Risk factors for urinary tract infections in geriatric hospitals

Girard, R.; Gaujard, Sylvain; Pergay, V.; Pornon, P.; Martin-Gaujard, Géraldine; Bourguignon, Laurent

doi:10.1016/j.jhin.2017.05.007

Cited by 16 publications

(9 citation statements)

References 29 publications

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“…by reducing unnecessary use or improving management of indwelling urinary catheters in high-risk patients. Girard et al thus identified the factors associated with HA-UTI in the general population of patients in geriatric hospitals, the most important of which were UTI in the six months before the study, and intermittent bladder catheterization [24]. We also found a strong correlation to previous UTIs in a general hospital population, which is likely to be associated with underlying urological pathologies in the patient.…”

Section: Number Of Splitting Rules Training Importance Validation Impsupporting

confidence: 65%

Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: A retrospective cohort study

Frimodt‐Møller

Sørensen²,

Hardahl

2021

PLoS ONE

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Background Healthcare associated infections (HAI) are a major burden for the healthcare system and associated with prolonged hospital stay, increased morbidity, mortality and costs. Healthcare associated urinary tract infections (HA-UTI) accounts for about 20–30% of all HAI’s, and with the emergence of multi-resistant urinary tract pathogens, the total burden of HA-UTI will most likely increase. Objective The aim of the current study was to develop two predictive models, using data from the index admission as well as historic data on a patient, to predict the development of UTI at the time of entry to the hospital and after 48 hours of admission (HA-UTI). The ultimate goal is to predict the individual patient risk of acquiring HA-UTI before it occurs so that health care professionals may take proper actions to prevent it. Methods Retrospective cohort analysis of approx. 300 000 adult admissions in a Danish region was performed. We developed models for UTI prediction with five machine-learning algorithms using demographic information, laboratory results, data on antibiotic treatment, past medical history (ICD10 codes), and clinical data by transformation of unstructured narrative text in Electronic Medical Records to structured data by Natural Language Processing. Results The five machine-learning algorithms have been evaluated by the performance measures average squared error, cumulative lift, and area under the curve (ROC-index). The algorithms had an area under the curve (ROC-index) ranging from 0.82 to 0.84 for the entry model (T = 0 hours after admission) and from 0.71 to 0.77 for the HA-UTI model (T = 48 hours after admission). Conclusion The study is proof of concept that it is possible to create machine-learning models that can serve as early warning systems to predict patients at risk of acquiring urinary tract infections during admission. The entry model and the HA-UTI models perform with a high ROC-index indicating a sufficient sensitivity and specificity, which may make both models instrumental in individualized prevention of UTI in hospitalized patients. The favored machine-learning methodology is Decision Trees to ensure the most transparent results and to increase clinical understanding and implementation of the models.

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Section: Number Of Splitting Rules Training Importance Validation Impsupporting

confidence: 65%

Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: A retrospective cohort study

Frimodt‐Møller

Sørensen²,

Hardahl

2021

PLoS ONE

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“…Frequency of nosocomial cases amongst elderly inpatients were higher than other age group, and this was supported by several studies [35][36][37][38][39][40][41][42], which reported that it may be due to risk factors such as impaired immunity, chronic diseases, medications, malnutrition, and functional impairments, among others.…”

Section: Nosocomialmentioning

confidence: 71%

Nosocomial Infections at Three Regional Tertiary Hospitals in Trinidad and Tobago

