Predictors Of Urinary Tract Infection Research Articles

A multiple logistic model for prediction of urinary tract infections in an urban community: A public health perspective

Urinary tract infection (UTI) is one of the most common infectious diseases globally. A lot of clinical research has been done on UTI patients, but a questionnaire-based study on UTI is scarce. A cross-sectional study was conducted on outpatients with a high suspicion of uncomplicated UTI in Hayes Memorial Mission Hospital at Prayagraj (Eastern part of Northern India) to find out the frequency of symptoms and predisposing factors and their relationship towards the prediction of UTI. Logistic regression analysis showed a significant association between UTI and some of the variables. Also, the factors responsible for the occurrence of UTI are “gender”, “how many times you urinate from morning till night”, “a sudden desire to urinate, which is difficult to hold”, “weakness of urinary stream”, “splitting or spraying of the urinary stream” and “fever”. A statistical model (multiple logistic model) has been also established for the prediction of UTIs with an accuracy of 82.2%. It is also observed that the prevalence rate (odds ratio) of UTI in females is 2.38 times that of males. The study created a screening questionnaire for patients suspected of having UTI. A multiple logistic model has been established for the prediction of UTI which can be instrumental for clinicians from a public health perspective in the management of Urinary Tract Infections in this era of escalating AMR.

Prediction of upcoming urinary tract infection after intracerebral hemorrhage: a machine learning approach based on statistics collected at multiple time points.

Accurate prediction of urinary tract infection (UTI) following intracerebral hemorrhage (ICH) can significantly facilitate both timely medical interventions and therapeutic decisions in neurocritical care. Our study aimed to propose a machine learning method to predict an upcoming UTI by using multi-time-point statistics. A total of 110 patients were identified from a neuro-intensive care unit in this research. Laboratory test results at two time points were chosen: Lab 1 collected at the time of admission and Lab 2 collected at the time of 48 h after admission. Univariate analysis was performed to investigate if there were statistical differences between the UTI group and the non-UTI group. Machine learning models were built with various combinations of selected features and evaluated with accuracy (ACC), sensitivity, specificity, and area under the curve (AUC) values. Corticosteroid usage (p < 0.001) and daily urinary volume (p < 0.001) were statistically significant risk factors for UTI. Moreover, there were statistical differences in laboratory test results between the UTI group and the non-UTI group at the two time points, as suggested by the univariate analysis. Among the machine learning models, the one incorporating clinical information and the rate of change in laboratory parameters outperformed the others. This model achieved ACC = 0.773, sensitivity = 0.785, specificity = 0.762, and AUC = 0.868 during training and 0.682, 0.685, 0.673, and 0.751 in the model test, respectively. The combination of clinical information and multi-time-point laboratory data can effectively predict upcoming UTIs after ICH in neurocritical care.

Predictors of postoperative urinary tract infection following holmium laser enucleation of the prostate.

Storage urinary symptoms and urinary tract infection (UTI) are among the most common complications following holmium laser enucleation of the prostate (HoLEP). We aimed to study the incidence and risk factors for storage urinary symptoms and early UTI following HoLEP. A prospectively maintained database was reviewed for patients who underwent HoLEP over a five-year period at a single tertiary center. Patient demographics, preoperative, operative, and postoperative characteristics, as well as infection rates, were obtained and analyzed using the appropriate statistical methods. Of a total 514 patients who underwent HoLEP, 473 patients with complete followup data were included. Mean (± standard deviation) age and median (interquartile range) prostate volume were 72±9.1 years and 89 (68-126) g, respectively. Preoperative positive urine culture and urine retention were seen in 28.5% (n=135) and 23.46 % (n=111) of patients, respectively. At six-week followup, irritative urinary symptoms were seen in 32.3% (n=153) of patients, while 13.5% (n= 64) of patients had positive urine culture. Bivariate and multivariate analysis showed that factors associated with significant higher rate of postoperative UTI at six weeks were high body mass index (BMI) (p= 0.023), weak grip strength within preoperative frailty assessment (p=0.042), positive preoperative urine culture (p=0.025), and postoperative incontinence (p=0.002). Storage urinary symptoms are common complaints post-HoLEP; however, it may be caused by an inflammatory rather than infective process in a significant percentage of patients. Possible predictors of UTI after HoLEP are high BMI, preoperative positive urine culture, higher frailty scale, and postoperative urinary incontinence.

