Point of Care Ultrasound Bladder Volume Calculation on the Acute Medical Unit.

Nursing staff, healthcare assistants (HCAs) and other healthcare professionals on the Acute Medical Unit (AMU) at Royal Berkshire Hospital (RBH) were taught a Point of Care Ultrasound (POCUS) skill during a twenty minute session. Practitioners learned how to take bladder volume measurements with the Butterfly iQ, a portable ultrasound device which provides a visually-aided method of volume measurement. A Likert scale was used to measure the confidence that staff had in performing volume measurements with the AMU automated scanners, and with the semi-automated Butterfly iQ. After the teaching session, confidence reported by practitioners in using the semi-automated visual method was significantly higher than confidence reported in using the automated non-visual scanners (t < 0.001). Minimal time and expense was required to teach practitioners how to perform this skill. Training nurses in POCUS for bladder visualisation and bladder volume calculation is easy and practicable.

Development of an enhanced scoring system to predict ICU readmission or in-hospital death within 24 hours using routine patient data from two NHS Foundation Trusts.

RATIONALE: Intensive care units (ICUs) admit the most severely ill patients. Once these patients are discharged from the ICU to a step-down ward, they continue to have their vital signs monitored by nursing staff, with Early Warning Score (EWS) systems being used to identify those at risk of deterioration. OBJECTIVES: We report the development and validation of an enhanced continuous scoring system for predicting adverse events, which combines vital signs measured routinely on acute care wards (as used by most EWS systems) with a risk score of a future adverse event calculated on discharge from the ICU. DESIGN: A modified Delphi process identified candidate variables commonly available in electronic records as the basis for a 'static' score of the patient's condition immediately after discharge from the ICU. L1-regularised logistic regression was used to estimate the in-hospital risk of future adverse event. We then constructed a model of physiological normality using vital sign data from the day of hospital discharge. This is combined with the static score and used continuously to quantify and update the patient's risk of deterioration throughout their hospital stay. SETTING: Data from two National Health Service Foundation Trusts (UK) were used to develop and (externally) validate the model. PARTICIPANTS: A total of 12 394 vital sign measurements were acquired from 273 patients after ICU discharge for the development set, and 4831 from 136 patients in the validation cohort. RESULTS: Outcome validation of our model yielded an area under the receiver operating characteristic curve of 0.724 for predicting ICU readmission or in-hospital death within 24 hours. It showed an improved performance with respect to other competitive risk scoring systems, including the National EWS (0.653). CONCLUSIONS: We showed that a scoring system incorporating data from a patient's stay in the ICU has better performance than commonly used EWS systems based on vital signs alone. TRIAL REGISTRATION NUMBER: ISRCTN32008295.