Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to improve the transparency and application of AI methods, with the potential to benefit patients in routine cardiovascular care. Following a clear research hypothesis, an AI-based workflow begins with data selection and pre-processing prior to analysis, with the type of data (structured, semi-structured, or unstructured) determining what type of pre-processing steps and machine-learning algorithms are required. Algorithmic and data validation should be performed to ensure the robustness of the chosen methodology, followed by an objective evaluation of performance. Seven case studies are provided to highlight the wide variety of data modalities and clinical questions that can benefit from modern AI techniques, with a focus on applying them to cardiovascular disease management.Despite the growing use of AI, further education for healthcare workers, researchers, and the public are needed to aid understanding of how AI works and to close the existing gap in knowledge. In addition, issues regarding data access, sharing, and security must be addressed to ensure full engagement by patients and the public. The application of AI within healthcare provides an opportunity for clinicians to deliver a more personalized approach to medical care by accounting for confounders, interactions, and the rising prevalence of multi-morbidity.

Background: There is a paucity of data comparing 30-day morbidity and mortality of sleeve gastrectomy (SG), Roux-en-Y gastric bypass (RYGB), and one anastomosis gastric bypass (OAGB). This study aimed to compare the 30-day safety of SG, RYGB, and OAGB in propensity score-matched cohorts. Materials and methods: This analysis utilised data collected from the GENEVA study which was a multicentre observational cohort study of bariatric and metabolic surgery (BMS) in 185 centres across 42 countries between 01/05/2022 and 31/10/2020 during the Coronavirus Disease-2019 (COVID-19) pandemic. 30-day complications were categorised according to the Clavien–Dindo classification. Patients receiving SG, RYGB, or OAGB were propensity-matched according to baseline characteristics and 30-day complications were compared between groups. Results: In total, 6770 patients (SG 3983; OAGB 702; RYGB 2085) were included in this analysis. Prior to matching, RYGB was associated with highest 30-day complication rate (SG 5.8%; OAGB 7.5%; RYGB 8.0% (p = 0.006)). On multivariate regression modelling, Insulin-dependent type 2 diabetes mellitus and hypercholesterolaemia were associated with increased 30-day complications. Being a non-smoker was associated with reduced complication rates. When compared to SG as a reference category, RYGB, but not OAGB, was associated with an increased rate of 30-day complications. A total of 702 pairs of SG and OAGB were propensity score-matched. The complication rate in the SG group was 7.3% (n = 51) as compared to 7.5% (n = 53) in the OAGB group (p = 0.68). Similarly, 2085 pairs of SG and RYGB were propensity score-matched. The complication rate in the SG group was 6.1% (n = 127) as compared to 7.9% (n = 166) in the RYGB group (p = 0.09). And, 702 pairs of OAGB and RYGB were matched. The complication rate in both groups was the same at 7.5 % (n = 53; p = 0.07). Conclusions: This global study found no significant difference in the 30-day morbidity and mortality of SG, RYGB, and OAGB in propensity score-matched cohorts.

