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Diagnostics
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Urinary Tract Infections: Diagnosis and Management
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Urinary Tract Infections: Diagnosis and Management

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Diagnostic Microbiology and Infectious Disease".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 8442

Special Issue Editor

Prof. Dr. Paul Froom

E-Mail Website
Guest Editor
School of Public Health, Sackler School of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel
Interests: clinical utility; urinary tract infections; elderly, internal medicine; laboratory testing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The purpose of this Special Issue is threefold. First, we will review various methods for urinalysis, including the more recently introduced automated technology. Second, we will discuss the clinical utility and disutility of various findings. Finally, we will discuss indications for testing. Original research articles, reviews, short communications, and interesting images are welcome, as well as either clinical or basic research.

Prof. Dr. Paul Froom
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Published Papers (4 papers)

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Research

14 pages, 1224 KiB  
Article
Interpretable Machine Learning Models for Predicting Critical Outcomes in Patients with Suspected Urinary Tract Infection with Positive Urine Culture
by Chieh-Ching Yen, Cheng-Yu Ma and Yi-Chun Tsai
Diagnostics 2024, 14(17), 1974; https://doi.org/10.3390/diagnostics14171974 - 6 Sep 2024
Cited by 1 | Viewed by 1656
Abstract
(1) Background: Urinary tract infection (UTI) is a leading cause of emergency department visits and hospital admissions. Despite many studies identifying UTI-related risk factors for bacteremia or sepsis, a significant gap remains in developing predictive models for in-hospital mortality or the necessity for [...] Read more.
(1) Background: Urinary tract infection (UTI) is a leading cause of emergency department visits and hospital admissions. Despite many studies identifying UTI-related risk factors for bacteremia or sepsis, a significant gap remains in developing predictive models for in-hospital mortality or the necessity for emergent intensive care unit admission in the emergency department. This study aimed to construct interpretable machine learning models capable of identifying patients at high risk for critical outcomes. (2) Methods: This was a retrospective study of adult patients with urinary tract infection (UTI), extracted from the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database. The critical outcome is defined as either in-hospital mortality or transfer to an intensive care unit within 12 h. ED visits were randomly partitioned into a 70%/30% split for training and validation. The extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM) algorithms were constructed using variables selected from the stepwise logistic regression model. The XGBoost model was then compared to the traditional model and clinical decision rules (CDRs) on the validation data using the area under the curve (AUC). (3) Results: There were 3622 visits among 3235 unique patients diagnosed with UTI. Of the 2535 patients in the training group, 836 (33%) experienced critical outcomes, and of the 1087 patients in the validation group, 358 (32.9%) did. The AUCs for different machine learning models were as follows: XGBoost, 0.833; RF, 0.814; and SVM, 0.799. The XGBoost model performed better than others. (4) Conclusions: Machine learning models outperformed existing traditional CDRs for predicting critical outcomes of ED patients with UTI. Future research should prospectively evaluate the effectiveness of this approach and integrate it into clinical practice. Full article
(This article belongs to the Special Issue Urinary Tract Infections: Diagnosis and Management)
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12 pages, 241 KiB  
Article
Is It Safe to Treat Stable Patients with Bacteremic Urinary Tract Infections with High-Resistant-Rate Antibiotics?
by Zvi Shimoni, Hanna Salama, Talya Finn and Paul Froom
Diagnostics 2024, 14(15), 1620; https://doi.org/10.3390/diagnostics14151620 - 26 Jul 2024
Viewed by 1071
Abstract
Background and Objectives: In most areas of the world, urine bacteria have high resistance rates to third-generation cephalosporins, and it is unclear if it is safe to treat stable patients with bacteremic urinary tract infections (UTI) with those antibiotics. There are recommendations that [...] Read more.
Background and Objectives: In most areas of the world, urine bacteria have high resistance rates to third-generation cephalosporins, and it is unclear if it is safe to treat stable patients with bacteremic urinary tract infections (UTI) with those antibiotics. There are recommendations that empiric therapy for a suspected UTI should include only antibiotics with resistance rates less than 10%. Materials and Methods: In this historical observational single center study, we selected 180 stable internal medicine patients hospitalized between January 2019 and December 2021, with identical bacteria isolated from blood and urine cultures. Charts were reviewed to determine if deaths and readmissions up to 30 days after discharge were due to bacterial resistance to initial antibiotic therapy (BRIAT). Results: The patient’s median age was 82 years (1st–3rd quartiles, 73–87 years). A total