Clinically relevant and explainable feature extraction from remote monitoring data for the prediction of urinary tract infections

AbstractBackgroundA frequent cause for hospitalisation in people living with dementia (PLWD) is urinary tract infections (UTIs) (Dufour et al., 2015). Early detection aids in avoiding unplanned hospitalisations and machine learning and connected sensors enable development of risk analysis models based on in‐home monitoring data. This work makes use of data from the ongoing Minder study at the UK Dementia Research Institute, including in‐door movement, appliance use, physiology, sleep, and environmental information. Previous work (Li et al., 2020) shows complex models can achieve high accuracy when detecting UTIs, but interpretability and generalisability remain an open issue. This work evaluates how clinically interpretable features and simple models perform.MethodThe engineered features reflect visible symptoms of UTIs in older adults and are daily aggregated and pre‐processed information from PLWD’s homes containing passive sensors. 14 activity, sleep and physiological features were engineered relating to bathroom visits, entropy rate, physiological readings, and sleep behaviour. Raw data consisted of frequencies of the activity sensor firings. Their use in predicting cases of UTIs was compared by evaluating multilayer perceptron models on labelled data (643 negatives and 311 positives, from 39 PLWD), using 5‐fold cross‐validation (80%/20% train/test split), with 5 repeats. Prior to training and testing, the data was z‐normalised using unlabelled data. When evaluating, recall is most essential because reducing false positive rates is crucial in our setting.ResultThe highest performing model achieved the best accuracy, recall, precision and F1 score (85.6%, 69.9%, 81.5%, 74.8%) on combinations of engineered and raw features; though not significantly higher than with raw features alone (83.2%, 63.5%, 80.5%, 70.8%). Full results can be seen in the attached figure.ConclusionOur engineered features do not significantly improve results but add interpretability and generalisability to predictions. The models' hidden layers have a high capacity for learning patterns in the raw data, however, may not be clinically interpretable and we hypothesise that engineered features improve generalisation. Engineered features are homogeneous across the cohort and new data, and so create a way of representing the data which is not single device dependent. To verify this, we will continue our data collection and will conduct further experiments.

Prediction of Urinary Tract Infection in IoT-Fog Environment for Smart Toilets Using Modified Attention-Based ANN and Machine Learning Algorithms

UTI (Urinary Tract Infection) has become common with maximum error rates in diagnosis. With the current progress on DM (Data Mining) based algorithms, several research projects have tried such algorithms due to their ability in making optimal decisions and efficacy in resolving complex issues. However, conventional research has failed to attain accurate predictions due to improper feature selection. To resolve such existing pitfalls, this research intends to employ suitable ML (Machine Learning)-based algorithms for predicting UTI in IoT-Fog environments, which will be applicable to a smart toilet. Additionally, bio-inspired algorithms have gained significant attention in recent eras due to their capability in resolving complex optimization issues. Considering this, the current study proposes MFB-FA (Modified Flashing Behaviour-based Firefly Algorithm) for feature selection. This research initializes the FF (Firefly) population and interchanges the constant absorption coefficient value with the chaotic maps as the chaos possesses an innate ability to evade getting trapped in local optima with the improvement in determining global optimum. Further, GM (Gaussian Map) is taken into account for moving all the FFs to a global optimum in an individual iteration. Due to such nature, this algorithm possesses a better optimization ability than other swarm intelligence approaches. Finally, classification is undertaken by the proposed MANN-AM (Modified Artificial Neural Network with Attention Mechanism). The main intention for proposing this network involves its ability to focus on small and significant data. Moreover, ANNs possess the ability for learning and modelling complex and non-linear relationships, in which the present study considers it. The proposed method is compared internally by using Random Forest, Naive Bayes and K-Nearest Neighbour to show the efficacy of the proposed model. The overall performance of this study is assessed with regard to standard performance metrics for confirming its optimal performance in UTI prediction. The proposed model has attained optimal values such as accuracy as 0.99, recall as 0.99, sensitivity as 1, precision as 1, specificity as 0.99 and f1-score as 0.99.

