ORIGINAL RESEARCH article
Front. Public Health
Sec. Infectious Diseases: Epidemiology and Prevention
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1605272
Risk Factor Analysis and Predictive Model Development for Healthcare-Associated Infections Post-Coronary Artery Bypass Grafting
Provisionally accepted
- Nantong First People’s Hospital, Nantong, China
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Objective: This study aimed to analyze the risk factors associated with healthcareassociated infections (HAIs) in individuals who underwent post-coronary artery bypass grafting (CABG) and to develop a predictive model for infection risk assessment.: Clinical data were retrospectively collected from patients who underwent CABG at our hospital between January 2019 and December 2023. Data sources included the hospital infection surveillance system, hospital information system, and a questionnaire for HAIs in patients after cardiac surgery. Patients were divided into an infection group and a non-infection group based on whether they developed HAIs during the postoperative hospitalization period. Logistic regression was used to identify independent risk factors and to develop a risk prediction model. The predictive performance of the model was assessed using receiver operating characteristic curve analysis. Results: Independent risk factors for HAIs post-CABG included diabetes (odds ratio [OR] = 1.467), preoperative white blood cell count (OR = 0.117), preoperative albumin levels (OR = -0.146), intraoperative blood transfusion (OR = 0.001), presence of an indwelling drainage tube (OR = 0.864), drainage volume (OR = 0.003), duration of ventilator use (OR = 0.656), and central venous catheterization time (OR = 0.103). The predictive model was established as: Ln (P/1 -P) = -2.230 + 1.467 * diabetes + 0.117 * preoperative white blood cell count -0.146 * preoperative albumin + 0.001 * intraoperative blood transfusion + 0.864 * drainage tube indwelling + 0.003 * drainage volume + 0.656 * ventilator use time + 0.103 * central venous catheterization time. The Hosmer-Lemeshow test indicated a good model fit with observed values. Receiver operating characteristic curve analysis demonstrated that the model achieved an area under the curve of 0.970, with a sensitivity of 90.5% and a specificity of 92.1%. Conclusion: The independent risk factors for HAIs after CABG were diabetes, body mass index, preoperative white blood cell count, intraoperative blood transfusion volume, duration of pericardial and mediastinal drainage tube placement, total drainage volume, duration of mechanical ventilation, and duration of central venous catheterization. The developed risk prediction model demonstrated high accuracy in estimating postoperative HAI risk.
Keywords: Coronary Artery Bypass Grafting (CABG), health care associated infections (HAIs), Logistic regression, Prediction model, Risk factors
Received: 03 Apr 2025; Accepted: 13 Jun 2025.
Copyright: © 2025 Liu, Xue, Jiang, She, Peng and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xiaoqiang Yu, Nantong First People’s Hospital, Nantong, China
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