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Risk factor analysis and predictive model development for healthcare-associated infections post-coronary artery
Yan Liu1, Lingbo Xue1, Ping Jiang1
1Infection Management Department, Nantong First People's Hospital, Nantong, China.
Insights
This study identified key risk factors for healthcare-associated infections (HAIs) after coronary artery bypass grafting (CABG). A predictive model was developed to accurately assess HAI risk in post-CABG patients.
Area of Science:
- Cardiology
- Infectious Diseases
- Medical Informatics
Background:
- Healthcare-associated infections (HAIs) pose a significant risk to patients undergoing cardiac surgery.
- Coronary artery bypass grafting (CABG) is a common cardiac procedure associated with potential postoperative complications, including infections.
Purpose of the Study:
- To identify independent risk factors for HAIs in patients following CABG.
- To develop and validate a predictive model for assessing the risk of HAIs in post-CABG patients.
Main Methods:
- Retrospective collection of clinical data from CABG patients (January 2019 - December 2023).
- Logistic regression analysis to determine independent risk factors for HAIs.
- Development and validation of a risk prediction model using receiver operating characteristic (ROC) curve analysis.
Main Results:
- Identified risk factors include diabetes, preoperative white blood cell count, preoperative albumin levels, intraoperative blood transfusion, indwelling drainage tube, drainage volume, duration of ventilator use, and central venous catheterization time.
- The developed predictive model demonstrated high accuracy (Area Under Curve = 0.970, sensitivity = 90.5%, specificity = 92.1%).
Conclusions:
- Diabetes, preoperative laboratory values, transfusion requirements, and duration of invasive procedures are significant risk factors for HAIs post-CABG.
- The validated predictive model offers a valuable tool for accurate postoperative HAI risk assessment in CABG patients.
Objective:
This study aimed to analyze the risk factors associated with healthcare-associated infections (HAIs) in individuals who underwent post-coronary artery bypass grafting (CABG) and to develop a predictive model for infection risk assessment.
Methods:
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.
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