Prevalence, Etiology, Antimicrobial Susceptibility Testing and Predictors of Urinary Tract Infection among Neonates with Clinical Sepsis

Background and Aim: The most common pediatric infection is urinary tract infection (UTI). Neonates who suffer from neonatal sepsis remain among the most vulnerable to death and morbidity, especially in developing countries. The present study aimed to determine the prevalence, etiology, and susceptible antimicrobial agents of UTIs among neonates with clinical sepsis. Patients and Methods: This cross-sectional study was carried out on 128 sepsis diagnosed neonates admitted to the Department of Pediatrics, Microbiology and Infectious Diseases, Niazi Welfare Teaching Hospital, Sargodha from September 2021 to May 2022. All neonates aged ≤ 28 days diagnosed of clinical sepsis and consent provided by their parents/guardian were enrolled. An interviewed based questionnaire was used for data collection from neonate’s parents or caregiver. The questionnaire included information on the socio-demographics and clinical presentations of neonates, as well as maternal history and laboratory examinations. The history is taken from prenatal to postnatal, including birth weight analysis and birth conditions. Neonates underwent general and local examinations to determine their state of consciousness, vital signs, and weight. We examined the abdomen (for tenderness and abdominal distention) and the nervous system (to elicit suckling and motor reflexes). A hematological analyzer was used to test the full blood picture (FBP). SPSS version 27 was used for data analysis. Results: Of the total 128 neonates, there were 76 (59.4%) females and 52 (40.6%) male neonates. The overall mean age was 6.4 ± 0.36 days with majority of neonates 112 (87.5%) were &lt; 7 days. The low birth weight and low Apgar score were found in 13 (10.2%) and 16 (12.5%) neonates respectively. Of all neonates, the failure to breastfeed was found in 26 (20.3%) being the most common clinical symptom followed by an increase in breathing rate 21 (16.4%), diarrhea 19 (14.8%), gritting 16 (12.5%), altered mental status 12 (9.4%), and jaundice 11 (8.6%). The incidence of UTI was 20.3% (n=26). The prevalence of separated bacterial agents such as Klebsiella pneumonia and Enterobacter spp. was 61.5% (n=16) and 38.5% (n=10) respectively. Ciprofoxacin and amikacin were both effective against 90% and 60% of the bacterial isolates, respectively. The UTI potential predictors involved prolong labour, positive leucocyte esterase test, Low Apgar score, and positive urine nitrite test. Conclusion: The present study found that the prevalence of UTI was 20.3%. Failure to breastfeed was the most common clinical symptom followed by an increase in breathing rate, diarrhea, gritting, altered mental status, jaundice, convulsion, and cyanosis. Klebsiella pneumonia and Enterobacter spp. were the most prevalent separated bacterial agents. Ciprofoxacin and amikacin were both effective in bacterial isolates. Keywords: UTI, Clinical sepsis, Neonates, Antibiotic sensitivity

Prevalence of urinary tract infections and risk factors among diabetic patients in Ethiopia, a systematic review and meta-analysis.

Urinary tract infection (UTI) is a common clinical problem that comprises 1-6% of medical referrals and includes urinary tract, bladder, and kidney infections. UTI is the most commonly occurring infectious disease in diabetic patients. Therefore, this systematic review and meta-analysis aimed to estimate the prevalence of urinary tract infection and its associated factors in Ethiopia. The online libraries of PubMed, Google Scholar, Scopus, and Science Direct, were searched. Data were extracted using Microsoft Excel and analyzed using STATA statistical software (v. 16). Forest plots, Begg's rank test, and Egger's regression test were all used to check for publication bias. To look for heterogeneity, I2 was computed, and an overall estimated analysis was carried out. Subgroup analysis was done by region, and publication year. Meta-regression analysis using study-level covariates as predictors of study-level estimates to explore the determinants of potential heterogeneity in our pooled estimates. The pooled odds ratio for related covariates was also calculated. Out of 1128 studies assessed, 14 met our criteria and were included in the study. A total of 3773 people were included in the study. The prevalence of urinary tract infection was estimated to be 15.97% (95% CI: 12.72-19.23). According to subgroup analysis, the highest prevalence was observed in the SNNP region (19.21%) and studies conducted in and after 2018 (17.98%). Being female (AOR = 3.77; 95% CI: 1.88, 5.65), being illiterate (AOR = 5.29; 95% CI: 1.98, 8.61), prior urinary tract infection history (AOR = 3.04; 95% CI: 2.16-3.92) were the predictor of urinary tract infection. The prevalence of urinary tract infections was high in Ethiopia. Female gender, illiteracy, and prior UTI history were associated with urinary tract infections. Since UTIs in diabetic patients has serious medical and public health consequence, screening of UTIs in diabetic patients and early initiation of treatment should become a public health priority.

Clinico-Microbiological Profile and Clinical Predictor of Urinary Tract Infection in Children: A Single-Center Study From Himalayan Foothills.

