Protocolised non-invasive compared with invasive weaning from mechanical ventilation for adults in intensive care : the Breathe RCT

Background: Invasive mechanical ventilation (IMV) is a life-saving intervention. Following resolution of the condition that necessitated IMV, a spontaneous breathing trial (SBT) is used to determine patient readiness for IMV discontinuation. In patients who fail one or more SBTs, there is uncertainty as to the optimum management strategy. Objective: To evaluate the clinical effectiveness and cost-effectiveness of using non-invasive ventilation (NIV) as an intermediate step in the protocolised weaning of patients from IMV. Design: Pragmatic, open-label, parallel-group randomised controlled trial, with cost-effectiveness analysis. Setting: A total of 51 critical care units across the UK. Participants: Adult intensive care patients who had received IMV for at least 48 hours, who were categorised as ready to wean from ventilation, and who failed a SBT. Interventions: Control group (invasive weaning): patients continued to receive IMV with daily SBTs. A weaning protocol was used to wean pressure support based on the patient’s condition. Intervention group (non-invasive weaning): patients were extubated to NIV. A weaning protocol was used to wean inspiratory positive airway pressure, based on the patient’s condition. Main outcome measures: The primary outcome measure was time to liberation from ventilation. Secondary outcome measures included mortality, duration of IMV, proportion of patients receiving antibiotics for a presumed respiratory infection and health-related quality of life. Results: A total of 364 patients (invasive weaning, n = 182; non-invasive weaning, n = 182) were randomised. Groups were well matched at baseline. There was no difference between the invasive weaning and non-invasive weaning groups in median time to liberation from ventilation {invasive weaning 108 hours [interquartile range (IQR) 57–351 hours] vs. non-invasive weaning 104.3 hours [IQR 34.5–297 hours]; hazard ratio 1.1, 95% confidence interval [CI] 0.89 to 1.39; p = 0.352}. There was also no difference in mortality between groups at any time point. Patients in the non-invasive weaning group had fewer IMV days [invasive weaning 4 days (IQR 2–11 days) vs. non-invasive weaning 1 day (IQR 0–7 days); adjusted mean difference –3.1 days, 95% CI –5.75 to –0.51 days]. In addition, fewer non-invasive weaning patients required antibiotics for a respiratory infection [odds ratio (OR) 0.60, 95% CI 0.41 to 1.00; p = 0.048]. A higher proportion of non-invasive weaning patients required reintubation than those in the invasive weaning group (OR 2.00, 95% CI 1.27 to 3.24). The within-trial economic evaluation showed that NIV was associated with a lower net cost and a higher net effect, and was dominant in health economic terms. The probability that NIV was cost-effective was estimated at 0.58 at a cost-effectiveness threshold of £20,000 per quality-adjusted life-year. Conclusions: A protocolised non-invasive weaning strategy did not reduce time to liberation from ventilation. However, patients who underwent non-invasive weaning had fewer days requiring IMV and required fewer antibiotics for respiratory infections. Future work: In patients who fail a SBT, which factors predict an adverse outcome (reintubation, tracheostomy, death) if extubated and weaned using NIV? Trial registration: Current Controlled Trials ISRCTN15635197. Funding: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 23, No. 48. See the NIHR Journals Library website for further project information

Analysis of the cardiorespiratory pattern of patients undergoing weaning using artificial intelligence

The optimal extubating moment is still a challenge in clinical practice. Respiratory pattern variability analysis in patients assisted through mechanical ventilation to identify this optimal moment could contribute to this process. This work proposes the analysis of this variability using several time series obtained from the respiratory flow and electrocardiogram signals, applying techniques based on artificial intelligence. 154 patients undergoing the extubating process were classified in three groups: successful group, patients who failed during weaning process, and patients who after extubating failed before 48 hours and need to reintubated. Power Spectral Density and time-frequency domain analysis were applied, computing Discrete Wavelet Transform. A new Q index was proposed to determine the most relevant parameters and the best decomposition level to discriminate between groups. Forward selection and bidirectional techniques were implemented to reduce dimensionality. Linear Discriminant Analysis and Neural Networks methods were implemented to classify these patients. The best results in terms of accuracy were, 84.61 ± 3.1% for successful versus failure groups, 86.90 ± 1.0% for successful versus reintubated groups, and 91.62 ± 4.9% comparing the failure and reintubated groups. Parameters related to Q index and Neural Networks classification presented the best performance in the classification of these patients.Peer ReviewedPostprint (published version

