Predicting healthcare-associated infections, length of stay, and mortality with the nursing intensity of care index

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Bevin Cohen
  • Elioth Sanabria
  • Jianfang Liu
  • Philip Zachariah
  • Jingjing Shang
  • Jiyoun Song
  • David Calfee
  • Elaine Larson

Detail(s)

Original languageEnglish
Pages (from-to)298-305
Journal / PublicationInfection Control and Hospital Epidemiology
Volume43
Issue number3
Online published16 Apr 2021
Publication statusPublished - Mar 2022
Externally publishedYes

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.

Citation Format(s)

Predicting healthcare-associated infections, length of stay, and mortality with the nursing intensity of care index. / Cohen, Bevin; Sanabria, Elioth; Liu, Jianfang; Zachariah, Philip; Shang, Jingjing; Song, Jiyoun; Calfee, David; Yao, David; Larson, Elaine.

In: Infection Control and Hospital Epidemiology, Vol. 43, No. 3, 03.2022, p. 298-305.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review