Prediction model for thyrotoxic atrial fibrillation: a retrospective study
| Autor: | Alina Y. Babenko, Sergey V. Kovalchuk, Ilia V. Derevitskii, Daria Aleksandrovna Ponomartseva |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Male
medicine.medical_specialty Premature atrial contraction Endocrinology Diabetes and Metabolism Graves' disease 030204 cardiovascular system & hematology Individual risk Thyrotoxic atrial fibrillation Hyperthyroidism Risk Assessment Diseases of the endocrine glands. Clinical endocrinology 03 medical and health sciences 0302 clinical medicine Heart Rate Risk Factors Prediction model Internal medicine Heart rate Machine learning medicine Humans 030212 general & internal medicine Extreme gradient boosting Retrospective Studies Models Statistical business.industry Research Retrospective cohort study Atrial fibrillation General Medicine Middle Aged medicine.disease Prognosis RC648-665 Thyrotoxicosis Case-Control Studies Cardiology Female business Risk assessment Graves’ disease Follow-Up Studies |
| Zdroj: | BMC Endocrine Disorders, Vol 21, Iss 1, Pp 1-14 (2021) BMC Endocrine Disorders |
| ISSN: | 1472-6823 |
| Popis: | BackgroundThyrotoxic atrial fibrillation (TAF) is a recognized significant complication of hyperthyroidism. Early identification of the individuals predisposed to TAF would improve thyrotoxic patients’ management. However, to our knowledge, an instrument that establishes an individual risk of the condition is unavailable. Therefore, the aim of this study is to build a TAF prediction model and rank TAF predictors in order of importance using machine learning techniques.MethodsIn this retrospective study, we have investigated 36 demographic and clinical features for 420 patients with overt hyperthyroidism, 30% of which had TAF. At first, the association of these features with TAF was evaluated by classical statistical methods. Then, we developed several TAF prediction models with eight different machine learning classifiers and compared them by performance metrics. The models included ten features that were selected based on their clinical effectuality and importance for model output. Finally, we ranked TAF predictors, elicited from the optimal final model, by the machine learning tehniques.ResultsThe best performance metrics prediction model was built with the extreme gradient boosting classifier. It had the reasonable accuracy of 84% and AUROC of 0.89 on the test set. The model confirmed such well-known TAF risk factors as age, sex, hyperthyroidism duration, heart rate and some concomitant cardiovascular diseases (arterial hypertension and conjestive heart rate). We also identified premature atrial contraction and premature ventricular contraction as new TAF predictors. The top five TAF predictors, elicited from the model, included (in order of importance) PAC, PVC, hyperthyroidism duration, heart rate during hyperthyroidism and age.ConclusionsWe developed a machine learning model for TAF prediction. It seems to be the first available analytical tool for TAF risk assessment. In addition, we defined five most important TAF predictors, including premature atrial contraction and premature ventricular contraction as the new ones. These results have contributed to TAF prediction investigation and may serve as a basis for further research focused on TAF prediction improvement and facilitation of thyrotoxic patients’ management. |
| Databáze: | OpenAIRE |
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