Urinary tract infection (UTI) in children is one of the commonest bacterial infections seen in the pediatric population. Clinical presentation ranges from fever with or without focus and isolation of microbiological agents streamline thetreatment. Moreover, local/regional microbial profiles are helpful in antibiotic selection, we conducted a study to assess the prevalence of urine culture positivity in a suspected case of UTI. In addition, antibiotic susceptibility patterns andultrasonography (USG) finding in culture-positive patients were also studied. It is a prospective observational study comprising symptomatic children aged one month to 18 years presenting to the outpatient department (OPD), inpatient department (IPD), and the emergency department of Pediatrics with UTI during the period of September 2019 to September 2020. The recorded variables were demographic, clinical presentation, anthropometry, physical examination, blood biochemistry, and outcome. Urine samples were collected and processed as per standard protocols. USG was done for all culture-positive children.Data were presented as frequency, mean (SD) and parametric and non-parametric data were analyzed byWilcoxon-Mann-Whitney U Test, Chi-Squared Test, or Fisher's Exact Test. Results:Of the total 354 children, 202 (57.1%) were male and the prevalence of UTI was 64 (18.1%). E. coli (70.3%) was the commonest isolated organism followed by Klebsiella spp (15.6%) and Pseudomonas spp (7%) respectively. The mean (SD) age (months) of presentation of symptoms was significantly lower in culture-positive children as compared to [ 83.49 (58.96) vs 110.10 (58.60); p=0.001] culture-negative children. Fever (96.6%) followed by dysuria (20.1%) were the most common symptoms presented for UTI however dysuria (p=0.003), pus cells (p<0.0001), and RBCs (p=0.002) were significantly present in culture positive children. This study shows increased resistance to third generation of cephalosporins. This study revealed significant differences among various groups (organism growth in positive culture) and the Antibiotic susceptibility test (AST) with a p-value of <0.001. Conclusion:The prevalence of culture-positive UTI was similar to the reported literature and the presence of fever, dysuria, pus cells, and RBC in urine were commonly observed in the lower age group. Amikacin can be used in suspected UTIs with cephalosporin as empirical antibiotics in the Himalayan Foothills region.

Pyuria Is Associated with Dysbiosis of the Urinary Microbiota in Type 2 Diabetes Patients Receiving Sodium–Glucose Cotransporter 2 Inhibitors

Treating type 2 diabetes (T2D) patients with sodium–glucose cotransporter 2 (SGLT2) inhibitors may be associated with an increased risk of urinary tract infections (UTIs), such as diabetes-induced asymptomatic bacteriuria. Pyuria—a condition wherein leukocytes are detected in the urine—is a predictor of UTIs. The aim of this study was to examine the urinary microbiome of Taiwanese T2D patients, with or without pyuria, undergoing SGLT2 treatment. We recruited seven T2D patients, recorded their clinical and biochemical characteristics, and collected their urine samples for 16S metagenomic sequencing. The primary outcomes were the diversity of urinary microbiota and the relative abundance of different species. We found that the microbiome of the pyuria group was significantly less diverse than the non-pyuria group (0.24 ± 0.04 vs. 2.21 ± 0.28, p = 0.002), while the number of operational taxonomic units did not differ significantly (763.5 ± 78.67 and 747 ± 141.3, p = 0.92). Escherichia-Shigella spp. dominated the microbiome of the pyuria group (97.4%–99.4%), and these patients tended to have more comorbidities. In conclusion, pyuria is associated with urinary microbiota dysbiosis in T2D patients being treated with SGLT2 inhibitors.

Machine learning-assisted ensemble analysis for the prediction of urinary tract infection in elderly patients with ovarian cancer after cytoreductive surgery

Urinary tract infection (UTI) is a common type of postoperative infection following cytoreductive surgery for ovarian cancer, which severely impacts the prognosis and quality of life of patients. To develop a machine learning assistant model for the prevention and control of nosocomial infection. A total of 674 elderly patients with ovarian cancer who were treated at the Department of Gynaecology at Jingzhou Central Hospital between January 31, 2016 and January 31, 2022 and met the inclusion criteria of the study were selected as the research subjects. A retrospective analysis of the postoperative UTI and related factors was performed by reviewing the medical records. Five machine learning-assisted models were developed using two-step estimation methods from the candidate predictive variables. The robustness and clinical applicability of each model were assessed using the receiver operating characteristic curve, decision curve analysis and clinical impact curve. A total of 12 candidate variables were eventually included in the UTI prediction model. Models constructed using the random forest classifier, support vector machine, extreme gradient boosting, and artificial neural network and decision tree had areas under the receiver operating characteristic curve ranging from 0.776 to 0.925. The random forest classifier model, which incorporated factors such as age, body mass index, catheter, catheter intubation times, blood loss, diabetes and hypoproteinaemia, had the highest predictive accuracy. These findings demonstrate that the machine learning-based prediction model developed using the random forest classifier can be used to identify elderly patients with ovarian cancer who may have postoperative UTI. This can help with treatment decisions and enhance clinical outcomes.

