Development and validation of a parsimonious prediction model for positive urine cultures in outpatient visits.

Urine culture is often considered the gold standard for detecting the presence of bacteria in the urine. Since culture is expensive and often requires 24-48 hours, clinicians often rely on urine dipstick test, which is considerably cheaper than culture and provides instant results. Despite its ease of use, urine dipstick test may lack sensitivity and specificity. In this paper, we use a real-world dataset consisting of 17,572 outpatient encounters who underwent urine cultures, collected between 2015 and 2021 at a large multi-specialty hospital in Abu Dhabi, United Arab Emirates. We develop and evaluate a simple parsimonious prediction model for positive urine cultures based on a minimal input set of ten features selected from the patient's presenting vital signs, history, and dipstick results. In a test set of 5,339 encounters, the parsimonious model achieves an area under the receiver operating characteristic curve (AUROC) of 0.828 (95% CI: 0.810-0.844) for predicting a bacterial count ≥ 105 CFU/ml, outperforming a model that uses dipstick features only that achieves an AUROC of 0.786 (95% CI: 0.769-0.806). Our proposed model can be easily deployed at point-of-care, highlighting its value in improving the efficiency of clinical workflows, especially in low-resource settings.

Machine Learning Models Versus the National Early Warning Score System for Predicting Deterioration: Retrospective Cohort Study in the United Arab Emirates.

BACKGROUND: Early warning score systems are widely used for identifying patients who are at the highest risk of deterioration to assist clinical decision-making. This could facilitate early intervention and consequently improve patient outcomes; for example, the National Early Warning Score (NEWS) system, which is recommended by the Royal College of Physicians in the United Kingdom, uses predefined alerting thresholds to assign scores to patients based on their vital signs. However, there is limited evidence of the reliability of such scores across patient cohorts in the United Arab Emirates. OBJECTIVE: Our aim in this study was to propose a data-driven model that accurately predicts in-hospital deterioration in an inpatient cohort in the United Arab Emirates. METHODS: We conducted a retrospective cohort study using a real-world data set that consisted of 16,901 unique patients associated with 26,073 inpatient emergency encounters and 951,591 observation sets collected between April 2015 and August 2021 at a large multispecialty hospital in Abu Dhabi, United Arab Emirates. The observation sets included routine measurements of heart rate, respiratory rate, systolic blood pressure, level of consciousness, temperature, and oxygen saturation, as well as whether the patient was receiving supplementary oxygen. We divided the data set of 16,901 unique patients into training, validation, and test sets consisting of 11,830 (70%; 18,319/26,073, 70.26% emergency encounters), 3397 (20.1%; 5206/26,073, 19.97% emergency encounters), and 1674 (9.9%; 2548/26,073, 9.77% emergency encounters) patients, respectively. We defined an adverse event as the occurrence of admission to the intensive care unit, mortality, or both if the patient was admitted to the intensive care unit first. On the basis of 7 routine vital signs measurements, we assessed the performance of the NEWS system in detecting deterioration within 24 hours using the area under the receiver operating characteristic curve (AUROC). We also developed and evaluated several machine learning models, including logistic regression, a gradient-boosting model, and a feed-forward neural network. RESULTS: In a holdout test set of 2548 encounters with 95,755 observation sets, the NEWS system achieved an overall AUROC value of 0.682 (95% CI 0.673-0.690). In comparison, the best-performing machine learning models, which were the gradient-boosting model and the neural network, achieved AUROC values of 0.778 (95% CI 0.770-0.785) and 0.756 (95% CI 0.749-0.764), respectively. Our interpretability results highlight the importance of temperature and respiratory rate in predicting patient deterioration. CONCLUSIONS: Although traditional early warning score systems are the dominant form of deterioration prediction models in clinical practice today, we strongly recommend the development and use of cohort-specific machine learning models as an alternative. This is especially important in external patient cohorts that were unseen during model development.

Artificial urinary sphincters for treating postprostatectomy incontinence: A contemporary experience from the UK.

OBJECTIVES: To report a real-time contemporary practice and outcome of artificial urinary sphincter (AUS) in patients with postradical prostatectomy urinary incontinence (PPI) in the UK. METHODS: A retrospective observational study of patients who underwent AUS implantation (AMS 800) from 2007 to 2013. Data were collected on patients' demographics, infection and erosion rate, mechanical failure, reoperation, and continence rates. The study strictly included patients with postradical PPI only. Minimum follow-up was 15 months. RESULTS: Eighty-four AUSs were implanted over a period of 6 years. Patients' age ranged between 51 and 78 (median 69, mean 69.25) years. Median follow-up was 37 months, mean 39 months, and range 15-92 months. Among the 83 follow-up patients, 38.5% (32/83) reported that they were completely dry with no pads; 42.2% (35/83) of patients were socially continent (using 1 pad/day) and 19.3% (16/83) using ≥2 pads/day. One patient was lost to follow-up. Reoperation rate was 13.25% (11/83), including nine mechanical failures (10.8%). Two implant infections (2.4%) required explantation, out of which one had erosion (1.2%). Bladder overactivity developed in 6% of patients. Of the 83, 15 (18%) had pelvic radiotherapy. CONCLUSION: The implantation of AUS in patients with post-PPI has lower complications and reoperation rates than historical impression painted in the literature. This can be beneficial in counseling as well as during the education process of patients going through the decision process for prostate cancer treatment. Furthermore, these figures can add to our quest to increase the awareness of the success of anti-incontinence surgery among patients.

A report of a regional service for post-prostatectomy urinary incontinence: a model for best practice?

INTRODUCTION: Post-prostatectomy incontinence (PPI) is a potentially highly significant complication of a common urological procedure. Pathophysiology may be multifactorial but most commonly involves urinary sphincter weakness. The gold standard treatment for severe incontinence is artificial urinary sphincter but multiple alternatives exist. The growing incidence of PPI has led to the development of a specialized regional service dedicated to management. PATIENTS AND METHODS: In 2004 a regional referral protocol for PPI was established with a dedicated clinic at a single centre for assessment and management including videourodynamics, pelvic floor rehabilitation, biofeedback and a consultant with a specialist interest in PPI surgery. Data regarding all in-house and tertiary referrals to this clinic between 2004 and 2011 were analysed with patients categorized by symptom severity. RESULTS: A total of 267 patients were referred to the post-prostatectomy service (mean age 66.6, range 49-83 years) with numbers increasing year on year. Two-thirds of these were tertiary referrals: 27.7% of referrals were for mild symptoms, 35.2% moderate and 33.3% severe. One-third of referrals were made within 2 years of the primary procedure. Just over half of referred patients underwent invasive treatment including 24.3 artificial sphincter (24.3%) and male slings (22.8%). 7.5% patients were managed with medication, 14.6% were managed conservatively with containment therapy only. One-fifth remain under assessment or have deferred treatment. CONCLUSION: PPI is of increasing personal and societal impact which should be identified early and supported. Investigation and management can be standardized and intervention at a high volume centre achieved by early specialist referral.

Post-radical-prostatectomy urinary incontinence: the management of concomitant bladder neck contracture.

Urinary incontinence postradical prostatectomy is a common problem which adversely affects quality of life. Concomitant bladder neck contracture in the setting of postprostatectomy incontinence represents a challenging clinical problem. Postprostatectomy bladder neck contracture is frequently recurrent and makes surgical management of incontinence difficult. The aetiology of bladder neck contracture and what constitutes the optimum management strategy are controversial. Here we review the literature and also present our approach.