Implementation of an external female urinary catheter strategy on prevention of skin breakdown in acute care: A quality improvement study.

AIM(S): To evaluate the incidence of skin-related complications attributable to incontinence-associated dermatitis (IAD) using an external female urinary catheter device strategy for urinary incontinent (UI) patients in acute care. DESIGN: Multicenter quality improvement study. METHODS: Randomized allocation of two commercially available external female urinary catheter devices was used in hospitalized UI female patients. Daily nursing skin assessments were documented in the electronic health record before, during and after external catheter device application. Methods and results were reported following SQUIRE guidelines. RESULTS: Three hundred and eighty-one patients from 57 inpatient care units were included in the analysis. Both catheter devices were associated with an overall low risk (5 %) of new or worsening skin breakdown. CONCLUSION: The overall benefit of external catheters is most persuasive for skin integrity, rather than infection prevention. IMPACT: Significant negative outcomes are associated with UI patients. External female urinary catheters are a non-invasive alternative strategy to reduce exposure of regional skin to urine contamination and IAD-related skin complications. Use of external female urinary catheters in hospitalized UI female patients offers low risk (5%) of new or worsening overall skin breakdown. PATIENT CONTRIBUTION: Hospitalized UI female patients were screened for external catheter device eligibility by the bedside nurse. The quality improvement review committee waved consent because the intervention was considered standard care.

Development, Evaluation, and Multisite Deployment of a Machine Learning Decision Tree Algorithm To Optimize Urinalysis Parameters for Predicting Urine Culture Positivity.

PittUDT, a recursive partitioning decision tree algorithm for predicting urine culture (UC) positivity based on macroscopic and microscopic urinalysis (UA) parameters, was developed in support of a broader system-wide diagnostic stewardship initiative to increase appropriateness of UC testing. Reflex algorithm training utilized results from 19,511 paired UA and UC cases (26.8% UC positive); the average patient age was 57.4 years, and 70% of samples were from female patients. Receiver operating characteristic (ROC) analysis identified urine white blood cells (WBCs), leukocyte esterase, and bacteria as the best predictors of UC positivity, with areas under the ROC curve of 0.79, 0.78, and 0.77, respectively. Using the held-out test data set (9,773 cases; 26.3% UC positive), the PittUDT algorithm met the prespecified target of a negative predictive value above 90% and resulted in a 30 to 60% total negative proportion (true-negative plus false-negative predictions). These data show that a supervised rule-based machine learning algorithm trained on paired UA and UC data has adequate predictive ability for triaging urine specimens by identifying low-risk urine specimens, which are unlikely to grow pathogenic organisms, with a false-negative proportion under 5%. The decision tree approach also generates human-readable rules that can be easily implemented across multiple hospital sites and settings. Our work demonstrates how a data-driven approach can be used to optimize UA parameters for predicting UC positivity in a reflex protocol, with the intent of improving antimicrobial stewardship and UC utilization, a potential avenue for cost savings.