Related factors of urinary tract infections in inpatients based on real world data

To analyze the risk factors for urinary tract infection (UTI) among inpatients. The case data of 1 875 inpatients receiving urinary bacterial culture in Beijing Haidian Hospital from October 2019 to May 2021 were analyzed retrospectively. According to the etiological diagnostic criteria of UTI, they were divided into infection group and non-infection group. The species and distribution of pathogens in the infection group were analyzed, and the case data and laboratory indexes were subjected to univariate analysis. The variables with statistical significance were selected for binary logistic regression to analyze the risk factors of urinary tract infection and establish a prediction model. The receiver operating characteristic (ROC) curve was drawn for each parameter included in the model, and the area under the curve (AUC) was calculated. The diagnostic and predictive efficacy of each parameter alone and their combination for UTI were evaluated. So, a total of 1 162 patients with non-infection group and 713 patients with UTI were detected. Among the cultured pathogens, the constituent ratio of Gram-negative bacteria, Gram-positive bacteria and fungi was 57.2%(408/713), 35.9%(256/713) and 6.9%(49/713) respectively. Multivariate analysis showed that, Age, duration of urinary catheterization>7 d, stroke and orthopedic surgery were the risk factors of UTI among inpatients. The use of antibiotics is a protective factor for urinary tract infections. The prediction model of UTI was established by the risk factors, age, duration of urinary catheterization>7 d, stroke, orthopedic surgery, urinary leukocyte esterase, urinary nitrite and Coefficient of variability of red blood cell volume distribution width (RDW-CV). The AUC of the combination of the eight parameters in diagnosing and predicting UTI was 0.835 (95%CI: 0.816-0.855), with the sensitivity of 70.7% and the specificity of 82.8%. In conclusion,the combination of the eight parameters can better assist in the diagnosis and prediction of UTI, and provide an experimental basis for clinicians to judge UTI.

Prediction of Urinary Tract Infection in Neonates with Unexplained Indirect Hyperbilirubinemia

Background: Neonates with urinary tract infection (UTI) are susceptible to higher rates of morbidity and mortality, specifically when presented with hyperbilirubinemia. Screening for UTIs in jaundiced neonates is a cost-effective strategy. The aims of this study were to investigate the pattern of UTI (prevalence, etiology, and susceptible antimicrobial agents) in neonates admitted to the NICU with unexplained indirect hyperbilirubinemia, as well as to identify early predictors of UTI in order to reduce the present morbidity and long-term consequences in NICU patients.&#x0D; . Methods: A cross-sectional hospital-based study that included 140 neonates diagnosed with unexplained indirect hyperbilirubinemia in the first 4 weeks of life. A questionnaire was applied to obtain demographic and clinical data. A number of laboratory parameters were assessed with clinical examination. Bacterial growth of 1 × 103 colony-forming units/mL of a single uropathogen was used to identify the existence of UTI. Multivariate analysis was used to identify the predicting factors of UTIs. Results: In the NICU group investigated, 25.7% of subjects had a culture-proved UTI. The most frequently isolated organism was Escherichia coli. Amikacin was the most common antibiotic that the isolates were susceptible to. In multivariable logistic regression analysis, a positive urine culture was statistically associated with an increase in WBCs (OR= 6.90, p= 0.001), pyuria (OR= 5.55, p= 0.001), small for gestational age (OR= 4.07, p= 0.021), prolonged phototherapy duration (OR= 3.50, p= 0.034), and the presence of obstetric complications (OR= 2.92, p= 0.001). Conclusion: UTI is substantially prevalent among neonates admitted to the NICU with unexplained indirect hyperbilirubinemia. The importance of routine UTI screening (urine culture) as part of the clinical assessment of unexplained hyperbilirubinemia was highlighted in this study, particularly in neonates with leukocytosis, pyuria, small for gestational age, prolonged phototherapy, and those born from mothers with a history of obstetric complications.&#x0D;

