Predictive nomogram models for atrial fibrillation in COPD patients: A comprehensive analysis of risk factors and prognosis.

ABSTRACT

The aim of the present study was to identify the independent risk factors and prognostic indicators for atrial fibrillation (AF) in patients with chronic obstructive pulmonary disease (COPD) and to develop predictive nomogram models. This retrospective study included a total of 286 patients with COPD who were admitted to the Second Affiliated Hospital of Guilin Medical College between January 2020 and May 2022. The average age of the patients was 77.11±8.67 years. Based on the presence or absence of AF, the patients were divided into two groups The AF group (n=87) and the non-AF group (n=199). Logistic regression analysis was conducted to identify variables with significant differences between the two groups. Nomogram models were constructed to predict the occurrence of AF in COPD patients and to assess prognosis. Survival analysis was performed using the Kaplan-Meier method. The follow-up period for the present study extended until April 31, 2023. Survival time was defined as the duration from the date of the interview to the date the participant succumbed or the end of the follow-up period. In the present study, age, uric acid (UA) and left atrial diameter (LAD) were found to be independent risk factors for the development of AF in patients diagnosed with COPD. The stepwise logistic regression analysis revealed that age had an odds ratio (OR) of 1.072 [95% confidence interval (CI) 1.019-1.128; P=0.007], UA had an OR of 1.004 (95% CI 1.001-1.008; P=0.010) and LAD had an OR of 1.195 (95% CI 1.098-1.301; P<0.001). Univariate and multivariate Cox regression analysis revealed that LAD and UA were independent prognostic factors for long-term mortality in COPD patients with AF. LAD had a hazard ratio (HR) of 1.104 (95% CI 1.046-1.165; P<0.001) and UA had an HR of 1.004 (95% CI 1.000-1.008; P=0.042). Based on these findings, predictive nomogram models were developed for AF in COPD patients, which demonstrated good discrimination ability with an area under the curve of 0.886. The prognostic nomogram for COPD patients with AF also showed good predictive accuracy with a concordance index of 0.886 (95% CI 0.842-0.930). These models can provide valuable information for risk assessment and prognosis evaluation in clinical practice. Age, UA and LAD are independent risk factors for AF in COPD patients. The developed nomogram models provide a reliable tool for predicting AF in COPD patients and for prognosis assessment.