Predicting healthcare-associated infections, length of stay, and mortality with the nursing intensity of care index. (16th March 2022)

• Record Type: Journal Article
• Authors: Cohen, Bevin; Sanabria, Elioth; Liu, Jianfang; Zachariah, Philip; Shang, Jingjing; Song, Jiyoun; Calfee, David; Yao, David; Larson, Elaine
• Is Part Of: Infection control and hospital epidemiology. Volume 43:Number 3(2022)
• Page Start: 298
• Page End: 305
• Publication Date: 2022-03-16
• Subjects: Nosocomial infections -- Epidemiology -- Periodicals; Health facilities -- Sanitation -- Periodicals; Hospital buildings -- Sanitation -- Periodicals; Cross Infection -- Periodicals; Epidemiology -- Periodicals; Hospitals -- Periodicals; Infection Control -- Periodicals; 614.44
• DOI: 10.1017/ice.2021.114
• Languages: English
• ISSNs: 0899-823X

Abstract

Abstract: Objectives: The objectives of this study were (1) to develop and validate a simulation model to estimate daily probabilities of healthcare-associated infections (HAIs), length of stay (LOS), and mortality using time varying patient- and unit-level factors including staffing adequacy and (2) to examine whether HAI incidence varies with staffing adequacy. Setting: The study was conducted at 2 tertiary- and quaternary-care hospitals, a pediatric acute care hospital, and a community hospital within a single New York City healthcare network. Patients: All patients discharged from 2012 through 2016 (N = 562, 435). Methods: We developed a non-Markovian simulation to estimate daily conditional probabilities of bloodstream, urinary tract, surgical site, and Clostridioides difficile infection, pneumonia, length of stay, and mortality. Staffing adequacy was modeled based on total nurse staffing (care supply) and the Nursing Intensity of Care Index (care demand). We compared model performance with logistic regression, and we generated case studies to illustrate daily changes in infection risk. We also described infection incidence by unit-level staffing and patient care demand on the day of infection. Results: Most model estimates fell within 95% confidence intervals of actual outcomes. The predictive power of the simulation model exceeded that of logistic regression (area under the curve [AUC], 0.852 and 0.816, respectively). HAI incidence was greatest when staffing was lowest and nursing care intensity was highest. Conclusions: This model has potential clinical utility for identifying modifiable conditions in real time, such as low staffing coupled with high care demand.