Predictive Value of P-Wave Dispersion and P-Wave Peak Time for Development of Postoperative Atrial Fibrillation in Renal Transplant Recipients

Introduction

Atrial fibrillation (AF) is a relatively common postoperative complication, particularly following major surgeries. While it is most frequently observed after cardiac surgery, it can also occur in varying rates after non-cardiac surgeries [1,2]. Postoperative atrial fibrillation (POAF), once considered a relatively benign and transient event, is now recognized as a clinically significant complication. Its occurrence has been associated with increased in-hospital mortality, prolonged length of stay, a higher risk of stroke and myocardial infarction, and higher long-term risk of stroke and all-cause mortality [3,4].

The growing body of compelling evidence linking POAF to increased morbidity and mortality has prompted considerable interest in investigating the underlying factors that can help predict its onset. Understanding these risk factors is essential for early identification and prevention strategies. For example, factors such as the nature of the surgical procedure, age, and underlying comorbidities have all been identified as important contributors to the risk of developing POAF [5,6]. Beyond clinical risk factors, electrocardiography (ECG) provides valuable insights in POAF, and several studies have demonstrated that specific ECG parameters are associated with an increased risk of its development. P-wave amplitude, P-wave index, PR interval, and P-wave axis are among the ECG parameters that are significantly associated with the development of POAF [7–10]. In addition, P-wave dispersion following coronary artery bypass graft (CABG) surgery has been shown to be a significant predictor of POAF [11].

Renal transplantation (RT) is generally considered the most effective form of renal replacement therapy for individuals with end-stage renal disease (ESRD), as it tends to offer reduction in mortality and cardiovascular outcomes, enhanced quality of life, and greater long-term cost efficectiveness compared to ongoing dialysis treatment. According to data from the European Renal Association, RT rates across Europe have exhibited a steady upward trend over the past several years, with an average annual increase of approximately 2%. This gradual yet significant growth has resulted in transplantation rates approaching 35 procedures per million population [12–14]. In renal transplant recipients, AF is associated with an increased risk of mortality, allograft failure, and stroke [15].

Based on the aforementioned scientific evidence, this study investigated whether P-wave dispersion (PWD) and P-wave peak time (PWPT) – parameters derived from surface ECG – can predict the development of POAF in renal transplant recipients.

Material and Methods

STUDY POPULATION:

Between January 2022 and January 2024, consecutive patients who underwent RT were retrospectively included in our study. The inclusion criteria comprised renal transplant patients aged 18 years and older. Patients with a documented history of AF, incomplete electrocardiographic data, history of moderate-to-severe valvular heart disease, those lost to follow-up, and those refusing to participate were systematically excluded from the study cohort.

DATA COLLECTION:

Demographic, clinical, electrocardiographic, and echocardiographic variables were systematically extracted from hospital records. The patient population was divided into 2 groups based on the presence (POAF +) or absence (POAF −) of postoperative atrial fibrillation, and comparisons were made between these groups.

Preoperative ECGs were recorded at a paper speed of 25 mm/s and a calibration of 10 mm/mV. The following parameters were manually measured by 2 independent observers blinded to the clinical data to ensure accuracy and consistency: Preoperative P-wave dispersion (preopPWD) was defined as the difference between maximal and minimal P-wave durations across all 12 leads. P-wave peak time in lead DII (PWPTDII) was defined as the interval from the onset of the P-wave to its peak in lead DII on the ECG. P-wave peak time in lead V1 (PWPTV1) was defined as the duration from the onset of the P-wave to its peak in lead V1 on the ECG. QRS duration was defined as the interval between the onset and the end of the QRS complex. PR interval was defined as the interval between the onset of the P-wave and the beginning of the QRS. QTc was defined as the heart rate-corrected QT interval on the ECG, calculated using formulas such as Bazett’s formula [16].

Preoperative transthoracic echocardiography results were reviewed to evaluate cardiac structure and function, including left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVDd), left atrial diameter (LA diameter), and systolic pulmonary artery pressure (sPAP).

