Abstract
PURPOSE: To evaluate the impact of secondary bloodstream infections (BSI) on healthcare quality indicators in patients with cerebral infarction, and to develop a validated predictive model. METHODS: This study conducted a retrospective analysis of 7,698 distinct patients with cerebral infarction (2023) from a tertiary hospital in Guangzhou. Patients were categorized into two groups: BSI-negative (n=7,573) and BSI-positive (n=125). Healthcare quality indicators were compared using Mann-Whitney U-test. A predictive model was created using Least Absolute Shrinkage and Selection Operator (LASSO) regression, based on a 7:3 training-validation split. The model's performance was validated through the area under the Receiver Operating Characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). RESULTS: Patients with BSI had significantly prolonged hospital stays (median of 17 days versus 11 days, p<0.001), higher costs (median of 34,859 yuan compared to 16,921 yuan, p<0.001), and increased adverse outcomes (34.4% versus 1.6%, p<0.001). The LASSO analysis identified four predictors: The following variables were found to have a statistically significant relationship to the occurrence of the primary complication: peripherally inserted central venous catheters (PICC) (odds ratio [OR] = 2.791, 95% confidence interval [CI] =1.514-5.148), use of ventilators(VA) (OR = 2.771, 95% CI=1.410-5.443), Indwelling urinary catheters(CAU) (OR = 1.800, 95% CI= 0.990-3.276), and hypoalbuminemia (OR = 3.643, 95% CI=2.195-6.046).The nomogram demonstrated an AUC of 0.789 in the training set and 0.778 in the test set, indicating a satisfactory model fit across data sets. Good model fit based on Hosmer-Lemeshowp-values(Hosmer-LemeshowP=0.338/0.170).DCA indicated a net clinical benefit at risk thresholds of 0-15%. CONCLUSION: Secondary BSI in patients with cerebral infarction can seriously affect the quality of medical care.The developed nomogram functions as a pragmatic instrument for the preliminary identification of patients at high risk. It facilitates the implementation of targeted interventions, thereby reducing the incidence of BSI and enhancing patient outcomes.