Prevalence, aetiology, antimicrobial susceptibility testing, and predictors of urinary tract infection among neonates with clinical sepsis: a cross-sectional study

BackgroundUrinary tract infection (UTI) is the most common and life-threatening bacterial infection among neonates. This study aimed to determine the prevalence, aetiology, and susceptible antimicrobial agents among neonates with UTI.MethodsThis was a cross-sectional analytical hospital-based study that included 152 neonates with clinical sepsis who were admitted at Dodoma regional referral hospital from January to June 2020. Bacterial growth of 1 × 103 colony forming units/mL of a single uropathogen was used to define the presence of UTI. Statistical analysis was performed using SPSS version 23.0 and multivariate analysis was used to determine the predicting factors of UTI. P <0.05 was regarded statistically significant.ResultsThe prevalence of UTI was 18.4% (28/152). Klebsiella pneumoniae 64.3% (18/28) and Enterobacter spp. 35.7% (10/28) were the bacterial agents isolated. The bacterial isolates were 90%, and 60% sensitive to ciprofloxacin and amikacin, respectively. Low Apgar score (AOR = 12.76, 95% CI = 4.17–39.06, p<0.001), prolonged labour (AOR = 5.36, 95% CI = 1.28–22.52, p = 0.022), positive urine nitrite test (AOR = 26.67, 95% CI = 7.75–91.70, p<0.001), and positive leucocyte esterase test (AOR = 6.64, 95% CI = 1.47–29.97, p = 0.014) were potential predictors of UTI.ConclusionThe prevalence of UTI confirmed by urine culture among neonates that were included in the present study indicates that this problem is common in the population where the study was conducted. Klebsiella pneumoniae and Enterobacter spp. were the uropathogens which were isolated. Ciprofloxacin, nitrofurantoin, and amikacin were sensitive to the isolated uropathogens.

Reported Cases of Urinary Tract Infections and the Susceptibility of Uropathogens from Hospitals in Northern Ghana.

As global studies report varying trends in antibiotic susceptibility of uropathogens, it is necessary to have current and constant information on the prevalence of urinary tract infections, the causative pathogens, and their susceptibility profiles, for effective management in specific geographical settings. This prospective cross-sectional study focused on the prevalence of urinary tract infections, etiological agents, and their antibiogram in a secondary and tertiary care hospital in Northern Ghana. Urine samples collected from 219 patients of all age groups were cultured on cysteine lactose electrolyte deficient agar. Pathogens were identified following standard microbiological methods, and their susceptibility to antibiotics was determined by the Kirby-Bauer disk diffusion method. Approximately 34% of the patients had significant bacteria, but the prevalence was slightly higher (P = .763) in the Tertiary care hospital (37.3%) than in the Secondary hospital (30.3%). Patients who were 60 years and above (27.0%) were commonly found with UTIs followed by the year group 20 to 29 years (20.3%). Although all the diagnoses had a positive relationship with urinary tract infection except Pyelonephritis, none of the underlying conditions was a significant (P > .05) predictor of urinary tract infection, with the odds ratio indicating that patients with hyperparathyroidism and dysuria had 2.606 times more likely increased risk or predictor of urinary tract infection. Ten different pathogens were identified, but Escherichia coli and Staphylococcus saprophyticus were frequently encountered. Gram-negative isolates generally showed more resistance. High resistance against ampicillin (100%), trimethoprim-sulfamethoxazole (88.5%), chloramphenicol (84.6%), augmentin (69.2%), ceftriaxone (69.2%), and ciprofloxacin (61.5%) were recorded. Amikacin was relatively effective against isolated pathogens. The high records of resistance among uropathogens and the occurrence of multidrug resistance (92%) reiterate the urgent call for rigorous surveillance of antimicrobial resistance among infectious pathogens in Ghana.

