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. 2022 Jul 31;63(2):E304-E309.
doi: 10.15167/2421-4248/jpmh2022.63.2.1496. eCollection 2022 Jun.

Predicting Healthcare-associated Infections: are Point of Prevalence Surveys data useful?

Marco Golfera  1 , Fabrizio Toscano  1 , Gabriele Cevenini  2 , Maria F DE Marco  3 , Barbara R Porchia  3 , Andrea Serafini  1   4 , Emma Ceriale  5 , Daniele Lenzi  3 , Gabriele Messina  6
Affiliations

Predicting Healthcare-associated Infections: are Point of Prevalence Surveys data useful?

Marco Golfera et al. J Prev Med Hyg. .

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% CI: 1.02-1.05), devices breaking the skin (i.e. peripheral or central vascular catheter, OR 4.38; 95% CI: 1.52-12.63), urinary catheter (OR 4.71; 95% CI: 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.

Keywords: Healthcare-associated infections; Point prevalence surveys; Prediction Models.

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Conflict of interest statement

Conflict of interest statement None to declare

Figures

Fig. 1.
Fig. 1.
HAI probability by devices. The four graphs above show the probability of developing HAI if are present both devices (A), one of them (B,C) or no devices (D) according to the following formula. The coefficient value so identified were B1= 0,033 (length of stay), B2 = 1,550 (urinary catheter), B3 = 1,477 (devices breaking the skin); C indicates regression constant (C = -5,057); X defines the vector of independent variables relatives for each subject, in detail X1 for length of stay, X2 for presence/absence of urinary catheter and X3 for devices breaking the skin.
None
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