Impact of urinary tract infection-causative microorganisms on the progression to bloodstream infection: A propensity score-matched analysis

Urine Leukocyte Counts for Differentiating Asymptomatic Bacteriuria From Urinary Tract Infection and Predicting Secondary Bacteremia

BACKGROUND

Differentiating between asymptomatic bacteriuria (ASB) and urinary tract infection (UTI) is difficult in patients who have difficulty communicating their symptoms. This study aimed to evaluate the diagnostic accuracy of urine leukocytes in distinguishing between UTI and ASB, and the clinical outcomes of patients with UTI according to the degree of pyuria.

METHODS

This retrospective cohort study included patients with positive urine cultures between July 2022 and June 2023 at two hospitals. UTI and ASB were diagnosed through a comprehensive review of medical records. We evaluated the differences in urine leukocyte counts between patients with UTI and ASB. The diagnostic performance of urine leukocytes to differentiate between UTI and ASB was evaluated. To investigate the clinical outcomes based on the degree of pyuria, we classified patients with upper UTI according to their urine leukocyte counts.

RESULTS

Of the 1,793 eligible patients with bacteriuria included, 1,464 had UTI and 329 had ASB. Patients with UTI had higher urinary leukocytes than patients with ASB did (490.4 vs. 123.5 cells/µL; P < 0.001). The area under the receiver operating characteristic curve was 0.702 for discriminating between ASB and UTI. The optimal urine leukocyte cutoff was 195.35 cells/µL, with a sensitivity and specificity of 0.70 and 0.60, respectively. A sequential rise in secondary bacteremia rate was observed according to an increase in urine leukocytes in patients with upper UTI, whereas in-hospital mortality showed no corresponding trend.

CONCLUSION

Urine leukocyte counts could be used to predict UTI occurrence and bacteremia secondary to UTI. Higher degrees of pyuria were associated with bacteremia but not with mortality. Urine leukocyte counts can provide additive information for patients with bacteriuria with vague symptoms.

Effects of Immersive Straight Catheterization Virtual Reality Simulation on Skills, Confidence, and Flow State in Nursing Students

Core nursing procedures are essential for nursing students to master because of their high frequency in nursing practice. However, the experience of performing procedures in actual hospital settings decreased during the coronavirus disease 2019 pandemic, necessitating the development of various contents to supplement procedural training. This study investigated the effects of a straight catheterization program utilizing an immersive virtual reality simulation on nursing students' procedural performance, self-confidence, and immersion. The study employed a nonequivalent control group pretest-posttest design with 29 participants in the experimental group and 25 in the control group. The experimental group received training through a computer-based immersive virtual reality program installed in a virtual reality hospital, with three weekly sessions over 3 weeks. The control group underwent straight catheterization using manikin models. The research findings validated that virtual reality-based straight catheterization education significantly improved students' procedural skills, self-confidence, and flow state. Therefore, limited practical training can be effectively supplemented by immersive virtual reality programs.

Predictive performance of urinalysis for urine culture results according to causative microorganisms: an integrated analysis with artificial intelligence

Urinary tract infections (UTIs) are pervasive and prevalent in both community and hospital settings. Recent trends in the changes of the causative microorganisms in these infections could affect the effectiveness of urinalysis (UA). We aimed to evaluate the predictive performance of UA for urinary culture test results according to the causative microorganisms. In addition, UA results were integrated with artificial intelligence (AI) methods to improve the predictive power. A total of 360,376 suspected UTI patients were enrolled from two university hospitals and one commercial laboratory. To ensure broad model applicability, only a limited range of clinical data available from commercial laboratories was used in the analyses. Overall, 53,408 (14.8%) patients were identified as having a positive urine culture. Among the UA tests, the combination of leukocyte esterase and nitrite tests showed the highest area under the curve (AUROC, 0.766; 95% CI, 0.764-0.768) for predicting urine culture positivity but performed poorly for Gram-positive bacteriuria (0.642; 0.637-0.647). The application of an AI model improved the predictive power of the model for urine culture results to an AUROC of 0.872 (0.870-0.875), and the model showed superior performance metrics not only for Gram-negative bacteriuria (0.901; 0.899-0.902) but also for Gram-positive bacteriuria (0.745; 0.740-0.749) and funguria (0.872; 0.865-0.879). As the prevalence of non-Escherichia coli-caused UTIs increases, the performance of UA in predicting UTIs could be compromised. The addition of AI technologies has shown potential for improving the predictive performance of UA for urine culture results.IMPORTANCEUA had good performance in predicting urine culture results caused by Gram-negative bacteria, especially for Escherichia coli and Pseudomonas aeruginosa bacteriuria, but had limitations in predicting urine culture results caused by Gram-positive bacteria, including Streptococcus agalactiae and Enterococcus faecalis. We developed and externally validated an AI model incorporating minimal demographic information of patients (age and sex) and laboratory data for UA, complete blood count, and serum creatinine concentrations. The AI model exhibited improved performance in predicting urine culture results across all the causative microorganisms, including Gram-positive bacteria, Gram-negative bacteria, and fungi.

