Machine-learning approach to atrial fibrillation prediction among individuals without prior cardiovascular diseases.

BACKGROUND

There is a lack of atrial fibrillation (AF) prediction models tailored for individuals without prior cardiovascular diseases (CVDs) to facilitate early intervention. This study aimed to develop and validate an AF prediction model using machine-learning methods based on routine biomarkers in middle-aged individuals without overt CVD.

METHODS

Data were derived from 122 822 individuals in the Swedish AMORIS (Apolipoprotein-Mortality Risk) cohort who were aged 40-65 years and without CVD diagnosis at baseline (1985-96) and followed for 20 years for incident AF. The sample was split into training and validation data sets. Random forest was used to identify AF predictors from 16 routine biomarkers covering lipids, liver/kidney markers, glucose control and inflammation.

RESULTS

10 356 (8.4%) incident AF diagnosis occurred over a mean of 18.1 years (SD 4.4). Model performance increased sharply when adding the first seven predictors and plateaued when adding additional ones. Therefore, a final AF prediction model was established based on seven predictors: age, albumin, uric acid, triglycerides, glucose, alkaline phosphatase and sex. C-statistics of the final model were 0.82 (95% CI: 0.81 to 0.82) in the training and 0.71 (0.70 to 0.72) in the validation data set in predicting 20-year AF. The model was well-calibrated in the full sample and age and sex subgroups.

CONCLUSIONS

A new AF prediction model was established using seven biomarkers from a population without pre-existing CVDs, thus complementing currently available AF prediction models. These markers are readily accessible in primary and specialist care and demonstrate acceptable performance in predicting short- and long-term AF risk.