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A clinical decision support system improves antibiotic therapy for upper urinary tract infection in a randomized single-blinded study.

ABSTRACT: BACKGROUND:Due to increasing bacterial resistance rates choosing a correct empiric antibiotic therapy is getting more and more complex. Often medical doctors use information tools to make the right treatment choice. METHODS:One hundred sixty six participants (77 medical doctors and 89 medical students) were asked to provide a diagnosis and antibiotic therapy in a simple fictive paper case of upper urinary tract infection (UTI) in a randomized single-blinded study. Participants were randomized to one of four information tools they were allowed to use in the study or control: 1. free internet access, 2. pharmaceutical pocket guide, 3. pocket guide antibiotic therapy, 4. clinical decision support system (CDSS), and control (no information tool). The CDSS was designed for the study. The adherence to the national German UTI guideline was evaluated. RESULTS:Only 27.1% (n?=?45/166) provided a correct diagnosis of upper UTI and 19.4% (n?=?32/166) an antibiotic treatment recommended by national German treatment guidelines indicating their need for information tools. This result was not significantly different between medical doctors and medical students, residents and medical specialists or level of working experience. Using CDSS improved results significantly compared to conventional tools (diagnosis 57.1%; treatment recommendation 40.5%; p?

SUBMITTER: Neugebauer M

PROVIDER: S-EPMC7059328 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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A clinical decision support system improves antibiotic therapy for upper urinary tract infection in a randomized single-blinded study.

Neugebauer M M Ebert M M Vogelmann R R

BMC health services research 20200306 1

<h4>Background</h4>Due to increasing bacterial resistance rates choosing a correct empiric antibiotic therapy is getting more and more complex. Often medical doctors use information tools to make the right treatment choice.<h4>Methods</h4>One hundred sixty six participants (77 medical doctors and 89 medical students) were asked to provide a diagnosis and antibiotic therapy in a simple fictive paper case of upper urinary tract infection (UTI) in a randomized single-blinded study. Participants wer ...[more]

PMID: 32143630

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Project description:BackgroundThere is increasing attention on machine learning (ML)-based clinical decision support systems (CDSS), but their added value and pitfalls are very rarely evaluated in clinical practice. We implemented a CDSS to aid general practitioners (GPs) in treating patients with urinary tract infections (UTIs), which are a significant health burden worldwide.ObjectiveThis study aims to prospectively assess the impact of this CDSS on treatment success and change in antibiotic prescription behavior of the physician. In doing so, we hope to identify drivers and obstacles that positively impact the quality of health care practice with ML.MethodsThe CDSS was developed by Pacmed, Nivel, and Leiden University Medical Center (LUMC). The CDSS presents the expected outcomes of treatments, using interpretable decision trees as ML classifiers. Treatment success was defined as a subsequent period of 28 days during which no new antibiotic treatment for UTI was needed. In this prospective observational study, 36 primary care practices used the software for 4 months. Furthermore, 29 control practices were identified using propensity score-matching. All analyses were performed using electronic health records from the Nivel Primary Care Database. Patients for whom the software was used were identified in the Nivel database by sequential matching using CDSS use data. We compared the proportion of successful treatments before and during the study within the treatment arm. The same analysis was performed for the control practices and the patient subgroup the software was definitely used for. All analyses, including that of physicians' prescription behavior, were statistically tested using 2-sided z tests with an α level of .05.ResultsIn the treatment practices, 4998 observations were included before and 3422 observations (of 2423 unique patients) were included during the implementation period. In the control practices, 5044 observations were included before and 3360 observations were included during the implementation period. The proportion of successful treatments increased significantly from 75% to 80% in treatment practices (z=5.47, P<.001). No significant difference was detected in control practices (76% before and 76% during the pilot, z=0.02; P=.98). Of the 2423 patients, we identified 734 (30.29%) in the CDSS use database in the Nivel database. For these patients, the proportion of successful treatments during the study was 83%-a statistically significant difference, with 75% of successful treatments before the study in the treatment practices (z=4.95; P<.001).ConclusionsThe introduction of the CDSS as an intervention in the 36 treatment practices was associated with a statistically significant improvement in treatment success. We excluded temporal effects and validated the results with the subgroup analysis in patients for whom we were certain that the software was used. This study shows important strengths and points of attention for the development and implementation of an ML-based CDSS in clinical practice.Trial registrationClinicalTrials.gov NCT04408976; https://clinicaltrials.gov/ct2/show/NCT04408976.

Project description:BackgroundTo develop an original and standardized ureteral stricture disease (USD) score and classification system for quantifying ureter stricture characteristics, assessing complexity of the minimally invasive upper urinary tract reconstructive (UUTR) surgical procedure, formulating preoperative plans, and offering objective comparisons of surgical techniques between different institutions and surgeons.MethodsWe retrospectively reviewed a test set of 64 patients and a validation set of 170 patients who underwent minimally invasive UUTR surgery from January 2018 to January 2021. Three factors were selected to be included in the USD score and classification system: (I) stricture etiology (E, 1-2 points); (II) stricture segment (S, 0-3 points); and (III) length of stricture (L, 1-5 points). The UUTR surgery involves low-complex surgeries (cystoscopy with ureteral dilation and stent placement, ureteropyeloplasty, end to end repair, ureteral reimplantation) and high-complex surgeries (onlay repair (buccal mucosae, lingual mucosae, appendix mucosae), Boari flap repair and ileal ureter replacement). Estimated blood loss and operative time were used as surrogate indicators of surgical complexity.ResultsThe interrater reliability of the USD score and classification system was 0.908. A linear relationship between the USD score and estimated blood loss was observed (rs =0.676, P<0.001). The USD score was also correlated with operative time (rs =0.638, P<0.001). A significant difference in USD scores was found between the high and low complexity surgery groups (4 vs. 7, P<0.001). Variability of UUTR surgery is based on USD classification system, but with regularity to conform to.ConclusionsThe USD score and classification system is a concise, easily applicable, and validated scale to delineate the clinically significant features of ureter stricture that correlate with the complexity of the UUTR surgical procedure. The use of this score and classification system can facilitate preoperative plan and comparison of USD treatments in clinical practice and urological literature. Research with large sample is needed to further examine and modify the use of the system.

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A clinical decision support system improves antibiotic therapy for upper urinary tract infection in a randomized single-blinded study.

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A clinical decision support system improves antibiotic therapy for upper urinary tract infection in a randomized single-blinded study.

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