Reduced left atrial PITX2 is associated with atrial cardiomyopathy and atrial fibrillation. PITX2 is restricted to left atrial cardiomyocytes in the adult heart. The links between PITX2 deficiency, atrial cardiomyopathy and atrial fibrillation are not fully understood. To identify mechanisms linking PITX2 deficiency to atrial fibrillation, we generated and characterized PITX2-deficient human atrial cardiomyocytes derived from human induced pluripotent stem cells (hiPSC) and their controls. PITX2-deficient hiPSC-derived atrial cardiomyocytes showed shorter and disorganised sarcomeres and increased mononucleation. Electron microscopy found an increased number of smaller mitochondria compared to the control. Mitochondrial protein expression was altered in PITX2-deficient hiPSC-derived atrial cardiomyocytes. Single-nuclear RNA-sequencing found differences in cellular respiration pathways and differentially expressed mitochondrial and ion channel genes in PITX2-deficient hiPSC-derived atrial cardiomyocytes. PITX2 repression in hiPSC-derived atrial cardiomyocytes replicated dysregulation of cellular respiration. Mitochondrial respiration was shifted to increased glycolysis in PITX2-deficient hiPSC-derived atrial cardiomyocytes. PITX2-deficient human hiPSC-derived atrial cardiomyocytes showed higher spontaneous beating rates. Action potential duration was more variable with an overall prolongation of early repolarization, consistent with metabolic defects. Gene expression analyses confirmed changes in mitochondrial genes in left atria from 42 patients with atrial fibrillation compared to 43 patients in sinus rhythm. Dysregulation of left atrial mitochondrial (COX7C) and metabolic (FOXO1) genes was associated with PITX2 expression in human left atria. In summary, PITX2 deficiency causes mitochondrial dysfunction and a metabolic shift to glycolysis in human atrial cardiomyocytes. PITX2-dependent metabolic changes can contribute to the structural and functional defects found in PITX2-deficient atria.

In patients with atrial fibrillation (AF), recurrent AF and sinus rhythm during follow-up are determined by interactions between cardiovascular disease processes and rhythm-control therapy. Predictors of attaining sinus rhythm at follow-up are not well known. To quantify the interaction between cardiovascular disease processes and rhythm outcomes, 14 biomarkers reflecting AF-related cardiovascular disease processes in 1586 patients in the EAST-AFNET 4 biomolecule study (71 years old, 46% women) were quantified at baseline. Mixed logistic regression models including clinical features were constructed for each biomarker. Biomarkers were interrogated for interaction with early rhythm control. Outcome was sinus rhythm at 12 months. Results were validated at 24 months and in external datasets. Higher baseline concentrations of three biomarkers were independently associated with a lower chance of sinus rhythm at 12 months: angiopoietin 2 (ANGPT2) (odds ratio [OR] 0.76 [95% confidence interval 0.65-0.89], p=0.001), bone morphogenetic protein 10 (BMP10) (OR 0.83 [0.71-0.97], p=0.017) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) (OR 0.73 [0.60–0.88], p=0.001). Analysis of rhythm at 24 months confirmed the results. Early rhythm control interacted with the predictive potential of NT-proBNP (p_interaction=0.033). The predictive effect of NT-proBNP was reduced in patients randomized to early rhythm control (usual care: OR 0.64 [0.51-0.80], p<0.001; early rhythm control: OR 0.90 [0.69-1.18], p=0.453). External validation confirmed that low concentrations of ANGPT2, BMP10 and NT-proBNP predict sinus rhythm during follow-up. Low concentrations of ANGPT2, BMP10 and NT-proBNP identify patients with AF who are likely to attain sinus rhythm during follow-up. The predictive ability of NT-proBNP is attenuated in patients receiving rhythm control.

