including premature convergence and limited adaptability of the models. To address these challenges, this study proposes a Dynamic Optuna Hybrid Harris Hawks Optimization (Dynamic Optuna H-HHO) framework to enhance the performance of deep learning-based CAD prediction models. The proposed approach integrates dynamic parameter adjustment, adaptive escape energy mechanisms, and Optuna-based hyperparameter tuning. The framework was applied to optimize several deep-learning classifiers, including ResNet-50, VGG-16, InceptionV3, and MobileNet, using a coronary artery stenosis dataset. The performance was evaluated through a comparative analysis with models optimized using the conventional Hybrid Harris Hawks Optimization (H-HHO) algorithm. The experimental results indicate that the proposed Dynamic Optuna H-HHO framework consistently improves the predictive accuracy across all evaluated models. InceptionV3 achieved the highest accuracy of 97.9%, followed by MobileNet with 97.6%, compared with the maximum accuracy of 82.46% obtained using traditional HHO-based optimization. By combining adaptive optimization strategies with automated hyperparameter tuning, the proposed framework provides a robust and scalable solution for improving the accuracy of coronary artery disease prediction.
20 July 2026
Read appraisal →Annals of medicine
Predicting major adverse cardiovascular and cerebrovascular events in chronic heart failure: a machine learning study
BACKGROUND: Heart failure (HF) is a clinical syndrome characterized by impaired cardiac diastolic and systolic function due to structural or functional damage to the myocardium. HF represents the end-stage manifestation of many cardiac diseases. Therefore, early identification of high-risk patients is crucial. This study aims to utilize machine learning (ML) methods to develop and validate a model to predict major adverse cardiovascular and cerebrovascular events (MACCE) in patients with HF and identify its key predictive features. METHODS: This study is a retrospective cohort study. We enrolled a total of 271 patients, who were divided into training and testing sets. Baseline data, including cardiopulmonary exercise testing (CPET) parameters and laboratory tests, were collected for all participants. Based on the presence or absence of MACCE during follow-up, they were categorized into a No-event group and MACCE group. We developed seven ML models to predict the incidence of MACCE in patients with chronic heart failure (CHF) using CPET parameters. The predictive performance of these models was systematically compared, and model interpretability was evaluated using Shapley Additive exPlanations (SHAP). Subsequently, retaining only those with HF with preserved ejection fraction (HFpEF) for a sensitivity analysis. Additionally, a subgroup analysis was conducted between No-event group and Worsening HF (WHF) group. RESULTS: We used Boruta feature selection, four important predictive features were identified. Among the ML models constructed with these features, the Categorical Boosting (CatBoost) model demonstrated the best performance. SHAP analysis was applied to interpret the optimal model, revealing that lower values of heart rate recovery at 1 min (HRR1), as well as a higher carbon dioxide ventilation equivalent slope (VE/VCO2 slope), were associated with higher SHAP values-indicating greater importance in predicting adverse outcomes. CONCLUSION: HRR1 and VE/VCO2 slope can serve as independent predictors for the incidence of MACCE in patients with CHF. Both impaired HRR1 and an elevated VE/VCO2 slope serve as significant independent predictors of MACCE in the CHF population.HRR1 is an independent predictor of WHF in patients with CHF.
14 June 2026
Read appraisal →Heart (British Cardiac Society)
Natural history of asymptomatic moderate or severe aortic regurgitation: a systematic review and meta-analysis
BACKGROUND: The natural history of asymptomatic moderate or severe aortic regurgitation (AR) remains uncertain, with conflicting reports about its progression and surgical timing. We aimed to quantify adverse outcomes under conservative management and evaluate the association of aortic valve replacement/repair (AVR) with mortality. METHODS: Systematic searches (inception-July 2025) identified cohort studies of asymptomatic moderate/severe AR. Random-effects models estimated pooled incidence rates of adverse events; fixed-effects models were used for hazard ratios (HRs) of AVR vs conservative management. RESULTS: Twenty-seven studies (4720 patients; mean age 49 years; mean follow-up 3.9 years) were included. Pooled incidence rates per 100 person-years were 1.75 (95% CI 1.27 to 2.41) for all-cause mortality, 1.29 for cardiac death, 0.29 for sudden death, 4.30 for new symptoms and 7.01 for AVR. Asymptomatic low left ventricular ejection fraction occurred in only 0.9 per 100 person-years. Mortality rates were more than double those of the general population across age groups-2.45 (1.90 to 3.18) per 100 person-years for cohorts with mean age ≥50 years versus 0.59 (0.29 to 1.21) for younger cohorts. Early AVR was associated with lower mortality (pooled HR 0.33; 95% CI 0.30 to 0.37). CONCLUSION: Asymptomatic moderate/severe AR carries significant excess mortality irrespective of age, contradicting its historically benign reputation. Given the rarity of asymptomatic LV dysfunction, earlier intervention guided by more sensitive markers of LV damage may improve outcomes, although heterogeneity and study quality warrant cautious interpretation. PROSPERO REGISTRATION NUMBER: CRD42024522683.
