Electronic nudge tool technology used in the critical care and peri-anaesthetic setting: a scoping review protocol.

INTRODUCTION: Electronic clinical decision support (eCDS) tools are used to assist clinical decision making. Using computer-generated algorithms with evidence-based rule sets, they alert clinicians to events that require attention. eCDS tools generating alerts using nudge principles present clinicians with evidence-based clinical treatment options to guide clinician behaviour without restricting freedom of choice. Although eCDS tools have shown beneficial outcomes, challenges exist with regard to their acceptability most likely related to implementation. Furthermore, the pace of progress in this field has allowed little time to effectively evaluate the experience of the intended user. This scoping review aims to examine the development and implementation strategies, and the impact on the end user of eCDS tools that generate alerts using nudge principles, specifically in the critical care and peri-anaesthetic setting. METHODS AND ANALYSIS: This review will follow the Arksey and O'Malley framework. A search will be conducted of literature published in the last 15 years in MEDLINE, EMBASE, CINAHL, CENTRAL, Web of Science and SAGE databases. Citation screening and data extraction will be performed by two independent reviewers. Extracted data will include context, e-nudge tool type and design features, development, implementation strategies and associated impact on end users. ETHICS AND DISSEMINATION: This scoping review will synthesise published literature therefore ethical approval is not required. Review findings will be published in topic relevant peer-reviewed journals and associated conferences.

An AMPK activator as a therapeutic option for congenital nephrogenic diabetes insipidus.

Nephrogenic diabetes insipidus (NDI) patients produce large amounts of dilute urine. NDI can be congenital, resulting from mutations in the type-2 vasopressin receptor (V2R), or acquired, resulting from medications such as lithium. There are no effective treatment options for NDI. Activation of PKA is disrupted in both congenital and acquired NDI, resulting in decreased aquaporin-2 phosphorylation and water reabsorption. We show that adenosine monophosphate-activated protein kinase (AMPK) also phosphorylates aquaporin-2. We identified an activator of AMPK, NDI-5033, and we tested its ability to increase urine concentration in animal models of NDI. NDI-5033 increased AMPK phosphorylation by 2.5-fold, confirming activation. It increased urine osmolality in tolvaptan-treated NDI rats by 30%-50% and in V2R-KO mice by 50%. Metformin, another AMPK activator, can cause hypoglycemia, which makes it a risky option for treating NDI patients, especially children. Rats with NDI receiving NDI-5033 showed no hypoglycemia in a calorie-restricted, exercise protocol. Congenital NDI therapy needs to be effective long-term. We administered NDI-5033 for 3 weeks and saw no reduction in efficacy. We conclude that NDI-5033 can improve urine concentration in animals with NDI and holds promise as a potential therapy for patients with congenital NDI due to V2R mutations.

Comparing regression and neural network techniques for personalized predictive analytics to promote lung protective ventilation in Intensive Care Units.

Mechanical ventilation is a lifesaving tool and provides organ support for patients with respiratory failure. However, injurious ventilation due to inappropriate delivery of high tidal volume can initiate or potentiate lung injury. This could lead to acute respiratory distress syndrome, longer duration of mechanical ventilation, ventilator associated conditions and finally increased mortality. In this study, we explore the viability and compare machine learning methods to generate personalized predictive alerts indicating violation of the safe tidal volume per ideal body weight (IBW) threshold that is accepted as the upper limit for lung protective ventilation (LPV), prior to application to patients. We process streams of patient respiratory data recorded per minute from ventilators in an intensive care unit and apply several state-of-the-art time series prediction methods to forecast the behavior of the tidal volume metric per patient, 1 hour ahead. Our results show that boosted regression delivers better predictive accuracy than other methods that we investigated and requires relatively short execution times. Long short-term memory neural networks can deliver similar levels of accuracy but only after much longer periods of data acquisition, further extended by several hours computing time to train the algorithm. Utilizing Artificial Intelligence, we have developed a personalized clinical decision support tool that can predict tidal volume behavior within 10% accuracy and compare alerts recorded from a real world system to highlight that our models would have predicted violations 1 hour ahead and can therefore conclude that the algorithms can provide clinical decision support.