Predicting Healthcare-associated Infections: are Point of Prevalence Surveys data useful?
Vol. 63 No. 2 (2022), Nosocomial Infections
Vol. 63 No. 2 (2022)

Predicting Healthcare-associated Infections: are Point of Prevalence Surveys data useful? A usefull pratical tool for detect Healthcare-associated Infections

Nosocomial Infections
https://doi.org/10.15167/2421-4248/jpmh2022.63.2.1496
Published 2022-07-18
Marco Golfera
MD, Post Graduate School of Public Health, University of Siena, Siena, Italy
Fabrizo Toscano
MD, Post Graduate School of Public Health, University of Siena, Siena, Italy
Gabriele Cevenini
BEng, Department of Medical Biotechnology, University of Siena, Siena, Italy.
Barbara Rita Porchia
MD, Medical Management, “Le Scotte” Teaching Hospital, Siena, Italy
Maria Francesca De Marco
MD, Medical Management, “Le Scotte” Teaching Hospital, Siena, Italy
Andrea Serafini
Medical Management, Grosseto Hospital, AUSL Sud-Est Grosseto, Italy
Emma Ceriale
Public Hygiene and Nutrition, “Ruffolo” Hospital, Siena, Italy
Daniele Lenzi
MD, Medical Director, Grosseto Hospital, Grosseto, Italy
Gabriele Messina
MD, PhD, MSc Epid, Department of Molecular and Developmental Medicine, Post Graduate School of Public Health, University of Siena, Siena, Italy.
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Keywords

Healthcare-acquired infections
hospital-acquired infections
pedictive model

Abstract

Introduction

Since 2012, the European Centre for Disease Prevention and Control (ECDC)

promotes a point prevalence survey (PPS) of HAIs in European acute care hospitals. Through a

retrospective analysis of 2012, 2015 and 2017 PPS of HAIs performed in a tertiary academic

hospital in Italy, we developed a model to predict the risk of HAI.

Methods

Following ECDC protocol we surveyed 1382 patients across three years. Bivariate logistic

regression analyses were conducted to assess the relationship between HAI and several variables.

Those statistically significant were included in a stepwise multiple regression model. The goodness

of fit of the latter model was assessed with the Hosmer-Lemeshow test, ultimately constructing a

probability curve to estimate the risk of developing HAIs.

Results

Three variables resulted statistically significant in the stepwise logistic regression model:

length of stay (OR 1.03; 95% C.I.: 1.02-1.05), devices breaking the skin (i.e. peripheral or central

vascular catheter, OR 4.38; 95% C.I.: 1.52-12.63), urinary catheter (OR 4.71; 95% C.I.: 2.78-7.98).

Conclusion

PPSs are a convenient and reliable source of data to develop HAIs prediction models.

The differences found between our results and previously published studies suggest the need of

developing hospital-specific databases and predictive models for HAIs.

https://doi.org/10.15167/2421-4248/jpmh2022.63.2.1496
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