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Objectives
Urine microscopy is a common test performed in emergency departments (EDs). Urine specimens can easily become contaminated by different factors, including the collection method. The midstream clean-catch (MSCC) collection technique is commonly used to reduce urine contamination. The urine culture contamination rate from specimens collected in our ED is 30%. We developed an instructional application (app) to show ED patients how to provide a MSCC urine sample. We hypothesized that ED patients who viewed our instructional app would have significantly lower urine contamination rates compared to patients who did not.Methods
We prospectively enrolled 257 subjects with a urinalysis and/or urine culture test ordered in the ED and asked them to watch our MSCC instructional app. After prospective enrollment was complete, we retrospectively matched each enrolled subject to an ED patient who did not watch the instructional app. Controls were matched to cases based on gender, type of urine specimen provided, ED visit date and shift. Urinalysis and urine culture contamination results were compared between the matched pairs using McNemar's test.Results
The overall urine culture contamination rate of the 514 subjects was 38%. The majority of the matched pairs had a urinalysis (63%) or urinalysis plus urine culture (35%) test done. There were no significant differences in our urine contamination rates between the matched pairs overall or when stratified by gender, by prior knowledge of the clean catch process or by type of urine specimen.Conclusion
We did not see a lower contamination rate for patients who viewed our instructional app compared to patients who did not. It is possible that MSCC is not effective for decreasing urine specimen contamination. 相似文献Context
Attrition is common in longitudinal observational studies in palliative care. Few studies have examined predictors of attrition.Objectives
To identify patient characteristics at enrollment associated with attrition in palliative oncology outpatient setting.Methods
In this longitudinal observational study, advanced cancer patients enrolled in an outpatient multicenter study were assessed at baseline and two to five weeks later. We compared baseline characteristics between patients who returned for follow-up and those who dropped out.Results
Seven hundred forty-four patients were enrolled from Jordan, Brazil, Chile, Korea, and India. Attrition rate was 33%, with variation among countries (22%–39%; P = 0.023). In univariate analysis, baseline predictors for attrition were cognitive failure (odds ratio [OR] 1.23 per point in Memorial Delirium Assessment Scale; P < 0.01), functional status (OR 1.55 per 10-point decrease in Karnofsky Performance Status; P < 0.01), Edmonton Symptom Assessment Scale [ESAS] physical score (OR 1.03 per point; P < 0.01), ESAS emotional score (OR 1.05 per point; P < 0.01), and shorter duration between cancer diagnosis and palliative care referral in months (OR 0.89 per log; P = 0.028). In multivariate analysis, cognitive failure (OR 1.12 per point; P = 0.007), ESAS physical score (OR 1.18 per point; P = 0.027), functional status (OR 1.35 per 10-point decrease; P < 0.001), and shorter duration from cancer diagnosis (OR 0.86 per log; P = 0.01) remained independent predictors of attrition.Conclusion
Advanced cancer patients with cognitive failure, increased physical symptoms, poorer performance status, and shorter duration from cancer diagnosis were more likely to dropout. These results have implications for research design, patient selection, and data interpretation in longitudinal observational studies. 相似文献Method: We first describe classification systems and the benefits of using them in both research and practice. We then present a theoretical framework from Implementation Science to report strategies we have used to implement two research-based classification tools into practice. We also illustrate some of the challenges we have encountered by reporting results from an online survey investigating 58 Speech-language Pathologists’ knowledge and use of the Communication Function Classification System (CFCS), a new tool to classify children’s functional communication skills.
Result and conclusions: We offer recommendations for researchers wanting to promote the uptake of new tools in clinical practice. Specifically, we identify structural, organizational, innovation, practitioner, and patient-related factors that we recommend researchers address in the design of implementation interventions. Roles and responsibilities of both researchers and clinicians in making implementations science a success are presented.
- Implications for rehabilitation
Promoting uptake of new and evidence-based tools into clinical practice is challenging.
Implementation science can help researchers to close the knowledge-to-practice gap.
Using concrete examples, we discuss our experiences in implementing evidence-based classification tools into practice within a theoretical framework.
Recommendations are provided for researchers wanting to implement new tools in clinical practice. Implications for researchers and clinicians are presented.