Predictive model for atrial fibrillation in hypertensive diabetic patients

Background: Several scores to identify patients at high risk of suffering atrial fibrillation have been developed. Their applicability in hypertensive diabetic patients, however, remains uncertain. Our aim is to develop and validate a diagnostic predictive model to calculate the risk of developing atrial fibrillation at five years in a hypertensive diabetic population. Methods: The derivation cohort consisted of patients with both hypertension and diabetes attended in any of the 52 primary healthcare centres of Barcelona; the validation cohort came from the 11 primary healthcare centres of Terres de l'Ebre (Catalonia South) from January 2013 to December 2017. Multivariable Cox regression identified clinical risk factors associated with the development of atrial fibrillation. The overall performance, discrimination and calibration of the model were carried out. Results: The derivation data set comprised 54 575 patients. The atrial fibrillation rate incidence was 15.3 per 1000 person/year. A 5-year predictive model included age, male gender, overweight, heart failure, valvular heart disease, peripheral vascular disease, chronic kidney disease, number of antihypertensive drugs, systolic and diastolic blood pressure, heart rate, thromboembolism, stroke and previous history of myocardial infarction. The discrimination of the model was good (c-index = 0.692; 95% confidence interval, 0.684-0.700), and calibration was adequate. In the validation cohort, the discrimination was lower (c-index = 0.670). Conclusions: The model accurately predicts future atrial fibrillation in a population with both diabetes and hypertension. Early detection allows the prevention of possible complications arising from this disease

Machine Learning Approaches to Predict Major Adverse Cardiovascular Events in Atrial Fibrillation

The increasing prevalence of atrial fibrillation (AF) and its association with Major Adverse Cardiovascular Events (MACE) presents challenges in early identification and treatment. Although existing risk factors, biomarkers, genetic variants, and imaging parameters predict MACE, emerging factors may be more decisive. Artificial intelligence and machine learning techniques (ML) offer a promising avenue for more effective AF evolution prediction. Five ML models were developed to obtain predictors of MACE in AF patients. Two-thirds of the data were used for training, employing diverse approaches and optimizing to minimize prediction errors, while the remaining third was reserved for testing and validation. AdaBoost emerged as the top-performing model (accuracy: 0.9999; recall: 1; F1 score: 0.9997). Noteworthy features influencing predictions included the Charlson Comorbidity Index (CCI), diabetes mellitus, cancer, the Wells scale, and CHA2DS2-VASc, with specific associations identified. Elevated MACE risk was observed, with a CCI score exceeding 2.67 ± 1.31 (p p < 0.001), and an intermediate-risk Wells scale classification. Overall, the AdaBoost ML offers an alternative predictive approach to facilitate the early identification of MACE risk in the assessment of patients with AF

Early Diagnosis of Atrial Fibrillation and Stroke Incidence in Primary Care: Translating Measurements into Actions—A Retrospective Cohort Study

(1) Background: AF-related strokes will triple by 2060, are associated with an increased risk of cognitive decline, and alone or in combination, will be one of the main health and economic burdens on the European population. The main goal of this paper is to describe the incidence of new AF associated with stroke, cognitive decline and mortality among people at high risk for AF. (2) Methods: Multicenter, observational, retrospective, community-based studies were conducted from 1 January 2015 to 31 December 2021. The setting was primary care centers. A total of 40,297 people aged ≥65 years without previous AF or stroke were stratified by AFrisk at 5 years. The main measurements were the overall incidence density/1000 person-years (CI95%) of AF and stroke, prevalence of cognitive decline, and Kaplan–Meier curve. (3) Results: In total, 46.4% women, 77.65 ± 8.46 years old on average showed anAF incidence of 9.9/103/year (CI95% 9.5–10.3), associated with a four-fold higher risk of stroke (CI95% 3.4–4.7), cognitive impairment(OR 1.34 (CI95% 1.1–1.5)), and all-cause mortality (OR 1.14 (CI95% 1.0–1.2)), but there was no significant difference in ischemic heart disease, chronic kidney disease, or peripheral arteriopathy. Unknown AF was diagnosed in 9.4% and of these patients, 21.1% were diagnosed with new stroke. (4) Conclusions: The patients at high AF risk (Q4th) already had an increased cardiovascular risk before they were diagnosed with AF

Adjusted Morbidity Groups and Intracerebral Haemorrhage: A Retrospective Primary Care Cohort Study

