Mathematical Models for the Prediction of Coagulation Activity in Patients with Paroxysmal Atrial Fibrillation
Krasimira Prodanova1, Mariya Negreva2
1Krasimira Prodanova, B.Sc: Technical University of Sofia, Faculty of applied mathematics and informatics, Sofia, Bulgaria;
2Mariya Negreva*, Department of Cardiology, Medical University of Varna, First clinic of cardiology, Varna University Hospital “St. Marina”, Varna, Bulgaria;
Manuscript received on September 01, 2020. | Revised Manuscript Received on September 21, 2020.|. Manuscript published on September 20, 2020. | PP: 1-6 | Volume-3 Issue-2, September 2020. | Retrieval Number: 100.1/ijbsac.B0196083220 | DOI: 10.35940/ijbsac.B0196.099320
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© The Authors. Published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP).. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Our previous studies showed activation of coagulation in the early hours of the clinical manifestation of paroxysmal atrial fibrillation (PAF). Plasma coagulation activity of factor II, factor V, factor VII, factor VIII, factor IX, factor X, factor XI, factor XII, vWF, tissue factor levels, FVIII, vWF, prothrombin fragment 1+2(F1 + 2) and fibrinopeptide A (FPA) were significantly increased as early as the first twenty-four hours of the disease. The results suggest that there is a correlation between the studied parameters and development of the disease. Aim: To search for a statistical model that predicts coagulation activity in PAF patients. Materials and methods: Coagulation parameters were examined in 51 PAF patients (26 males, 25 females; mean age 59.84 ±1.60 years, onset of PAF episode < 24h prior to hospitalization). Controls included 52 individuals (26 males, 26 females; mean age 59.50 ± 1.46 years) with no prior anamnestic or ECG AF data, corresponding to patients in sex, age, BMI and comorbidities. A linear regression model was used to predict coagulation activity in PAF. Regression models showed good correlation between the duration of arrhythmia and six of the fourteen coagulation parameters studied: F1+2 (r = 0.83, p <0.001), FPA (r = 0.84, p <0.001), FVIII levels (r = 0.85, p <0.001) as well as activity of FII (r = 0.83, p <0.001), FVIII (r = 0.83, p <0.001) and FXII (r = 0.78, p <0.001). Changes in F1+2 plasma levels were most sensitive to PAF duration, where the contribution of duration to the values of the indicator is the greatest (b = 15.31). Conclusion: Linear regression analysis allowed us to create models with a high correlation coefficient for predicting the values of F1+2, FPA, FVIII levels, as well as activity of FII, FVIII and FXII in PAF patients. These models could allow for quantification of the procoagulatory process and thrombotic potential of the disease.
Keywords: coagulation, atrial fibrillation, predictive models.