Elliott¹,

Justiz-Vaillant²

2020

BRCR

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Objective: The main objectives of this research were to conduct and provide accurate and original findings related to the epidemiological study of nosocomial infections at three regional tertiary hospitals in Trinidad and Tobago. Specifically, the researcher determined; the frequency of nosocomial infections (NI), the frequency of multiple drug resistance among bacterial organisms associated with NI, infection control measures practiced at the research hospitals and the cost of such NI in terms of morbidity and mortality Synopsis: This study estimated the rate of nosocomial infections (NI) among patients at three major regional hospitals in Trinidad and Tobago and evaluated the frequency of pathogens associated with nosocomial infections. Approximately 450 of 126, 668 patients had nosocomial infections and the most frequent type of nosocomial pathogens were: Staphylococcus sp. (22.5%), Pseudomonas aeruginosa sp. (12.7%), Acinetobacter (11.8%) and Klebsiella sp. (11.6%). Methods: A one-year prospective cross-sectional study was carried out. The nosocomial pathogens were retrieved from the microbiology laboratory. Antimicrobial susceptibility test by the disk diffusion method were done on all bacterial isolates. Data was analysed using SPSS version 20.0. Results: This research revealed that 450 inpatients suffered nosocomial infections, with thirty (30) mortalities during the twelve (12) months that the study lasted (June 2013 to May 2014) at three regional hospitals of Trinidad and Tobago. The incidence of nosocomial infections was 5.8% and the nosocomial infection rate was 3.6 per 1000 (450/126,668). The highest rate (30.1%) was observed in the Intensive Care Unit (82/272 admissions). The most frequent type of nosocomial infection was Skin and Soft Tissue Infections 168 (37.3%). Staphylococcus sp. (22.5%), Pseudomonas aeruginosa sp. (12.7%), Acinetobacter (11.8%) and Klebsiella sp. (11.6%) were the most frequently occurring nosocomial pathogens. Conclusion: Consistency in performing good hygiene practices is vital for reducing the high nosocomial rate found at the research sites. Prediction of these infections is very important as a part of clinical surveillance programs to take preventive measures in advance. The antimicrobial susceptibility pattern rate (ASPR) showed that only 8.3 % (5/60) of the isolates were antibiotic-susceptible strains.

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“…However, male infections were more common than female infections overall. The study uncovered evidence contradicting the notion that women were more susceptible to disease because they had shorter urethra [ 3 , 12 , 13 ].…”

Section: Reviewmentioning

confidence: 91%

“…However, it was not considered an important risk factor in the past. Until now, it is regarded as one of the risk factors causing hospital-acquired urinary tract infections, especially among those above 70 years old [ 13 ].…”

Section: Reviewmentioning

confidence: 99%

“…The risk is further increased by placing the catheter outside the operation room after being admitted to the hospital, having a ureteral stent, and utilizing the catheter to assess urine output. Every investigation has identified extended catheterization for longer than six days as the most significant risk factor that may be modified; by the 30th day after catheterization, infection is almost ubiquitous [ 13 , 14 ].…”

Section: Reviewmentioning

confidence: 99%

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Catheter-Associated Urinary Tract Infection (CAUTI)

Rubi¹,

Mudey²,

Kunjalwar³

2022

Cureus

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One of the most prevalent health-related illnesses globally is catheter-associated urinary tract infection (CAUTI). CAUTIs account for almost half of all hospital-acquired diseases. Most of the healthcare-acquired urinary tract infections result from catheter tubes implantation. These tubes connect a collecting system and the urinary bladder via the urethra. These are known as indwelling urinary catheters. The length of catheterization has a key role in starting bacteriuria since biofilm eventually forms on all of these devices. Despite the low percentage of people with bacteriuria who start showing symptoms, there is nevertheless a significant burden associated with these contamination due to the repeated use of indwelling urinary devices. Minimizing indwelling device usage and stopping the catheter as soon as medically possible are the two most crucial preventative measures for bacteriuria and infection when device use is required. Efforts to avoid catheter-acquired urinary infections must be implemented and monitored by infection control guidelines in healthcare institutions. These approaches include monitoring device use, the suitability of device justifications, and problems. Ultimately, technological advancements in device substances that inhibit colony generation will be necessary to avoid these infestations. There is still some way by which we can bring down the increased phenomenon of catheter-associated urinary tract contamination by maintaining hygiene while handling the catheter and patients and keeping the infected patients away or isolated from unaffected patients as a precaution. This article mainly focuses on an overview that helps with discussing prevention, risk factors, diagnosis, control and management of CAUTI.

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Risk factors for urinary tract infections in geriatric hospitals

Cited by 16 publications

References 29 publications

Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: A retrospective cohort study

Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: A retrospective cohort study

Nosocomial Infections at Three Regional Tertiary Hospitals in Trinidad and Tobago

Catheter-Associated Urinary Tract Infection (CAUTI)

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