Background: A recent subanalysis of the EAST-AFNET 4 (Early Treatment of Atrial Fibrillation for Stroke Prevention Trial) suggests a stronger benefit of early rhythm control (ERC) in patients with atrial fibrillation and a high comorbidity burden when compared to patients with a lower comorbidity burden. Methods: We identified 109 739 patients with newly diagnosed atrial fibrillation in a large United States deidentified administrative claims database (OptumLabs) and 11 625 patients in the population-based UKB (UK Biobank). ERC was defined as atrial fibrillation ablation or antiarrhythmic drug therapy within the first year after atrial fibrillation diagnosis. Patients were classified as (1) ERC and high comorbidity burden (CHA ₂ DS ₂ -VASc score ≥4); (2) ERC and lower comorbidity burden (CHA ₂ DS ₂ -VASc score 2–3); (3) no ERC and high comorbidity burden; and (4) no ERC and lower comorbidity burden. Patients without an elevated comorbidity burden (CHA ₂ DS ₂ -VASc score 0–1) were excluded. Propensity score overlap weighting and cox proportional hazards regression were used to balance patients and compare groups for the primary composite outcome of all-cause mortality, stroke, or hospitalization with the diagnoses heart failure or myocardial infarction as well as for a primary composite safety outcome of death, stroke, and serious adverse events related to ERC. Results: In both cohorts, ERC was associated with a reduced risk for the primary composite outcome in patients with a high comorbidity burden (OptumLabs: hazard ratio, 0.83 [95% CI 0.72–0.95]; P =0.006; UKB: hazard ratio, 0.77 [95% CI, 0.63–0.94]; P =0.009). In patients with a lower comorbidity burden, the difference in outcomes was not significant (OptumLabs: hazard ratio, 0.92 [95% CI, 0.54–1.57]; P =0.767; UKB: hazard ratio, 0.94 [95% CI, 0.83–1.06]; P =0.310). The comorbidity burden interacted with ERC in the UKB (interaction- P =0.027) but not in OptumLabs (interaction- P =0.720). ERC was not associated with an increased risk for the primary safety outcome. Conclusions: ERC is safe and may be more favorable in a population-based sample of patients with high a comorbidity burden (CHA ₂ DS ₂ -VASc score ≥4).

Atrial fibrillation (AF) and concomitant cardiometabolic disease processes interact and combine to lead to adverse events, such as stroke, heart failure, myocardial infarction, and cardiovascular death. Circulating biomolecules provide quantifiable proxies for cardiometabolic disease processes. The aim of this study was to test whether biomolecule combinations can define phenotypes in patients with AF. This pre-specified analysis of the EAST-AFNET 4 biomolecule study assigned patients to clusters using polytomous variable latent-class analysis based on baseline concentrations of 13 precisely quantified biomolecules potentially reflecting ageing, cardiac fibrosis, metabolic dysfunction, oxidative stress, cardiac load, endothelial dysfunction, and inflammation. In each cluster, rates of cardiovascular death, stroke, or hospitalization for heart failure or acute coronary syndrome, the primary outcome of EAST-AFNET 4, were calculated and compared between clusters over median 5.1 years follow-up. Findings were independently validated in a prospective cohort of 748 patients with AF (BBC-AF; median follow-up 2.9 years). Unsupervised biomolecule analysis assigned 1586 patients (71 years old, 46% women) into four clusters. The highest risk cluster was dominated by elevated bone morphogenetic protein 10, insulin-like growth factor–binding protein 7, N-terminal pro-B-type natriuretic peptide, angiopoietin 2, and growth differentiation factor 15. Patients in the lowest risk cluster showed low concentrations of these biomolecules. Two intermediate-risk clusters differed by high or low concentrations of C-reactive protein, interleukin-6, and D-dimer. Patients in the highest risk cluster had a five-fold higher cardiovascular event rate than patients in the low-risk cluster. Early rhythm control was effective across clusters (P_interaction = 0.63). Sensitivity analyses and external validation in BBC-AF replicated clusters and risk gradients. Biomolecule concentrations identify cardiometabolic subphenotypes in patients with AF at high and low cardiovascular risk. The quantification of 13 biomolecules pre-selected for their ability to provide quantitative proxies for cardiovascular disease processes assigns patients with recently diagnosed atrial fibrillation and comorbidities to four clusters. The main biomolecules contributing to the clustering process are N-terminal pro-B-type natriuretic peptide, insulin-like growth factor–binding protein 7, bone morphogenetic protein 10, angiopoietin 2, growth differentiation factor 15, interleukin-6, and C-reactive protein. These biomolecules explain 66–85% of the assignment to a cluster. The four clusters differ in their risk of cardiovascular events (cardiovascular death, stroke, or unplanned hospitalisation for heart failure or acute coronary syndrome) over a 5-year time horizon. Early rhythm control therapy is effective in all four clusters (P_interaction = 0.63). The clusters and their associated risk are replicated in an independent cohort.