STATISTICAL ANALYSIS:

Continuous variables are presented as mean±standard deviations and were compared between groups using the independent samples t test for normally distributed data or the Mann–Whitney U test for non-normally distributed data. Categorical variables are expressed as frequencies and percentages and were compared using the chi-square test. Univariate analyses were conducted to identify variables associated with POAF. Variables with a P<0.05 in univariate analysis were subsequently considered for inclusion in penalized logistic regression model to determine independent predictors of POAF. However, due to significant collinearity among preopPWD, PWPTDII, and PWPTV1, only preopPWD was included in the penalized regression analysis. Statistical significance was set at a P value <0.05. All statistical analyses were performed using SPSS version 25.0 (IBM Corp., Armonk, NY, USA).

Results

A total of 166 renal transplant recipients were included to our study, of whom 39 (23.5%) developed POAF. The mean age of the study population was 52.76±8.57 years, with no significant difference in age distribution between the POAF (+) and POAF (−) groups. Of the study population, 69 patients (41.6%) were female with no significant difference in the proportion of females between the 2 groups. Smoking, hypertension, chronic obstructive pulmonary disease, and coronary artery disease were significantly more common in the POAF (+) group compared to the POAF (−) group. Importantly, the left atrial (LA) size was similar in both groups. Notably, preopPWD, PWPTDII, PWPTV1 were significantly higher in the POAF (+) group compared to the POAF (−) group. Baseline characteristics, clinical, electrocardiographic, and echocardiographic data are presented in Table 1.

In the univariable logistic regression analysis, several clinical variables were found to be significantly associated with the development of POAF. These included hypertension, diabetes mellitus, chronic obstructive pulmonary disease, and coronary artery disease. Electrocardiographic parameters, including preopPWD (OR: 1.354, 95%CI: 1.170–1.568, P <0.001), PWPTDII (OR: 1.309, 95%CI: 1.195–1.418, P <0.001), and PWPTV1 (OR: 1.309, 95%CI: 1.198–1.431, P<0.001), were also significantly associated with POAF in univariate analysis (Table 2).

A penalized regression analysis was conducted to identify the most relevant predictors of the outcome while minimizing overfitting. Preoperative PWD was identified as an independent predictor of POAF (Table 3).

Receiver operating characteristic (ROC) analysis revealed that PWPTDII, PWPTV1, and preopPWD each demonstrated excellent predictive accuracy for POAF. All 3 parameters had very high area under the curve (AUC) values: 0.924, 0.926, and 0.924, respectively. These findings highlight the strong and comparable predictive ability of these measures, suggesting they could serve as valuable tools for preoperative risk stratification of patients at risk for POAF (Figure 1, Table 4).

Discussion

STUDY LIMITATIONS:

Despite the valuable insights provided by our study, several limitations should be acknowledged. First, its retrospective nature and relatively small sample size may limit the generalizability our findings. Second, the measurements of electrocardiographic parameters were performed manually, which may have introduced inter-observer variability despite efforts to minimize this through blinded independent assessments. Finally, given that this investigation was performed at a single institution, it is imperative that future studies undertake external validation across larger, multi-center populations to ascertain the wider relevance and generalizability of our findings.

Conclusions

In conclusion, we found that both established clinical determinants (eg, hypertension and coronary artery disease) and specific electrocardiographic indices (eg, preopPWD, PWPTDII, and PWPTV1) are independent predictors of POAF in renal transplant recipients. The integration of these non-invasive, cost-effective, and readily accessible electrocardiographic parameters into preoperative evaluation protocols could substantially augment risk stratification paradigms, thereby facilitating the timely identification and preemptive management of patients predisposed to POAF. To consolidate these findings and broaden their applicability, further rigorous prospective studies encompassing diverse, multi-institutional cohorts are warranted. Such endeavors will be pivotal in refining predictive models and developing targeted prevention aimed at ameliorating postoperative outcomes in this high-risk patient population.