AI Article Synopsis
- The study evaluates an updated algorithm designed to accurately detect urinary tract infections (UTI) in multiple sclerosis patients before administering high-dose corticosteroids during acute relapses, aiming to reduce unnecessary antibiotic use.
- After analyzing 299 patients, it was found that 11% had significant bacteriuria, and the algorithm demonstrated a sensitivity of 24% and specificity of 94%, with an overall accuracy rate of 87%.
- The newly implemented algorithm effectively decreased unnecessary antibiotic prescriptions while maintaining safety, as no adverse effects were reported in those treated with methylprednisolone despite having untreated UTIs.
Article Abstract
Introduction: To evaluate an updated algorithm in the detection of urinary tract infection (UTI) prior to high-dose corticosteroid treatment in acute relapses in multiple sclerosis (MS). This updated algorithm aimed to decrease the unnecessary use of antibiotics, whilst maintaining accuracy and safety.
Methods: Prospective cohort study of 471 consecutive patients with MS relapses in a hospital-based outpatient acute relapse clinic. 172 patients met exclusion criteria, leaving 299 patients for analysis. Patients underwent urine dipstick and were treated for UTI if 2 or more of: nitrites, leukocyte esterase and cloudy urine were positive. Patients with confirmed acute MS relapse were treated with high dose intravenous or oral methylprednisolone.
Results: Significant bacteriuria (>10 colony forming units/mL) was present in 33 (11%, 95% CI 8-15) patients. The algorithm sensitivity and specificity was 24% and 94% respectively; the negative predictive value was 91%. The overall accuracy of the algorithm was 87%. No adverse sequelae were identified in 25 patients who received high dose methylprednisolone in the presence of an untreated UTI.
Conclusion: With an improved specificity, this updated algorithm addresses previous issues concerning the unnecessary prescription of antibiotics, whilst improving accuracy and maintaining safety.
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| http://dx.doi.org/10.1016/j.jns.2019.116456 | DOI Listing |
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