Background: Intracerebral haemorrhage rates are increasing among highly complex, elderly patients. The main objective of this study was to identify modifiable risk factors of intracerebral haemorrhage. Methods: Multicentre, retrospective, community-based cohort study was conducted, including patients in the Adjusted Morbidity Group 4 with no history of intracerebral haemorrhage. Cases were obtained from electronic clinical records of the Catalan Institute of Health and were followed up for five years. The primary outcome was the occurrence of intracerebral haemorrhage during the study period. Demographic, clinical and pharmacological variables were included. Logistic regression analyses were carried out to detect prognostic variables for intracerebral haemorrhage. Results: 4686 subjects were included; 170 (3.6%) suffered an intracerebral haemorrhage (85.8/10,000 person&ndash;year [95% CI 85.4 to 86.2]). The HAS-BLED score for intracerebral haemorrhage risk detection obtained the best AUC (0.7) when used in the highest complexity level (cut-off point &ge;3). Associated independent risk factors were age &ge;80 years, high complexity and use of antiplatelet agents. Conclusions: The Adjusted Morbidity Group 4 is associated with a high risk of intracerebral haemorrhage, particularly for highly complex patients and the use of antiplatelet agents. The risk of bleeding in these patients must be closely monitored

Atrial Fibrillation and Dementia:A Report From the AF-SCREEN International Collaboration

Growing evidence suggests a consistent association between atrial fibrillation (AF) and cognitive impairment and dementia that is independent of clinical stroke. This report from the AF-SCREEN International Collaboration summarizes the evidence linking AF to cognitive impairment and dementia. It provides guidance on the investigation and management of dementia in patients with AF on the basis of best available evidence. The document also addresses suspected pathophysiologic mechanisms and identifies knowledge gaps for future research. Whereas AF and dementia share numerous risk factors, the association appears to be independent of these variables. Nevertheless, the evidence remains inconclusive regarding a direct causal effect. Several pathophysiologic mechanisms have been proposed, some of which are potentially amenable to early intervention, including cerebral microinfarction, AF-related cerebral hypoperfusion, inflammation, microhemorrhage, brain atrophy, and systemic atherosclerotic vascular disease. The mitigating role of oral anticoagulation in specific subgroups (eg, low stroke risk, short duration or silent AF, after successful AF ablation, or atrial cardiopathy) and the effect of rhythm versus rate control strategies remain unknown. Likewise, screening for AF (in cognitively normal or cognitively impaired patients) and screening for cognitive impairment in patients with AF are debated. The pathophysiology of dementia and therapeutic strategies to reduce cognitive impairment warrant further investigation in individuals with AF. Cognition should be evaluated in future AF studies and integrated with patient-specific outcome priorities and patient preferences. Further large-scale prospective studies and randomized trials are needed to establish whether AF is a risk factor for cognitive impairment, to investigate strategies to prevent dementia, and to determine whether screening for unknown AF followed by targeted therapy might prevent or reduce cognitive impairment and dementia

Atrial Fibrillation and Dementia: A Report From the AF-SCREEN International Collaboration

Growing evidence suggests a consistent association between atrial fibrillation (AF) and cognitive impairment and dementia that is independent of clinical stroke. This report from the AF-SCREEN International Collaboration summarizes the evidence linking AF to cognitive impairment and dementia. It provides guidance on the investigation and management of dementia in patients with AF on the basis of best available evidence. The document also addresses suspected pathophysiologic mechanisms and identifies knowledge gaps for future research. Whereas AF and dementia share numerous risk factors, the association appears to be independent of these variables. Nevertheless, the evidence remains inconclusive regarding a direct causal effect. Several pathophysiologic mechanisms have been proposed, some of which are potentially amenable to early intervention, including cerebral microinfarction, AF-related cerebral hypoperfusion, inflammation, microhemorrhage, brain atrophy, and systemic atherosclerotic vascular disease. The mitigating role of oral anticoagulation in specific subgroups (eg, low stroke risk, short duration or silent AF, after successful AF ablation, or atrial cardiopathy) and the effect of rhythm versus rate control strategies remain unknown. Likewise, screening for AF (in cognitively normal or cognitively impaired patients) and screening for cognitive impairment in patients with AF are debated. The pathophysiology of dementia and therapeutic strategies to reduce cognitive impairment warrant further investigation in individuals with AF. Cognition should be evaluated in future AF studies and integrated with patient-specific outcome priorities and patient preferences. Further large-scale prospective studies and randomized trials are needed to establish whether AF is a risk factor for cognitive impairment, to investigate strategies to prevent dementia, and to determine whether screening for unknown AF followed by targeted therapy might prevent or reduce cognitive impairment and dementia