Estimating length of stay and inpatient charges attributable to hospital-acquired bloodstream infections

• Main Authors: Yuzheng Zhang, Mingmei Du, Janice Mary Johnston, Ellie Bostwick Andres, Jijiang Suo, Hongwu Yao, Rui Huo, Yunxi Liu, Qiang Fu
• Format: Article
• Language: English
• Published: BMC 2020-08-01
• Series: Antimicrobial Resistance and Infection Control
• Subjects: Hospital-acquired bloodstream infection; Length of stay; Hospital charge
• Online Access: http://link.springer.com/article/10.1186/s13756-020-00796-5

Abstract Background Hospital-acquired bloodstream infection (BSI) is associated with high morbidity and mortality and increases patients’ length of stay (LOS) and hospital charges. Our goals were to calculate LOS and charges attributable to BSI and compare results among different models. Methods A retrospective observational cohort study was conducted in 2017 in a large general hospital, in Beijing. Using patient-level data, we compared the attributable LOS and charges of BSI with three models: 1) conventional non-matching, 2) propensity score matching controlling for the impact of potential confounding variables, and 3) risk set matching controlling for time-varying covariates and matching based on propensity score and infection time. Results The study included 118,600 patient admissions, 557 (0.47%) with BSI. Six hundred fourteen microorganisms were cultured from patients with BSI. Escherichia coli was the most common bacteria (106, 17.26%). Among multi-drug resistant bacteria, carbapenem-resistant Acinetobacter baumannii (CRAB) was the most common (42, 38.53%). In the conventional non-matching model, the excess LOS and charges associated with BSI were 25.06 days (P < 0.05) and US$22041.73 (P < 0.05), respectively. After matching, the mean LOS and charges attributable to BSI both decreased. When infection time was incorporated into the risk set matching model, the excess LOS and charges were 16.86 days (P < 0.05) and US$15909.21 (P < 0.05), respectively. Conclusion This is the first study to consider time-dependent bias in estimating excess LOS and charges attributable to BSI in a Chinese hospital setting. We found matching on infection time can reduce bias.