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

Published online by Cambridge University Press:  16 April 2021

Bevin Cohen Open the ORCID record for Bevin Cohen [Opens in a new window] ,
Elioth Sanabria ,
Jianfang Liu ,
Philip Zachariah ,
Jingjing Shang ,
Jiyoun Song ,
David Calfee ,
David Yao  and
Elaine Larson
Bevin Cohen*
Affiliation:
Center for Nursing Research and Innovation, The Mount Sinai Hospital, New York, New York
Elioth Sanabria
Affiliation:
Columbia University Fu Foundation School of Engineering and Applied Sciences, New York, New York
Jianfang Liu
Affiliation:
Columbia University School of Nursing, New York, New York
Philip Zachariah
Affiliation:
Columbia University Vagelos College of Physicians and Surgeons, New York, New York
Jingjing Shang
Affiliation:
Columbia University School of Nursing, New York, New York
Jiyoun Song
Affiliation:
Columbia University School of Nursing, New York, New York
David Calfee
Affiliation:
Weill Cornell Medical College, New York, New York
David Yao
Affiliation:
Columbia University Fu Foundation School of Engineering and Applied Sciences, New York, New York
Elaine Larson
Affiliation:
Columbia University School of Nursing, New York, New York
*
Author for correspondence: Bevin Cohen, E-mail: bevin.cohen@mountsinai.org
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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.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 43 , Issue 3 , March 2022 , pp. 298 - 305
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Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

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