MP25-16 IMPACT OF DURATION OF ANTIBIOTIC PROPHYLAXIS ON UTI RATE AFTER ONABOTULINUMTOXINA INJECTION

You have accessJournal of UrologyInfections/Inflammation/Cystic Disease of the Genitourinary Tract: Kidney & Bladder I (MP25)1 Sep 2021MP25-16 IMPACT OF DURATION OF ANTIBIOTIC PROPHYLAXIS ON UTI RATE AFTER ONABOTULINUMTOXINA INJECTION Sarah Martin, Jacqueline Zillioux, Neil Kocher, Raymond Rackley, Sandip Vasavada, Howard Goldman, Katie Propst, Eiftu Haile, and Emily Slopnick Sarah MartinSarah Martin More articles by this author , Jacqueline ZilliouxJacqueline Zillioux More articles by this author , Neil KocherNeil Kocher More articles by this author , Raymond RackleyRaymond Rackley More articles by this author , Sandip VasavadaSandip Vasavada More articles by this author , Howard GoldmanHoward Goldman More articles by this author , Katie PropstKatie Propst More articles by this author , Eiftu HaileEiftu Haile More articles by this author , and Emily SlopnickEmily Slopnick More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002022.16AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: There are currently no specific guidelines on antibiotic prophylaxis for intravesical OnabotulinumtoxinA (BTX) injection, despite high risk of post-procedural urinary tract infection (UTI). Our objective was to assess the impact of duration of antibiotic prophylaxis on rate of UTI within 30 days of BTX injection. METHODS: A retrospective chart review was performed of patients who received office BTX injection from 2014-2020 with FPMRS providers at a single academic institution. UTI rates within 30 days of BTX were compared between three prophylaxis regimens: no antibiotic, a single dose, or a multiple day course of any antibiotic. UTI was defined as new lower urinary tract symptoms treated either empirically or associated with a positive urine culture. Patients on daily antibiotic prophylaxis for recurrent UTI were excluded. Association of UTI with units of BTX, BMI, history of diabetes, immunosuppresion, neurogenic bladder or recurrent UTI and incidence of new post-procedure urinary retention were also investigated. Statistical analysis was performed with univariate and subsequent multivariable logistic regression analysis to assess for possible confounding factors. RESULTS: A total of 180 patients and 560 cycles of BTX injections were included: 173 (96%) were women with mean age 62.9 years (range 20-95; SD 12.6). The majority received 100U (65%) for a non-neurogenic diagnosis (67%). Overall rates of UTI and urinary retention within 30 days were 11% and 6%, respectively. No antibiotic prophylaxis was given to 79 (14%) patients, 343 (61%) received a single dose, and 138 (25%) received a multiple day regimen (3-7 days). On univariate analysis, UTI incidence was lower with administration of any antibiotic prophylaxis as compared to none: single dose 7.6% vs. 25.3% (OR 0.24, p<0.001), multiple dose 11.0% vs. 25.3% (OR 0.36, p=0.007). There was no difference in UTI rates between single and multiple dose antibiotic regimens (p=0.233). On multivariable logistic regression, use of any antibiotic prophylaxis was associated with a lower incidence of UTI (single OR 0.32, p=0.002; multiple OR 0.42, p=0.042), with no difference between single and multiple dose regimens (p=0.068). History of recurrent UTI (OR 2.3, p=0.024) and de novo post-procedure retention (OR 3.9, p=0.002) were predictors of UTI. CONCLUSIONS: Use of antibiotic prophylaxis for office intravesical BTX injection is associated with a significantly lower incidence of UTI compared to no antibiotic. Importantly, a multiple day regimen was not more effective than a single dose of antibiotics in preventing UTI. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e459-e460 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Sarah Martin More articles by this author Jacqueline Zillioux More articles by this author Neil Kocher More articles by this author Raymond Rackley More articles by this author Sandip Vasavada More articles by this author Howard Goldman More articles by this author Katie Propst More articles by this author Eiftu Haile More articles by this author Emily Slopnick More articles by this author Expand All Advertisement PDF downloadLoading ...

Influence of Sex on Early Outcomes of Elective Lumbar Fusions: An Updated Propensity-Matched and Subgroup Analysis

Existing data have demonstrated significant differences in morbidity and mortality measures between men and women undergoing various spinal surgeries. However, studies of lumbar fusion surgery have been limited. Thus, we investigated the effects of patient sex on 30-day perioperative outcomes after elective lumbar fusion spine surgery. Patients who had undergone lumbar fusion from 2015 to 2018 were reviewed from the American College of Surgeons National Surgical Quality Improvement Program database. Propensity score matching was used to determine whether the patient's sex had influenced the 30-day perioperative complications. A total of 44,526 cases had met the inclusion criteria and were reviewed. Of the 44,526 patients, 13,715 had undergone posterior lumbar fusion, 21,993 had undergone posterior/transforaminal lumbar interbody fusion, and 8818 had undergone anterior/lateral lumbar interbody fusion. The women were more likely to be older, functionally dependent, and taking steroids for chronic conditions and to have a higher body mass index and lower preoperative hematocrit level. The men were more likely to be white, to smoke, and to have diabetes mellitus, hypertension, and bleeding disorders. In all cohorts, except for a higher incidence of urinary tract infection in the female patients and myocardial infarction in the male patients, no significant differences were found in morbidity and mortality between the sexes. Several differences in demographics and baseline health status were found between men and women undergoing lumbar fusion. When attempting to control for comorbid conditions using propensity score matching, we found that sex was an independent predictor of urinary tract infection in women and myocardial infarction in men across major morbidity and mortality categories in patients undergoing lumbar fusion surgery.

