Objective: To develop and validate a nomogram for assessing bladder outlet obstruction (BOO) in women derived from concurrent P det.Qmax and Q max based on radiographic evidence of increased urethral resistance. Patients and Methods: Retrospective analysis of prospectively acquired videourodynamics and clinical data of 185 women (development cohort) was performed. The P det.Qmax were plotted against Q max and cluster analysis was performed to determine an axis that best divided the definitively obstructed and unobstructed. Using data from a further 350 women (validation cohort), the sensitivity and specificity of the derived criterion was calculated. Finally, the data from both groups was pooled together and using binary logistic regression analysis, a nomogram was produced. Results: Of the 535 patients in the two cohorts, (122 [22.8%]) demonstrated radiographic evidence of BOO. Cluster analysis identified the axis that best separates the radiographically obstructed and unobstructed as P det.Qmax = 2*Q max . Using the data from the validation cohort, the sensitivity and specificity for this was calculated as 0.94 and 0.93, respectively. A nomogram, representing the probability of BOO for concurrent P det.Qmax and Q max measurements was derived by pooling data from both cohorts. Alternatively, a female BOO index (BOOIf) may be calculated mathematically using the formula BOOIf = P det.Qmax − 2.2*Q max, that is, BOOIf < 0, <10% probability of obstruction, BOOIf > 5 likely obstructed (50%) and If BOOIf > 18, obstruction almost certain (>90%). Conclusion: A female BOO nomogram (the SG nomogram) with high sensitivity and specificity is proposed. The nomogram can be used to stratify the degree of BOO or assess response to treatment.
BackgroundWhether explicit or implicit, models of value are fundamental in quality improvement (QI) initiatives. They embody the desirability of the impact of interventions—with either foresight or hindsight. Increasingly impact is articulated in terms of outcomes, which are often prescribed and sometimes inappropriate. Currently, there is little methodological guidance for deriving an appropriate set of outcomes for a given QI initiative. This paper describes a structured approach for identifying and mapping outcomes.Overall approachCentral to the approach presented here is the engagement of teams in the exploration of the system that is being designed into. This methodology has emerged from the analysis and abstraction of existing methods that define systems in terms of outcomes, stakeholders and their analogues. It is based on a sequence of questions that underpin these methods.Outcome elicitation toolsThe fundamental questions of outcome elicitation can be concatenated into a structured process, within the Outcome Identification Loop. This system-analysis process stimulates new insights that can be captured within a System Impact Model.The System Impact Model reconciles principles of intended cause/effect, with knowledge of unintended effects more typically emphasised by risk approaches. This system representation may be used to select sets of outcomes that signify the greatest impact on patients, staff and other stakeholders. It may also be used to identify potential QI interventions and to forecast their impact.Discussion and conclusionsThe Outcome Identification Loop has proven to be an effective tool for designing workshops and interviews that engage stakeholders, critically in the early stages of QI planning. By applying this process in different ways, existing knowledge is captured in System Impact Models and mobilised towards QI endeavours.
BackgroundDuring the COVID-19 pandemic, portable pulse oximeters were issued to some patients to permit home monitoring and alleviate pressure on inpatient wards. Concerns were raised about the accuracy of these devices in some patient groups. This study was conducted in response to these concerns.ObjectivesTo evaluate the performance characteristics of five portable pulse oximeters and their suitability for deployment on home-use pulse oximetry pathways created during the COVID-19 pandemic. This study considered the effects of different device models and patient characteristics on pulse oximeter accuracy, false negative and false positive rate.MethodsA total of 915 oxygen saturation (spO2) measurements, paired with measurements from a hospital-standard pulse oximeter, were taken from 50 patients recruited from respiratory wards and the intensive care unit at an acute hospital in London. The effects of device model and several patient characteristics on bias, false negative and false positive likelihood were evaluated using multiple regression analyses.Results and conclusionsAll five portable pulse oximeters appeared to outperform the standard to which they were manufactured. Device model, patient spO2 and patient skin colour were significant predictors of measurement bias, false positive and false negative rate, with some variation between models. The false positive and false negative rates were 11.2% and 24.5%, respectively, with substantial variation between models.
Unfamiliarity with medical device regulations can sometimes be a barrier to deploying technology in a clinical setting for researchers and innovators. Health service providers recognise that innovation can happen within smaller organisations, where regulatory support may be limited. This article sets out to increase transparency and outline key considerations on medical device regulations from a UK healthcare provider’s perspective. The framework used by Guy’s and St Thomas’ NHS Foundation Trust (GSTFT) for assessing research devices is presented to give an overview of the routes that R&D medical devices take to enter a clinical setting. Furthermore, current trends on research studies involving medical devices were extracted from the GSTFT internal R&D database and presented as the following categories (i) commercial vs. non-commercial, (ii) assessment type and (iii) software vs. non-software. New medical devices legislation will be introduced within the UK in July 2023. It is anticipated regulating software as a medical device may become more challenging for healthcare providers and device manufacturers alike. It is therefore important for different stakeholders involved to work together to ensure this does not become a barrier to innovation.
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