Risk Adjustment In Neurocritical care (RAIN)--prospective validation of risk prediction models for adult patients with acute traumatic brain injury to use to evaluate the optimum location and comparative costs of neurocritical care: a cohort study.

OBJECTIVES: To validate risk prediction models for acute traumatic brain injury (TBI) and to use the best model to evaluate the optimum location and comparative costs of neurocritical care in the NHS. DESIGN: Cohort study. SETTING: Sixty-seven adult critical care units. PARTICIPANTS: Adult patients admitted to critical care following actual/suspected TBI with a Glasgow Coma Scale (GCS) score of < 15. INTERVENTIONS: Critical care delivered in a dedicated neurocritical care unit, a combined neuro/general critical care unit within a neuroscience centre or a general critical care unit outside a neuroscience centre. MAIN OUTCOME MEASURES: Mortality, Glasgow Outcome Scale - Extended (GOSE) questionnaire and European Quality of Life-5 Dimensions, 3-level version (EQ-5D-3L) questionnaire at 6 months following TBI. RESULTS: The final Risk Adjustment In Neurocritical care (RAIN) study data set contained 3626 admissions. After exclusions, 3210 patients with acute TBI were included. Overall follow-up rate at 6 months was 81%. Of 3210 patients, 101 (3.1%) had no GCS score recorded and 134 (4.2%) had a last pre-sedation GCS score of 15, resulting in 2975 patients for analysis. The most common causes of TBI were road traffic accidents (RTAs) (33%), falls (47%) and assault (12%). Patients were predominantly young (mean age 45 years overall) and male (76% overall). Six-month mortality was 22% for RTAs, 32% for falls and 17% for assault. Of survivors at 6 months with a known GOSE category, 44% had severe disability, 30% moderate disability and 26% made a good recovery. Overall, 61% of patients with known outcome had an unfavourable outcome (death or severe disability) at 6 months. Between 35% and 70% of survivors reported problems across the five domains of the EQ-5D-3L. Of the 10 risk models selected for validation, the best discrimination overall was from the International Mission for Prognosis and Analysis of Clinical Trials in TBI Lab model (IMPACT) (c-index 0.779 for mortality, 0.713 for unfavourable outcome). The model was well calibrated for 6-month mortality but substantially underpredicted the risk of unfavourable outcome at 6 months. Baseline patient characteristics were similar between dedicated neurocritical care units and combined neuro/general critical care units. In lifetime cost-effectiveness analysis, dedicated neurocritical care units had higher mean lifetime quality-adjusted life-years (QALYs) at small additional mean costs with an incremental cost-effectiveness ratio (ICER) of £14,000 per QALY and incremental net monetary benefit (INB) of £17,000. The cost-effectiveness acceptability curve suggested that the probability that dedicated compared with combined neurocritical care units are cost-effective is around 60%. There were substantial differences in case mix between the 'early' (within 18 hours of presentation) and 'no or late' (after 24 hours) transfer groups. After adjustment, the 'early' transfer group reported higher lifetime QALYs at an additional cost with an ICER of £11,000 and INB of £17,000. CONCLUSIONS: The risk models demonstrated sufficient statistical performance to support their use in research but fell below the level required to guide individual patient decision-making. The results suggest that management in a dedicated neurocritical care unit may be cost-effective compared with a combined neuro/general critical care unit (although there is considerable statistical uncertainty) and support current recommendations that all patients with severe TBI would benefit from transfer to a neurosciences centre, regardless of the need for surgery. We recommend further research to improve risk prediction models; consider alternative approaches for handling unobserved confounding; better understand long-term outcomes and alternative pathways of care; and explore equity of access to postcritical care support for patients following acute TBI. FUNDING: The National Institute for Health Research Health Technology Assessment programme

Surgical trial in traumatic intracerebral haemorrhage (STITCH) : A randomised controlled trial of early surgery compared with Initial conservative treatment

Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme.Peer reviewedPublisher PD

