Novel Postoperative Atrial Fibrillation Risk Prediction from Preoperative High-Resolution ECG-Based Assessment of Nonlinear Heart Rate Dynamics

Objectives: We and others have shown that patients at risk of postoperative atrial fibrillation (POAF) after cardiac surgery have severe cardiac autonomic derangement and altered heart rhythm dynamics already preoperatively. Yet not excessive sympathetic activation alone, but rather excessive parasympathetic modulation orchestrating excessive sympathetic modulation paves the way to the development of POAF. The aim of this study is to explore, whether non-linear heart rate dynamics enables identification and prediction of POAF, superior and more reliable than conventional linear heart rate variability methods in our previous study.

Methods: A total of 179 consecutive patients scheduled for cardiac surgery were enrolled in our study prospectively. Eight were excluded, 4 died, and 17 had high ectopic activity not amenable to analysis, so 150 preoperative recordings represented the final study sample. High-resolution 20-minute ECG recordings were obtained 1 day before surgery to determine RR, PQ, and QT intervals as well as linear (time and frequency domain) and nonlinear heart rate variability parameters such as fractal dimension and detrended fluctuation analysis (DFA).

Results: 31 patients developed POAF after operation (POAF group), and 119 did not. The two groups were similar, except for more arterial hypertension as well as higher age, EuroSCORE II, and leukocyte count on the second postoperative day in POAF group. PQ intervals were shorter in the POAF group (156 ± 23 vs. 173 ± 31 milliseconds, p = 0.011). Among nonlinear parameters, DFAα1 was lower in the POAF group (0.95 ± 0.36 vs. 1.11 ± 0.30, p = 0.032). Most relevant predictors of POAF in the model were PQ Interval, DFAα1 and Age. The AUC of POAF prediction on train data was 88.2%, whereas leave-one-out cross-validation approach produced an AUC of 75.6%.

Conclusion: Determination of cardiac autonomic modulation and heart rhythm dynamics through digital ECG offers a platform for a true on-line prediction of POAF. We found, that nonlinear HRV parameter α 1 differentiated AF from non-AF group already preoperatively. Additionally, shortened PQ intervals irrespective of Heart Rate were associated with increased risk of POAF. Our prediction model applying advanced ECG and Dynamic Temporal analysis presently performs in the good- to - excellent range.

No conflict of interest has been declared by the author(s).