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Introduction: Bacterial urinary tract infections (UTI) and some sexually transmitted infections (STI) can have overlapping signs and symptoms or nonspecific findings, such as pyuria on urinalysis. Furthermore, results from the urine culture and the nucleic acid amplification test for an STI may not be available during the clinical encounter. We sought to determine whether gonorrhea, chlamydia, and trichomoniasis are associated with bacteriuria, information that might aid in the differentiation of STIs and UTIs.Methods: We used multinomial logistic regression to analyze 9,650 encounters of female patients whowere aged ≥18 years and who underwent testing for STIs. The ED encounters took place from April 18, 2014–March 7, 2017. We used a multivariable regression analysis to account for patient demographics, urinalysis findings, vaginal wet-mount results, and positive or negative (or no) findings from the urine culture and testing for Neisseria gonorrhoeae, Chlamydia trachomatis, or Trichomonas vaginalis.Results: In multivariable analysis, infection with T vaginalis, N gonorrhoeae, or C trachomatis was not associated with having a urine culture yielding 10,000 or more colony-forming units per mililiter (CFU/mL) of bacteria compared with a urine culture yielding less than 10,000 CFU/mL or no urine culture obtained. The diagnosis of a UTI in the ED was not associated with having a urine culture yielding 10,000 or more CFU/mL compared with a urine culture yielding less than 10,000 CFU/mL.Conclusion: After adjusting for covariates, no association was observed between urine culture results and testing positive for trichomoniasis, gonorrhea, or chlamydia. Our results suggest that having a concurrent STI and bacterial UTI is unlikely.

Introduction: Urinary tract infections (UTI) are one of the most common infections encountered in the emergency department (ED) with an estimated 2-3 million annual visits.  Commonly prescribed antibiotics for UTIs have shown growing rates of resistance.  Previous studies lack direction on improving UTI treatment based on the labs available to the bedside clinician.   Methods: We sought to determine if antibiotic resistance in UTIs was related to demographics, urinalysis, and history of renal failure or kidney stones. We conducted an analysis of 892 women ≥18 years of age discharged from the ED with a UTI diagnosis. We assessed predictors of nitrofurantoin resistance, cefazolin resistance, ciprofloxacin resistance, and trimethoprim-sulfamethoxazole resistance using unadjusted and multivariable logistic regression models. Results: Antibiotic resistance was 13.6% for nitrofurantoin, 11.9% for cefazolin, 12.8% for ciprofloxacin, and 17.1% for trimethoprim-sulfamethoxazole. In multivariable analysis, significant independent associations with an increased likelihood of resistance to nitrofurantoin were observed for less urine blood (OR [per 1 category increase of score] 0.81; P = 0.02); greater mucous (OR [per 1 category increase of score] 1.22; P = 0.02); less specific gravity urine (OR [per 1 category increase] 0.87; P = 0.04), and presence of any history of kidney stones (OR 3.24; P = 0.01). There were no significant predictors for cefazolin resistance (all P ≥0.06); age was the only significant predictor of ciprofloxacin resistance (OR per 10 year increase] 1.10, P = 0.05), and lower specific gravity urine was significantly associated with an increased risk of resistance to trimethoprim- sulfamethoxazole (OR [per 1 category increase] 0.88, P = 0.04). Conclusion: Women with any history of kidney stones may have bacteriuria resistant to nitrofurantoin, suggesting that providers might consider alternative antibiotic therapies in this scenario.

Objective: An artificial intelligence (AI) algorithm has been developed to detect the electrocardiographic signature of atrial fibrillation (AF) present on an electrocardiogram (ECG) obtained during normal sinus rhythm. We evaluated the ability of this algorithm to predict incident AF in an emergency department (ED) cohort of patients presenting with palpitations without concurrent AF.Methods: This retrospective study included patients 18 years and older who presented with palpitations to one of 15 ED sites and had a 12‑lead ECG performed. Patients with prior AF or newly diagnosed AF during the ED visit were excluded. Of the remaining patients, those with a follow up ECG or Holter monitor in the subsequent year were included. We evaluated the performance of the AI-ECG output to predict incident AF within one year of the index ECG by estimating an area under the receiver operating characteristics curve (AUC). Sensitivity, specificity, and positive and negative predictive values were determined at the optimum threshold (maximizing sensitivity and specificity), and thresholds by output decile for the sample.Results: A total of 1403 patients were included. Forty-three (3.1%) patients were diagnosed with new AF during the following year. The AI-ECG algorithm predicted AF with an AUC of 0.74 (95% CI 0.68-0.80), and an optimum threshold with sensitivity 79.1% (95% Confidence Interval (CI) 66.9%-91.2%), and specificity 66.1% (95% CI 63.6%-68.6%).Conclusions: We found this AI-ECG AF algorithm to maintain statistical significance in predicting incident AF, with clinical utility for screening purposes limited in this ED population with a low incidence of AF. [ABSTRACT FROM AUTHOR]