27 May 2026
Read appraisal →European heart journal
Surgical left atrial appendage occlusion in valvular heart disease without atrial fibrillation: the OPINION trial
BACKGROUND AND AIMS: While surgical left atrial appendage occlusion (SLAAO) reduces stroke in atrial fibrillation (AF) patients, its efficacy in patients without pre-operative AF but with CHA₂DS₂-VASc ≥2 remains uncertain despite their high post-operative AF risk (15-54%). The aim of this study was to evaluate whether prophylactic SLAAO reduces post-operative thrombo-embolic events in valvular surgery patients. METHODS: The OPINION was a multicentre, open-label, randomized, superiority trial conducted at three cardiac surgery centres in China. Eligible non-AF patients with CHA₂DS₂-VASc ≥2 and an indication for valve repair or replacement due to mitral or aortic valve lesions were randomly assigned (1:1) to undergo SLAAO (intervention arm) or not undergo SLAAO (control arm) during surgery. The primary outcome was a composite of ischaemic stroke, transient ischaemic attack (TIA), or cardiovascular mortality assessed at 1 year. The primary analysis was done in the intention-to-treat population. RESULTS: Between April 2021 and June 2024, a total of 2157 patients were enrolled and randomized. After exclusion of 39 patients who withdrew informed consent, 2118 participants were included in the intention-to-treat population (1062 in the SLAAO group and 1056 in the control group). Baseline characteristics were well-balanced between the SLAAO group and control group (mean age 55.5 [11.4] vs 55.6 [11.5] years, P = .65; female 32.9% vs 32.3%, P = .78; CHA2DS2-VASc score 2.88 [0.98] vs 2.87 [0.96], P = .83; median EuroSCORE II 1.58% [1.42%] vs 1.56% [1.28%], P = .74). The 1-year primary endpoint occurred in 73 (6.9%) patients in the SLAAO group and in 87 (8.2%) patients in the control group (hazard ratio 0.83; 95% confidence interval 0.61-1.14; P = .25). CONCLUSIONS: For valvular surgery patients with CHA₂DS₂-VASc scores ≥2 but no pre-operative AF, routine prophylactic left atrial appendage closure did not significantly reduce the incidence of the primary composite endpoint (ischaemic stroke, TIA, and cardiovascular mortality) at 1-year follow-up. TRIAL REGISTRATION: ChiCTR.org registry ChiCTR2100042238.
22 May 2026
Read appraisal →BMC medical imaging
Development and validation of a novel deep learning coronary artery plaque quantification model
BACKGROUND: Coronary Computed Tomography Angiography (CCTA) is an established tool for assessing coronary artery disease. CCTA determined total coronary artery plaque volume predicts cardiovascular events, but manual quantification is impractical for routine use. Deep learning-based methods offer a promising solution for automated plaque volume assessment. METHODS: We developed and validated a novel software, QuantiPlaque, powered by deep learning models, for automated segmentation of total and calcified coronary plaque volume. Expert manual annotation served as the reference standard. Correlations between deep learning and expert segmentation were evaluated using intraclass correlation coefficient (ICC), Pearson's r, and Spearman's rho, and agreements were evaluated with Bland-Altman analyses. RESULTS: A total of 115 CCTA scans were included. Mean (range) age was 58 (51-64) and 51% were females. The mean coronary artery calcium score was 56 Agatston units, ranging from 0 to 601. The model demonstrated strong correlation and agreement with expert annotation for per-patient total plaque volume (ICC 0.95, mean difference - 8.35 mm3, 95% limit of agreement - 102 to 85 mm3, Spearman's rho 0.87, Pearson's r 0.95) and calcified plaque volume (ICC 0.90, mean difference - 0.28 mm3, 95% limits of agreement - 21.97 to 21.42 mm3, Spearman's rho 0.93, Pearson's r 0.90). Per-vessel analysis showed strong correlation in the left anterior descending artery and right coronary artery but was weaker in the left circumflex artery territory. CONCLUSION: The evaluated deep learning model provides accurate quantification of total and calcified plaque burden, with strong correlation and agreement to expert annotation.
