Abstract
BACKGROUND: A persistent fever following cardiovascular surgery presents a significant clinical challenge and often leads to adverse patient outcomes. This study aims to develop a nomogram predictive model for persistent postoperative fever, which could serve as a valuable tool for clinicians in making diagnostic and treatment decisions. METHODS: The medical records of patients who underwent cardiovascular surgery at the First Affiliated Hospital of Nanjing Medical University in 2023 were retrospectively analysed. The patients were divided into two groups based on whether their body temperature remained above 38℃ for three consecutive days after surgery: the persistent fever group and the control group. Independent risk factors for persistent postoperative fever were identified through univariate and multivariate logistic regression analyses. A predictive nomogram model was then developed and validated. RESULTS: The study involved 343 patients who underwent cardiovascular surgery, revealing an overall postoperative fever rate of 70.55% and a persistent fever rate of 38.78%. The highest fever rates were observed on the first and second postoperative days. Multivariate logistic regression analysis identified several independent risk factors for persistent postoperative fever, including older age at admission, a history of smoking, a higher Controlling Nutritional Status Score (CONUT), an elevated Monocyte-to-Lymphocyte Ratio (MLR), a longer duration of surgery, the use of cardiopulmonary bypass, and intraoperative transfusion of machine-washed red blood cells. A nomogram prediction model showed good discriminatory and predictive ability, with an area under the receiver operating characteristic curve (AUC) of 0.821 (95% CI: 0.775–0.866). CONCLUSION: The nomogram developed in this research offers a quantitative and visually intuitive method for early evaluation of the probability of persistent postoperative fever in individuals undergoing cardiovascular surgery.