C-Trend parameters and possibilities of federated learning

Abstract. In this observational study, federated learning, a cutting-edge approach to machine learning, was applied to one of the parameters provided by C-Trend Technology developed by Cerenion Oy. The aim was to compare the performance of federated learning to that of conventional machine learning. Additionally, the potential of federated learning for resolving the privacy concerns that prevent machine learning from realizing its full potential in the medical field was explored. Federated learning was applied to burst-suppression ratio’s machine learning and it was compared to the conventional machine learning of burst-suppression ratio calculated on the same dataset. A suitable aggregation method was developed and used in the updating of the global model. The performance metrics were compared and a descriptive analysis including box plots and histograms was conducted. As anticipated, towards the end of the training, federated learning’s performance was able to approach that of conventional machine learning. The strategy can be regarded to be valid because the performance metric values remained below the set test criterion levels. With this strategy, we will potentially be able to make use of data that would normally be kept confidential and, as we gain access to more data, eventually develop machine learning models that perform better. Federated learning has some great advantages and utilizing it in the context of qEEGs’ machine learning could potentially lead to models, which reach better performance by receiving data from multiple institutions without the difficulties of privacy restrictions. Some possible future directions include an implementation on heterogeneous data and on larger data volume.C-Trend-teknologian parametrit ja federoidun oppimisen mahdollisuudet. Tiivistelmä. Tässä havainnointitutkimuksessa federoitua oppimista, koneoppimisen huippuluokan lähestymistapaa, sovellettiin yhteen Cerenion Oy:n kehittämään C-Trend-teknologian tarjoamaan parametriin. Tavoitteena oli verrata federoidun oppimisen suorituskykyä perinteisen koneoppimisen suorituskykyyn. Lisäksi tutkittiin federoidun oppimisen mahdollisuuksia ratkaista yksityisyyden suojaan liittyviä rajoitteita, jotka estävät koneoppimista hyödyntämästä täyttä potentiaaliaan lääketieteen alalla. Federoitua oppimista sovellettiin purskevaimentumasuhteen koneoppimiseen ja sitä verrattiin purskevaimentumasuhteen laskemiseen, johon käytettiin perinteistä koneoppimista. Kummankin laskentaan käytettiin samaa dataa. Sopiva aggregointimenetelmä kehitettiin, jota käytettiin globaalin mallin päivittämisessä. Suorituskykymittareiden tuloksia verrattiin keskenään ja tehtiin kuvaileva analyysi, johon sisältyi laatikkokuvioita ja histogrammeja. Odotetusti opetuksen loppupuolella federoidun oppimisen suorituskyky pystyi lähestymään perinteisen koneoppimisen suorituskykyä. Menetelmää voidaan pitää pätevänä, koska suorituskykymittarin arvot pysyivät alle asetettujen testikriteerien tasojen. Tämän menetelmän avulla voimme ehkä hyödyntää dataa, joka normaalisti pidettäisiin salassa, ja kun saamme lisää dataa käyttöömme, voimme lopulta kehittää koneoppimismalleja, jotka saavuttavat paremman suorituskyvyn. Federoidulla oppimisella on joitakin suuria etuja, ja sen hyödyntäminen qEEG:n koneoppimisen yhteydessä voisi mahdollisesti johtaa malleihin, jotka saavuttavat paremman suorituskyvyn saamalla tietoja useista eri lähteistä ilman yksityisyyden suojaan liittyviä rajoituksia. Joitakin mahdollisia tulevia suuntauksia ovat muun muassa heterogeenisen datan ja suurempien tietomäärien käyttö

Impact of a Multifaceted Early Mobility Intervention for Critically Ill Children - the PICU Up! Trial: Study Protocol for a Multicenter Stepped-Wedge Cluster Randomized Controlled Trial

BACKGROUND: Over 50% of all critically ill children develop preventable intensive care unit-acquired morbidity. Early and progressive mobility is associated with improved outcomes in critically ill adults including shortened duration of mechanical ventilation and improved muscle strength. However, the clinical effectiveness of early and progressive mobility in the pediatric intensive care unit has never been rigorously studied. The objective of the study is to evaluate if the PICU Up! intervention, delivered in real-world conditions, decreases mechanical ventilation duration (primary outcome) and improves delirium and functional status compared to usual care in critically ill children. Additionally, the study aims to identify factors associated with reliable PICU Up! delivery. METHODS: The PICU Up! trial is a stepped-wedge, cluster-randomized trial of a pragmatic, interprofessional, and multifaceted early mobility intervention (PICU Up!) conducted in 10 pediatric intensive care units (PICUs). The trial\u27s primary outcome is days alive free of mechanical ventilation (through day 21). Secondary outcomes include days alive and delirium- and coma-free (ADCF), days alive and coma-free (ACF), days alive, as well as functional status at the earlier of PICU discharge or day 21. Over a 2-year period, data will be collected on 1,440 PICU patients. The study includes an embedded process evaluation to identify factors associated with reliable PICU Up! delivery. DISCUSSION: This study will examine whether a multifaceted strategy to optimize early mobility affects the duration of mechanical ventilation, delirium incidence, and functional outcomes in critically ill children. This study will provide new and important evidence on ways to optimize short and long-term outcomes for pediatric patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT04989790. Registered on August 4, 2021