16 May 2026
Read appraisal →EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology
Derivation and external validation of a deep learning model to predict changes in coronary plaque burden
BACKGROUND: Predicting the progression/regression of coronary plaque burden is challenging. AIMS: We aimed to develop a deep learning model to forecast changes in percent atheroma volume (ΔPAV) using intravascular ultrasound (IVUS). METHODS: We analysed data from IBIS-4 and PACMAN-AMI. Core lab measurements of plaque burden were available from IVUS pullbacks. Each model consists of a bidirectional Long Short-Term Memory (biLSTM) layer followed by two fully connected layers with one neuron each, resulting in both a classification for input progression/regression and an estimation of the ΔPAV. RESULTS: For the derivation and validation, a total of 1,960 regions of interest (ROIs) from the IBIS-4 dataset were used. The mean±standard deviation of the model accuracy was 0.85±0.02, the Matthews correlation coefficient was 0.70±0.04, and the F1 score was 0.85±0.02 for both progression and regression classes. In the testing (external validation) process with the PACMAN-AMI dataset, 5,283 ROIs were utilised. The mean ΔPAV was -0.31±5.63, for which 2,665 featured regression with a mean ΔPAV of -4.57±3.73, and 2,618 presented progression with a mean ΔPAV of 4.02±3.55, representing 49.6% of plaque progression prevalence. The predictive performance across the 100 trained models in the testing dataset showed an accuracy of 0.84, a Matthews correlation coefficient of 0.68, and an F1 score for the progression and regression classes of 0.84. CONCLUSIONS: This is the first deep learning model capable of detecting changes in plaque progression by analysing the rate of plaque burden change between adjacent frames.
10 May 2026
Read appraisal →European heart journal. Cardiovascular Imaging
Additive prognostic value of functional performance to coronary artery anatomy: the ISCHEMIA trial.
AIMS: To assess whether baseline functional performance assessed by exercise treadmill stress testing (EST) has additive value to coronary computed tomography angiography (CCTA) for risk stratification among patients with chronic coronary disease (CCD) and moderate or severe ischaemia. METHODS AND RESULTS: We performed a subgroup analysis of the ISCHEMIA trial including participants who underwent EST and CCTA. EST data and severity of coronary artery disease (CAD) on CCTA were evaluated by core laboratories, blinded to clinical data and results of the other tests. The primary outcome for this analysis was all-cause death. Secondary outcomes were cardiovascular death, cardiovascular death or myocardial infarction (MI), MI and a composite of cardiovascular death, MI, or hospitalization for heart failure, unstable angina, or resuscitated cardiac arrest. EST and the number of vessels diseased on CCTA were both interpretable in 1864 patients (median age 62 years, IQR 55-68, 83% males). During a median follow-up of 3.1 years, 69 patients died. Higher peak metabolic equivalents (METs) achieved on the qualifying stress test was associated with lower all-cause death (HR 0.86, 95% CI 0.76-0.98; P = 0.025). The addition of peak METs to CAD severity improved the predictive ability of the all-cause death and CV death models by 10-20% and 8-13% respectively, depending on the metrics used for CCTA. Adding peak METs to CCTA anatomical models resulted in better prediction of MI by 11-17%, cardiovascular death or MI by 10-14%, and 5-component composite outcome by 12-16%. CONCLUSION: Peak METs on EST, a marker of functional performance, added prognostic value to models including CCTA anatomical findings in patients with CCD and moderate or severe ischaemia.