of 54.4% were female. There were 125 patients treated with ceftriaxone. A total of 38 (30.3%) had BRIAT. Four patients died, but none were because of a delay in appropriate treatment. The median days of hospitalization for all patients was 7 days, and 9 days versus 6 days in those with and without BRIAT. There were no re-hospitalizations for a UTI in patients with BRIAT. Conclusions: We conclude that, despite high resistance rates, empiric ceftriaxone in stable hospitalized patients with a bacteremic UTI is safe. There was no urosepsis-related mortality during the hospitalization or on follow-up. The treatment of all patients with wider-spectrum antibiotics might have decreased the median hospital stay by only one day. The potential effect would be even lower if all patients with a suspected systemic UTI were treated with wide-spectrum antibiotics, because some patients do not have an infection of the urinary tract. A reassessment of the recommendation that empiric therapy for a suspected systemic urinary tract infection should include only wider-spectrum antibiotics is warranted. Full article
(This article belongs to the Special Issue Urinary Tract Infections: Diagnosis and Management)
11 pages, 253 KiB  
Article
Evaluating the Performance of FlukeCatcher at Detecting Urogenital Schistosomiasis
by Louis Fok, Berhanu Erko, David Brett-Major, Abebe Animut, M. Jana Broadhurst, Daisy Dai, John Linville, Bruno Levecke, Yohannes Negash and Abraham Degarege
Diagnostics 2024, 14(10), 1037; https://doi.org/10.3390/diagnostics14101037 - 17 May 2024
Cited by 1 | Viewed by 1046
Abstract
Urine filtration microscopy (UFM) lacks sensitivity in detecting low-intensity Schistosoma haematobium infections. In pursuit of a superior alternative, this study evaluated the performance of FlukeCatcher microscopy (FCM) at detecting S. haematobium eggs in human urine samples. Urine samples were collected from 572 school-age [...] Read more.
Urine filtration microscopy (UFM) lacks sensitivity in detecting low-intensity Schistosoma haematobium infections. In pursuit of a superior alternative, this study evaluated the performance of FlukeCatcher microscopy (FCM) at detecting S. haematobium eggs in human urine samples. Urine samples were collected from 572 school-age children in Afar, Ethiopia in July 2023 and examined using UFM and FCM approaches. Using the combined UFM and FCM results as a reference, the sensitivity, negative predictive value, and agreement levels for the two testing methods in detecting S. haematobium eggs in urine samples were calculated. The sensitivity and negative predictive value of detecting S. haematobium eggs in urine samples for FCM was 84% and 97%, respectively, compared to 65% and 93% for UFM. The FCM test results had an agreement of 61% with the UFM results, compared to 90% with the combined results of FCM and UFM. However, the average egg count estimates were lower when using FCM (6.6 eggs per 10 mL) compared to UFM (14.7 eggs per 10 mL) (p < 0.0001). Incorporating FCM into specimen processing could improve the diagnosis of S. haematobium infection but may underperform in characterizing the intensity of infection. Full article
(This article belongs to the Special Issue Urinary Tract Infections: Diagnosis and Management)
11 pages, 1190 KiB  
Article
Atypical Presentation of Bacteremic Urinary Tract Infection in Older Patients: Frequency and Prognostic Impact
by Caroline Laborde, Julien Bador, Arthur Hacquin, Jérémy Barben, Sophie Putot, Patrick Manckoundia and Alain Putot
Diagnostics 2021, 11(3), 523; https://doi.org/10.3390/diagnostics11030523 - 15 Mar 2021
Cited by 9 | Viewed by 3694
Abstract
In older patients, urinary tract infection (UTI) often has an atypical clinical presentation, making its diagnosis difficult. We aimed to describe the clinical presentation in older inpatients with UTI-related bacteremia and to determine the prognostic impact of atypical presentation. This cohort study included [...] Read more.
In older patients, urinary tract infection (UTI) often has an atypical clinical presentation, making its diagnosis difficult. We aimed to describe the clinical presentation in older inpatients with UTI-related bacteremia and to determine the prognostic impact of atypical presentation. This cohort study included all consecutive patients older than 75 years hospitalized in a university hospital in 2019 with a UTI-related gram-negative bacillus (GNB) bacteremia, defined by blood and urine cultures positive for the same GNB, and followed up for 90 days. Patients with typical symptoms of UTI were compared to patients with atypical forms. Among 3865 inpatients over 75 with GNB-positive urine culture over the inclusion period, 105 patients (2.7%) with bacteremic UTI were included (mean age 85.3 ± 5.9, 61.9% female). Among them, UTI symptoms were reported in only 38 patients (36.2%) and 44 patients (41.9%) had no fever on initial management. Initial diagnosis of UTI was made in only 58% of patient. Mortality at 90 days was 23.6%. After adjustment for confounders, hyperthermia (HR = 0.37; IC95 (0.14–0.97)) and early UTI diagnosis (HR = 0.35; IC95 (0.13–0.94)) were associated with lower mortality, while UTI symptoms were not associated with prognosis. In conclusion, only one third of older patients with UTI developing bacteremia had UTI symptoms. However, early UTI diagnosis was associated with better survival. Full article
(This article belongs to the Special Issue Urinary Tract Infections: Diagnosis and Management)
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