Economic evaluation in intensive care: The case of SDD

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The aim of this thesis was to examine the use of modelling techniques in the economic evaluation of selective decontamination of the digestive tract (SDD), used to prevent intensive care unit (ICU) acquired pneumonia. The need for evidence for the effectiveness and cost effectiveness of technologies used in intensive care was highlighted through an examination of the literature. The clinical and economic issues pertinent to ICU-acquired pneumonia and SDD were described. It was suggested that an economic evaluation of SDD was required. An evaluation using modelling techniques was proposed. A secondary economic evaluation of SDD was carried out, utilising a decision-analytic model and published clinical and economic evidence to derive cost/outcome ratios. This analysis showed that SDD could be a dominant therapy, but improved economic and long term outcome evidence was required to increase the robustness of conclusions. This thesis concentrated on improving the economic evidence. A national survey of SDD use provided information on clinical practice. A prospective observational study was carried out at two British ICUs to obtain evidence on the economic impact of ICU-acquired pneumonia. The impact of infection and confounding factors on resource use was handled quantitatively, using regression techniques. It was found that ICU-acquired pneumonia significantly increased length of ICU stay. These two sets of empirical data were used in a revised economic evaluation of SDD. SDD was found to be a dominant therapy at both centres. Uncertainty around cost/outcome ratios was considered to be decreased, or at least quantified, by this primary economic evidence. This thesis concludes that modelling has a place in economic evaluation in intensive care, if rigorous methods are used. It has also demonstrated that current, reliable and applicable economic evidence is a prerequisite to any economic evaluation, if it is to be included in the decision-making process.Financial support was obtained from the Office of the Chief Pharmacist, Department of Health

Outcome prediction in intensive care with special reference to cardiac surgery

The development, use, and understanding of severity of illness scoring systems has advanced rapidly in the last decade; their weaknesses and limitations have also become apparent. This work follows some of this development and explores some of these aspects. It was undertaken in three stages and in two countries. The first study investigated three severity of illness scoring systems in a general Intensive Care Unit (ICU) in Cape Town, namely the Acute Physiology and Chronic Health Evaluation (APACHE II) score, the Therapeutic Intervention Scoring System (TISS), and a locally developed organ failure score. All of these showed a good relationship with mortality, with the organ failure score the best predictor of outcome. The TISS score was felt to be more likely to be representative of intensiveness of medical and nursing management than severity of illness. The APACHE II score was already becoming widely used world-wide and although it performed less well in some diagnostic categories (for example Adult Respiratory Distress Syndrome) than had been hoped, it clearly warranted further investigation. Some of the diagnosis-specific problems were eliminated in the next study which concentrated on the application of the APACHE II score in a cardiothoracic surgical ICU in London. Although group predictive ability was statistically impressive, the predictive ability of APACHE II in the individual patient was limited as only very high APACHE II scores confidently predicted death and then only in a small number of patients. However, there were no deaths associated with an APACHE II score of less than 5 and the mortality was less than 1 % when the APACHE II score was less than 10. Finally, having recognised the inadequacies in mortality prediction of the APACHE II score in this scenario, a study was undertaken to evaluate a novel concept: a combination of preoperative, intraoperative, and postoperative (including APACHE II and III) variables in cardiac surgery patients admitted to the same ICU. The aim was to develop a more precise method of predicting length of stay, incidence of complications, and ICU and hospital outcome for these patients. There were 1008 patients entered into the study. There was a statistically significant relationship between increasing Parsonnet (a cardiac surgery risk prediction score), APACHE II, and APACHE III scores and mortality. By forward stepwise logistic regression a model was developed for the probability of hospital death. This model included bypass time, need for inotropes, mean arterial pressure, urea, and Glasgow Coma Scale. Predictive performance was evaluated by calculating the area under the receiver operating characteristic (ROC) curve. The derived model had an area under the ROC curve 0.87, while the Parsonnet score had an area of 0.82 and the APACHE II risk of dying 0.84. It was concluded that a combination of intraoperative and postoperative variables can improve predictive ability