3 May 2026
Read appraisal →Circulation
Memantine for Premature Atrial Contractions: A Phase 2 Randomized Clinical Trial
BACKGROUND: Premature atrial contractions (PACs) are independently associated with atrial fibrillation, stroke, and heart failure, yet no pharmacological therapy is approved for PAC suppression. Experimental studies have identified a functional cardiac glutamatergic system in which N-methyl-D-aspartate receptors regulate atrial electrophysiology. Preclinical studies show that pharmacological antagonism of N-methyl-D-aspartate receptors with memantine suppresses atrial arrhythmias. METHODS: We conducted an investigator-initiated, phase 2, multicenter, randomized, double-blind, placebo-controlled trial. Symptomatic adults with frequent PACs (≥1000/24 h) were randomly assigned to receive memantine or placebo for 6 weeks. The primary end point was the percentage change in mean 24-hour PAC count from baseline to the end of treatment. The primary analysis was performed in the intention-to-treat population. Prespecified secondary end points included the responder rate (≥50% PAC reduction), percentage change in nonsustained atrial tachycardia burden, and cumulative incidence of new-onset atrial fibrillation. RESULTS: Among 241 patients included in the efficacy analysis, memantine resulted in a greater reduction in PAC count than placebo (between-group difference, 47.1 percentage points; P=0.0045). The responder rate was higher with memantine than with placebo (52.4% versus 23.1%; P<0.0001). Memantine also reduced nonsustained atrial tachycardia burden (between-group difference, 30.98 percentage points; P=0.0043) and was associated with a lower cumulative incidence of new-onset atrial fibrillation (4.8% versus 23.9%; P<0.0001). No clinically meaningful differences were observed in electrocardiographic intervals or left ventricular function, and no drug-related serious adverse events occurred. CONCLUSIONS: In patients with frequent symptomatic PACs, memantine reduced atrial ectopy and atrial tachyarrhythmia burden and demonstrated a favorable safety profile. These findings provide proof of concept for a novel, non-ion channel-based therapeutic strategy targeting the cardiac glutamatergic system. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT06501638.
23 Apr 2026
Read appraisal →Journal of ethnopharmacology
Efficacy of Yuxintong capsule on cardiorespiratory fitness in patients with stable coronary heart disease: A multicentre, randomized, double-blind, placebo-controlled, parallel-group clinical trial
ETHNOPHARMACOLOGICAL RELEVANCE: Yuxintong capsule, a traditional Chinese patent medicine, has been widely used in the treatment of coronary heart disease (CHD). However, whether it can improve cardiopulmonary fitness (CRF) in patients with CHD remains unclear. AIM OF THE STUDY: To evaluate the efficacy and safety of Yuxintong capsule in CRF in patients with stable CHD. MATERIALS AND METHODS: A multicentre, randomized, double-blind, placebo-controlled, parallel-group trial was conducted in 13 hospitals in China from March 2021 to November 2021, and 404 patients with stable CHD were randomly assigned to receive either Yuxintong capsule and conventional Western medicine (Yuxintong group) or placebo and conventional Western medicine (placebo group) in a 1:1 ratio for 12 weeks (ChiCTR2000041293, December 23, 2020). The primary outcomes were peak oxygen uptake (VO2peak) and anaerobic threshold (AT). The secondary outcomes were metabolic equivalents (METs), duration and onset time of 1-mm ST-segment depression on electrocardiography (ECG), and 36-Item Short Form Health Survey (SF-36) scores. The main safety indicators included complete blood count, urinalysis, liver function, renal function, coagulation function, and 12-lead electrocardiogram. And adverse events were recorded throughout the trial. RESULTS: 369 patients, 187 in the Yuxintong group and 182 in the placebo group, were finally included for analysis. Compared with placebo group, 12-week treatment with Yuxintong showed an improvement in AT (0.98 ± 2.88 vs. 0.12 ± 2.79 mL kg-1·min-1; LSMD, 0.80 [95 % CI: 0.28 to 1.32]; P < 0.025), and general health (13.61 ± 15.13 vs. 8.29 ± 14.14; LSMD 5.30, [95 % CI: 2.33 to 8.31]; P < 0.05) and vitality scores (12.46 ± 11.32 vs. 6.01 ± 11.41; LSMD 6.43, [95 % CI: 4.13 to 8.31]; P < 0.05) on SF-36 scale. The other outcomes did not significantly differ between the two groups. The adverse events were similar between the two groups. CONCLUSIONS: Yuxintong capsule, as an adjunctive therapy to conventional treatment, improved AT, and general health and vitality scores on SF-36 scale in patients with stable CHD. However, the improvements in VO2peak, METs, and the duration and onset time of 1-mm ST-segment depression on ECG were not statistically significant.
