Predicting healthcare-associated infections, length of stay, and mortality with the nursing intensity of care index
| Autor: | David P. Calfee, Jingjing Shang, Jiyoun Song, Bevin Cohen, Jianfang Liu, Elioth Sanabria, David D. Yao, Philip Zachariah, Elaine Larson |
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| Rok vydání: | 2021 |
| Předmět: |
Microbiology (medical)
medicine.medical_specialty Epidemiology Personnel Staffing and Scheduling Staffing Nursing Staff Hospital 030501 epidemiology Logistic regression 03 medical and health sciences Nursing care 0302 clinical medicine Nursing Acute care Health care medicine Humans Hospital Mortality 030212 general & internal medicine Child Cross Infection business.industry Incidence Incidence (epidemiology) Length of Stay Confidence interval Community hospital Infectious Diseases 0305 other medical science business Delivery of Health Care |
| Zdroj: | Infection Control & Hospital Epidemiology. 43:298-305 |
| ISSN: | 1559-6834 0899-823X |
| DOI: | 10.1017/ice.2021.114 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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