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

Blood-biomarkers and devices for atrial fibrillation screening : Lessons learned from the AFRICAT (Atrial Fibrillation Research In CATalonia) study

AFRICAT is a prospective cohort study intending to develop an atrial fibrillation (AF) screening program through the combination of blood markers, rhythm detection devices, and long-term monitoring in our community. In particular, we aimed to validate the use of NT-proBNP, and identify new blood biomarkers associated with AF. Also, we aimed to compare AF detection using various wearables and long-term Holter monitoring. 359 subjects aged 65-75 years with hypertension and diabetes were included in two phases: Phase I (n = 100) and Phase II (n = 259). AF diagnosis was performed by baseline 12-lead ECG, 4 weeks of Holter monitoring (Nuubo TM), and/or medical history. An aptamer array including 1310 proteins was measured in the blood of 26 patients. Candidates were selected according to p-value, logFC and biological function to be tested in verification and validation phases. Several screening devices were tested and compared: AliveCor, Watch BP, MyDiagnostick and Fibricheck. AF was present in 34 subjects (9.47%). The aptamer array revealed 41 proteins with differential expression in AF individuals. TIMP-2 and ST-2 were the most promising candidates in the verification analysis, but none of them was further validated. NT-proBNP (log-transformed) (OR = 1.934; p<0.001) was the only independent biomarker to detect AF in the whole cohort. Compared to an ECG, WatchBP had the highest sensitivity (84.6%) and AUC (0.895 [0.780-1]), while MyDiagnostick showed the highest specificity (97.10%). The inclusion and monitoring of a cohort of primary care patients for AF detection, together with the testing of biomarkers and screening devices provided useful lessons about AF screening in our community. An AF screening strategy using rhythm detection devices and short monitoring periods among high-risk patients with high NT-proBNP levels could be feasible

Top table of the differential expressed proteins between AF and no AF

S1 Table: Top table of the differential expressed proteins between AF and no AF. Results from the discovery study.Results from the discovery study. Proteins selected to be verified in the whole Phase 1 are highlighted in grey. Proteins in bold but not highlighted were already tested in the whole phase 1 as part of a previous published work.Peer reviewe

Boxplot distribution of the 3 proteins selected for validation (NT-proBNP, ST-2 and TIMP-2) according to the methodology used for AF diagnosis

Peer reviewe

All patient data and biomarker measurements

Peer reviewe

Blood-biomarkers and devices for atrial fibrillation screening: Lessons learned from the AFRICAT (Atrial Fibrillation Research In CATalonia) study.

AFRICAT is a prospective cohort study intending to develop an atrial fibrillation (AF) screening program through the combination of blood markers, rhythm detection devices, and long-term monitoring in our community. In particular, we aimed to validate the use of NT-proBNP, and identify new blood biomarkers associated with AF. Also, we aimed to compare AF detection using various wearables and long-term Holter monitoring. 359 subjects aged 65-75 years with hypertension and diabetes were included in two phases: Phase I (n = 100) and Phase II (n = 259). AF diagnosis was performed by baseline 12-lead ECG, 4 weeks of Holter monitoring (NuuboTM), and/or medical history. An aptamer array including 1310 proteins was measured in the blood of 26 patients. Candidates were selected according to p-value, logFC and biological function to be tested in verification and validation phases. Several screening devices were tested and compared: AliveCor, Watch BP, MyDiagnostick and Fibricheck. AF was present in 34 subjects (9.47%). The aptamer array revealed 41 proteins with differential expression in AF individuals. TIMP-2 and ST-2 were the most promising candidates in the verification analysis, but none of them was further validated. NT-proBNP (log-transformed) (OR = 1.934; p The inclusion and monitoring of a cohort of primary care patients for AF detection, together with the testing of biomarkers and screening devices provided useful lessons about AF screening in our community. An AF screening strategy using rhythm detection devices and short monitoring periods among high-risk patients with high NT-proBNP levels could be feasible