Guideline-based decision support in medicine : modeling guidelines for the development and application of clinical decision support systems

Guideline-based Decision Support in Medicine Modeling Guidelines for the Development and Application of Clinical Decision Support Systems The number and use of decision support systems that incorporate guidelines with the goal of improving care is rapidly increasing. Although developing systems that are both effective in supporting clinicians and accepted by them has proven to be a difficult task, of the systems that were evaluated by a controlled trial, the majority showed impact. The work, described in this thesis, aims at developing a methodology and framework that facilitates all stages in the guideline development process, ranging from the definition of models that represent guidelines to the implementation of run-time systems that provide decision support, based on the guidelines that were developed during the previous stages. The framework consists of 1) a guideline representation formalism that uses the concepts of primitives, Problem-Solving Methods (PSMs) and ontologies to represent guidelines of various complexity and granularity and different application domains, 2) a guideline authoring environment that enables guideline authors to define guidelines, based on the newly developed guideline representation formalism, and 3) a guideline execution environment that translates defined guidelines into a more efficient symbol-level representation, which can be read in and processed by an execution-time engine. The described methodology and framework were used to develop and validate a number of guidelines and decision support systems in various clinical domains such as Intensive Care, Family Practice, Psychiatry and the areas of Diabetes and Hypertension control

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

The factors impacting Critical Care Nurses' decision-making processes in continuous renal replacement therapy.

IntroductionContinuous Renal Replacement Therapy (CRRT) is a common treatment intervention in critical care units worldwide. It provides supportive therapy for critically ill patients with severe kidney dysfunction. Efforts to optimise its clinical effectiveness through modifying treatment regimens over recent years have proved largely unsuccessful. However, studies have not explored the human element of critical care nurses delivering CRRT.AimThis study is designed to understand the influences on critical care nurses’ decision-making in the management of CRRT. In doing so, develop and highlight areas where modifications in practices can be adopted, in order to improve both the patient and organisational quality indicators associated with CRRT delivery.MethodsThis study used an adaptive Interpretive Description approach. The study was conducted across four linked United Kingdom critical care units. Twenty- one (n=21) registered critical care nurses undertook the California Critical Thinking Disposition Inventory (CCTDI) and of these, 10 nurses were interviewed face to face in semi-structured audio recorded interview, which were then transcribed and thematically analysed.ResultsFour major themes influencing critical care nurses decision-making regarding CRRT were identified, with a further two contributing an overarching influence. These themes were the Individual; Organisational; Practice; Support, these themes were complex and intertwined and in themselves highlighted issues about Variability and Competing demands whilst delivering CRRT.DiscussionThe themes in this study showed an alignment with some of the findings from other critical care decision-making studies, including the influence of the individual nurse, experience, and support. However, this work has also been able to introduce significant new knowledge on the perceptions and insights of critical care nurses. These findings generate new knowledge and contextualise these understandings of individuals, the organisations, the wider interactions, and relationships between colleagues, and the CRRT technology, and provide insights to enable a holistic approach to understanding the provision of CRRT and potentially enable improvements in treatment delivery. In understanding these aspects, it has elucidated avenues for improvements in practice, identifying areas that can be engineered to improve CRRT practice and characterises elements within individuals which contribute to CRRT delivery.RecommendationsThis study draws up a number of recommendations from the themes identified, these recommendations focus on the ‘Individual’ and the ‘Organisational’ themes. They include the introduction of a harmonised training, educational and competency programmes with integrated in situ hi-fidelity simulation provision, alongside bespoke high quality clinical supervision, to facilitate critical care nurses self-awareness