Cluster analysis identifies patients at risk of catheter-associated urinary tract infections in intensive care units: findings from the SPIN-UTI Network

Abstract

Background: Although preventive strategies have been proposed against catheterassociated urinary tract infections (CAUTIs) in intensive care units (ICUs), more efforts are needed to control the incidence rate. Aim: To distinguish patients according to their characteristics at ICU admission, and to identify clusters of patients at higher risk for CAUTIs. Methods: A two-step cluster analysis was conducted on 9656 patients from the Italian Nosocomial Infections Surveillance in Intensive Care Units project. Findings: Three clusters of patients were identified. Type of admission, patient origin and administration of antibiotics had the greatest weight on the clustering model. Cluster 1 comprised more patients with a medical type of ICU admission who came from the community. Cluster 2 comprised patients who were more likely to come from other wards/ hospitals, and to report administration of antibiotics 48 h before or after ICU admission. Cluster 3 was similar to Cluster 2 but was characterized by a lower percentage of patients with administration of antibiotics 48 h before or after ICU admission. Patients in Clusters 1 and 2 had a longer duration of urinary catheterization [median 7 days, interquartile range (IQR) 12 days for Cluster 1; median 7 days, IQR 11 days for Cluster 2] than patients in Cluster 3 (median 6 days, IQR 8 days; P<0.001). Interestingly, patients in Cluster 1 had a higher incidence of CAUTIs (3.5 per 100 patients) compared with patients in the other two clusters (2.5 per 100 patients in both clusters; P¼0.033). Conclusion:To the authors' knowledge, this is the first study to use cluster analysis to identify patients at higher risk of CAUTIs who could gain greater benefit from preventive strategies.

Three-Year Trends of Healthcare-Associated Infections and Antibiotic Use in Acute Care Hospitals: Findings from 2016–2018 Point Prevalence Surveys in Sicily, Italy

“…By contrast, our analysis demonstrated that prevalence of HAIs remained stable from 2016 to 2018, with a negative but not significant peak in 2017. This was partly comforting since patients participating in the Sicilian PPSs became older and more severe over the years, although it is well known that aging and disease severity are risk factors for HAIs [ 26 , 27 , 28 ]. In line, we noted an increasing trend in the presence of invasive devices from 2016 to 2018, but it also did not result in an increased prevalence of HAIs.…”

“…It is worth mentioning that results from logistic regression indicated disease severity (i.e., assessed using the McCabe score) and the presence of invasive devices as factors associated with the prevalence of HAIs. Thus, this proves the emerging need for identifying patients at higher risk of HAIs at an early stage [ 27 , 28 ] and for improving the management of invasive devices and surgical procedures [ 16 , 29 , 30 , 31 , 32 , 33 ].…”

Nanomaterials for the Diagnosis and Treatment of Urinary Tract Infections

“…Common diseases associated with complicated UTIs include neurological impairment-based urinary obstruction, urinary retention, and immunosuppression, renal transplantation, renal failure, pregnancy, or foreign bodies such as biofilms, calculi, catheters, and other devices [8]. Catheter-associated UTIs (CAUTIs) are linked to increased morbidity and mortality and are the most common origin of secondary bloodstream infections [9,10]. Noncomplicated UTIs, the most prevalent forms of UTIs, are not based on any neurological, structural, nor physiological defects of the urinary tract [11], characteristically affecting Nanomaterials 2021, 11, 546 2 of 21 women, children, and old patients.…”

Disentangling the Association of Hydroxychloroquine Treatment with Mortality in Covid-19 Hospitalized Patients through Hierarchical Clustering

“…In unsupervised algorithms, the machine simply learns to identify hidden patterns across a high number of observations over many features, without the need for labels, and is more descriptive-rather than predictive-in nature. ese algorithms have shown promising findings in public health, in the management of both chronic [20] and acute health conditions [21,22], but also during the current pandemics. In particular, useful ML applications have been reported in the prediction of Covid-19 diagnosis and prognosis [23].…”