ArticleInfection and drug resistance2025
Clinical Prediction of Secondary Bloodstream Infections in Patients with Cerebral Infarction: A Nomogram-Driven Risk Assessment Model Based on LASSO Regression.
Article in Infection and drug resistance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Machine Learning Analysis of Risk Factors for Catheter-Associated Urinary Tract Infections in Stroke Patients and Their Impact on Healthcare Quality.International journal of general medicine · 2026Article
- Analysis of prognostic risk factors and risk management measures for patients with ischemic stroke and bloodstream infection based on machine learning.Frontiers in cellular and infection microbiology · 2025Article
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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 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-Lemeshow 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.
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