6 Apr 2026
Read appraisal →European journal of cardiovascular nursing
A review and promise of digital health technologies to promote global cardiovascular health
Digital health technologies include an array of mobile or digital devices and apps that may be used by patients to support lifestyle modification or medical apps that monitor vital parameters and provide data that may influence treatment decisions. The use of digital health technologies is a growing opportunity for disease prevention and management of cardiovascular disease (CVD) for both patients to engage in self-management as well as nurses to monitor patients remotely and facilitate clinical decision support. The aims of this review article are to provide an overview of pertinent digital health technologies in CVD care, address social determinants of health and other enablers/barriers to digital health implementation, and provide perspectives on novel models of care incorporating digital technologies. Based on the latest evidence, recommendations for special considerations and implementation of digital health technologies in low- and middle- vs high-income countries are presented. In addition, tables reviewing functionality of the digital tools and global examples of their use are provided. Extensive empirical evidence from diverse populations suggests adoption of these innovative tools has been low in clinical settings. We provide a table summarizing these barriers and include the facilitators that can improve the adoption of digital health tools to improve behavioral lifestyle modification. For learning purpose, implementation of an innovative model of care is illustrated graphically and in text. We include a call to action to achieve the maximum impact of digital technology to support nurses who work in almost all settings improving CVD prevention and management globally. The multiple roles of nurses using digital health for global CVD prevention and management are discussed in the settings of clinical practice, research, education, public health, health policy/leadership, and informatics.
5 Apr 2026
Read appraisal →Heart rhythm
External validation of an explainable electrocardiogram-only deep learning algorithm for the prediction of response after cardiac resynchronization therapy
BACKGROUND: Cardiac resynchronization therapy (CRT) can improve clinical outcomes in patients with dyssynchronous heart failure, but many patients selected according to the current guidelines do not respond. OBJECTIVE: This study aimed to externally validate an explainable deep learning algorithm (the FactorECG algorithm) for predicting response after biventricular pacing. METHODS: We previously trained a deep learning algorithm on >1 million electrocardiogram (ECG) median beats to learn the underlying generative factors of the ECG and applied it to 1306 patients with CRT from the Netherlands. Using the extracted 21 explainable factors, a model predicting the risk of volumetric nonresponse and poor clinical outcomes was developed. In the present analysis, this model was externally validated in a cohort of 161 patients with CRT from the University of Virginia for volumetric nonresponse only. Subsequently, the added value of clinical and cardiac magnetic resonance imaging-derived predictors was investigated. RESULTS: The original model significantly outperformed American Heart Association criteria for left bundle branch block for the prediction of nonresponse {C-statistic 0.67 (95% confidence interval [CI] 0.59-0.76) vs 0.51 (95% CI 0.41-0.60), respectively}. A refitted FactorECG-based model performed similarly to a model also integrating indices of mechanical dyssynchrony (C-statistic 0.74 [95% CI 0.66-0.82] vs 0.70 [95% CI 0.62-0.77], respectively). A combination of both models improved response prediction (C-statistic 0.79 [95% CI 0.71-0.85]). CONCLUSION: In this external validation study, an explainable ECG-only algorithm for the prediction of nonresponse after CRT device implantation generalized well to a lower-risk population from a different hospital. Adding indices of mechanical dyssynchrony and right ventricular function might be of additional value when evaluating volumetric response.
4 Apr 2026
Read appraisal →Eur Heart J
Coronary revascularization: a long-term perspective.
This paper will forecast advancements in coronary revascularization by 2040, drawing on historical trends and recent breakthroughs. Having forecasted in 2000 the impact of innovations like drug-eluting stents, coronary computed tomography angiography, and bioresorbable scaffolds, the authors examine the evolving landscape of coronary artery disease treatment emphasizing artificial intelligence and omics sciences. Imagenomics-the integration of imaging and omics-will be a transformative tool with artificial intelligence enabling more personalized decisions between pharmacological and mechanical revascularization. The shift towards minimally invasive, image-guided procedures is discussed along with the potential obsolescence of current antiplatelet and anticoagulant therapies with novel biomimetic peptides like CD31-covalently bound to metallic or polymeric devices. Advances in photon-counting computed tomography and Fibre Optic Real Shape promise high-resolution imaging with lower radiation, enhancing procedural safety and diagnostic accuracy. It is anticipated that robotics and 3D holograms in percutaneous and surgical revascularization will improve precision and the resulting outcomes. Additionally, the need for mechanical revascularization for younger patients may decline due to effective plaque regression therapies and novel potent anti-atherogenic biologics. However, the aging population will drive demand for mechanical interventions, with advancements in atherectomy and lithotripsy techniques improving outcomes in complex, calcified lesions. This comprehensive analysis outlines a future where myocardial revascularization becomes increasingly personalized, with technology-driven interventions redefining cardiovascular medicine.
1 Feb 2026
Read appraisal →