Clinical Profile, Prognostic Factors, and Outcome Prediction in Hospitalized Patients With Bloodstream Infection: Results From a 10-Year Prospective Multicenter Study

Background: Bloodstream infection (BSI) is one of the most common serious bacterial infections worldwide and also a major contributor to in-hospital mortality. Determining the predictors of mortality is crucial for prevention and improving clinical prognosis in patients with nosocomial BSI. Methods: A nationwide prospective cohort study was conducted from 2007 until 2016 in 16 teaching hospitals across China. Microbiological results, clinical information, and patient outcomes were collected to investigate the pathogenic spectrum and mortality rate in patients with BSI and identify outcome predictors using multivariate regression, prediction model, and Kaplan-Meier analysis. Results: No significant change was observed in the causative pathogen distribution during the 10-year period and the overall in-hospital mortality was 12.83% (480/3,741). An increased trend was found in the mortality of patients infected with Pseudomonas aeruginosa or Acinetobacter baumannii, while a decreased mortality rate was noted in Staphylococcus aureus-related BSI. In multivariable-adjusted models, higher mortality rate was significantly associated with older age, cancer, sepsis diagnosis, ICU admission, and prolonged hospital stay prior to BSI onset, which were also determined using machine learning-based predictive model achieved by random forest algorithm with a satisfactory performance in outcome prediction. Conclusions: Our study described the clinical and microbiological characteristics and mortality predictive factors in patients with BSI. These informative predictors would inform clinical practice to adopt effective therapeutic strategies to improve patient outcomes.

Automated summary

A nationwide prospective cohort study conducted in 16 teaching hospitals across China from 2007 to 2016 found that the overall in-hospital mortality rate for BSI patients was 12.83%. The study identified changing mortality trends associated with specific pathogens and determined that older age, cancer, sepsis diagnosis, ICU admission, and prolonged hospital stay prior to BSI onset were significant predictors of higher mortality rate. Predictive models using machine learning algorithms showed satisfactory performance in outcome prediction.