Intestinal microbiology and urinary tract infection associated risk in long-term aged care residents

BACKGROUND

Urinary tract infections (UTI) are the most frequently diagnosed infection in residents of long-term care and are a major risk factor for urosepsis, hospitalisation, and death. Translocation of gut pathobionts into the urinary tract is the presumed cause of most UTIs. While specific gut microbiota characteristics have been linked to UTI risk in younger adults, their relevance in aged care residents remains uncertain.

METHODS

The faecal microbiome was assessed in 54 long-term aged care residents with a history of UTIs and 69 residents without a UTI history. Further comparisons were made to microbiome characteristics in 20 younger adults without UTIs. Microbiome characteristics were examined in relation to prior and subsequent UTIs, as well as antibiotic therapy.

RESULTS

In long-term aged care residents, prior UTI history and exposure to UTI-exclusive antibiotics do not significantly affect microbiome composition or functional capacity. However, exposure to antibiotics unrelated to UTI treatment is associated with distinct microbiota compositional traits. Adjustment for dementia, incontinence, diabetes, and prior antibiotic use finds no microbiota characteristic linked to UTI development. However, prior UTI is identified as a predictor of future UTIs. Comparison with younger adults identifies greater within-participant dispersion in aged care residents, as well as lower microbiota diversity and altered microbiome functional potential.

CONCLUSIONS

No association between the gut microbiome and UTI incidence, as has been reported in younger individuals, is evident in long-term aged care residents. Considerable variability in gut microbiome characteristics, relating to high antibiotic exposure and age-related physiological and immunological factors, could mask such a relationship. However, it cannot be discounted that increased UTI risk in the elderly is independent of microbiome-mediated mechanisms.

Species Distribution, Typical Clinical Features and Risk Factors for Poor Prognosis of Super-Elderly Patients with Bloodstream Infection in China

Objective

Bloodstream infection (BSI) is characterized by high mortality, especially among these increasing super-elderly patients (≥85 years), and this study was conducted to understand the species distribution, typical clinical features and risk factors for poor prognosis of super-elderly patients with BSI.

Methods

Based on previous work, this retrospective study was performed by reviewing an ongoing prospective medical database in a comprehensive tertiary center in China, and all super-elderly patients with BSI in the past 6 years were enrolled in this study.

Results

Out of 5944 adult-patients with BSI, there were totally 431 super-elderly patients (≥85 years old) enrolled in this study and age ≥90 years accounted for 31.1% (134/431). Among these 431 super-elderly patients with BSI, 40 patients (9.3%) were diagnosed with BSI and the remained 401 super-elderly patients (90.7%) were defined as hospital-acquired BSI. The typical feature of these super-elderly patients with BSI was the high proportion of patients with various comorbidities, such as cardiovascular disease (83.8%), ischemic cerebrovascular disease (63.3%) and pulmonary infection (61.0%). The other typical feature was that most (60.1%) of these patients had been hospitalized for long time (≥28 days) prior to the onset of BSI, and most patients had received various invasive treatments, such as indwelling central venous catheter (53.1%) and indwelling urinary catheter (47.1%). Unfortunately, due to these adverse features above, both the 7-day short-term mortality (13.2%, 57/431) and the 30-day long-term mortality (24.8%, 107/431) were high. The multivariate analysis showed that both chronic liver failure (OR 7.9, 95% CI 2.3-27.8, P=0.001) and indwelling urinary catheter (OR 2.3, 95% CI 1.1-4.7, P=0.023) were independent risk factors for 7-day short-term mortality, but not for 30-day long-term mortality. In addition, the microbiology results showed that the most common species were associated with nosocomial infection or self-opportunistic infection, such as Staphylococcus hominis (18.3%), Staphylococcus epidermidis (11.8%), Escherichia coli (9.7%), Klebsiella pneumoniae (9.3%) and Candida albicans (8.6%, fungi).