Constipation on abdominal radiograph as potential risk factor for recurrent urinary tract infection development.

To compare clinical history and measurements of fecal load on abdominal radiography (AR) in the prediction of urinary tract infection (UTI) recurrence in children. We combined data from two multicenter longitudinal studies in which children less than 6 years of age with a first or second UTI were followed for recurrence of UTI. Two radiologists reviewed the scout abdominal radiographs of initial voiding cystourethrograms obtained at enrollment from children at two participating sites and measured stool visible in various parts of the colon. We examined how well clinical variables (e.g., voiding and bowel history, use of laxatives at enrollment) and measurements of fecal load predicted recurrence of UTI within 12 months of enrollment. One hundred and ninety-two children were included. On univariate analyses, age, vesicoureteral reflux (VUR), cecal diameter, rectal diameter, and total stool length on AR were associated with recurrence of UTI. After controlling for age, the odds of recurrent UTI in children with VUR at baseline was 3.85 (95% CI: 1.62, 9.14) higher than in children without VUR. Recurrent UTI was 2.57 (95% CI: 1.01, 6.55) times more likely in children with cecal diameter > 3.10 cm than children with lower cecal diameters; time to first recurrent UTI was shorter in children with elevated cecal diameters (p = 0.0023). Cecal diameter on abdominal radiographs predicts UTI recurrence in children with a previous UTI. However, its accuracy is suboptimal to serve as a screening test. Accordingly, its routine use for this indication is not supported. If cecal diameter on an AR ordered for another indication is > 3.10 cm, then management of constipation could be considered.

Prevalence and predictors of urinary tract infection in children with cerebral palsy

Prevalence and predictors of urinary tract infection in children with cerebral palsy

Can Immature Granulocyte Count and Hemogram Indices be Good Predictors of Urinary Tract Infections in Children?

Can Immature Granulocyte Count and Hemogram Indices be Good Predictors of Urinary Tract Infections in Children?

ESTABLISHING AND EVALUATING THE ARTIFICIAL NEURAL NETWORK MODEL FOR THE PREDICTION OF URINARY TRACT INFECTION AFTER UPPER URINARY CALCULI SURGERY

Objective:To establish and evaluate the artificial neural network (ANN) model for the prediction of urinary tract infection after upper urinary calculi surgery. Methods: A total of 350 patients with upper urinary tract stones were collected and divided into training group (n=280) and test group (n=70) according to the proportion of 4:1.The logistic regression (LR) model wasused to screen the data by multivariate analysis. Factors with statistically significant would be screened out to establish the LR model and artificial neural network (ANN) model.Receivers operation curve (ROC) was used to evaluate the predictive effect of the model. Results: A total of 29 cases (10.36%) developed postoperative urinary tract infections. Further analysis by logistic regression revealed that the following indicators were independent risk factors for urinary tract infection after upper urinary calculi surgery: infectious stones (OR=3.58, 95%CI=2.04-4.87 p&lt;0.001), operation time (OR=1.51, 95%CI=1.11-2.06 p=0.01), preoperative urine white blood cells and nitrite are both positive (uWBC+NIT+) (OR=1.97, 95%CI=1.55-2.76 p=0.005) , female patients (OR=1.55, 95%CI=1.03-2.35 p=0.04), preoperative positive urine culture (OR=1.33, 95%CI=1.20-1.73 p=0.03) and calculus with polyp encapsulation(OR=1.11, 95%CI=1.05-1.21 p=0.03). Furthermore, we have successfully established the ANN model for the predicting of urinary tract infection after upper urinary calculi surgery. When compared ANN model to LR model for the accurate of prediction by 70 patients in the test group, the accurateof prediction were 78.92% (LR) and 88.69% (ANN), respectively. Moreover, the area under the ROC curve (AUC) of ANN model was greater than the LR model (0.93±0.02vs0.74±0.05, p&lt;0.01). Conclusion: The established ANN model owned highly effectiveness and accuracy in the predicting of urinary tract infection after upper urinary calculi surgery.