Conclusion

Super-elderly patients with BSI had typical features, regardless of the pathogenic species distribution and their drug resistance, or clinical features and their risk factors for poor prognosis. These typical features deserved attention and could be used for the prevention and treatment of BSI among super-elderly patients.

Clinical Characteristics of Uncomplicated Acute Pyelonephritis Caused by Escherichia coli and Klebsiella pneumoniae

INTRODUCTION

This study compared the clinical characteristics and antimicrobial susceptibility of uncomplicated acute pyelonephritis (APN) caused by Escherichia coli and Klebsiella pneumoniae.

METHODS

We retrospectively reviewed the medical records of patients with uncomplicated APNs caused by E. coli and K. pneumoniae admitted to Keimyung University Dongsan Hospital between February 2014 and December 2021.

RESULTS

We enrolled 497 patients (372 with E. coli infection, 125 with K. pneumoniae infection). Male, healthcare-associated infection, solid tumors, liver cirrhosis, chronic renal disease, solid organ transplantation, and antibiotic usage within the last 3 months were more strongly associated with K. pneumoniae uncomplicated APNs than with E. coli. Bacteremia and fever occurred more frequently in E. coli uncomplicated APNs. Antimicrobial resistance rates to piperacillin/tazobactam and carbapenem were higher in K. pneumoniae. Antimicrobial resistance rates to aztreonam and ciprofloxacin were lower in K. pneumoniae. Thirty-day mortality was more observed in K. pneumoniae group in univariate analysis, but this difference was not observed after adjustment. Male sex, ultimately fatal disease in McCabe, and prior antibiotic use within 3 months were more associated with K. pneumoniae.

CONCLUSIONS

Male, underlying diseases, and prior antibiotic use was more associated with K. pneumoniae. Further study will be needed that microbiome of each situation and the related with the distribution of the strains.

Development and validation of artificial intelligence models to predict urinary tract infections and secondary bloodstream infections in adult patients

BACKGROUND

Traditional culture methods are time-consuming, making it difficult to utilize the results in the early stage of urinary tract infection (UTI) management, and automated urinalyses alone show insufficient performance for diagnosing UTIs. Several models have been proposed to predict urine culture positivity based on urinalysis. However, most of them have not been externally validated or consisted solely of urinalysis data obtained using one specific commercial analyzer.

METHODS

A total of 259,187 patients were enrolled to develop artificial intelligence (AI) models. AI models were developed and validated for the diagnosis of UTI and urinary tract related-bloodstream infection (UT-BSI). The predictive performance of conventional urinalysis and AI algorithms were assessed by the areas under the receiver operating characteristic curve (AUROC). We also visualized feature importance rankings as Shapley additive explanation bar plots.

RESULTS

In the two cohorts, the positive rates of urine culture tests were 25.2% and 30.4%, and the proportions of cases classified as UT-BSI were 1.8% and 1.6%. As a result of predicting UTI from the automated urinalysis, the AUROC were 0.745 (0.743-0.746) and 0.740 (0.737-0.743), and most AI algorithms presented excellent discriminant performance (AUROC > 0.9). In the external validation dataset, the XGBoost model achieved the best values in predicting both UTI (AUROC 0.967 [0.966-0.968]) and UT-BSI (AUROC 0.955 [0.951-0.959]). A reduced model using ten parameters was also derived.

CONCLUSIONS

We found that AI models can improve the early prediction of urine culture positivity and UT-BSI by combining automated urinalysis with other clinical information. Clinical utilization of the model can reduce the risk of delayed antimicrobial therapy in patients with nonspecific symptoms of UTI and classify patients with UT-BSI who require further treatment and close monitoring.