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Background: The use of machine learning is becoming increasingly popular in many disciplines, but there is still an implementation gap of machine learning models in clinical settings. Lack of trust in models is one of the issues that need to be addressed in an effort to close this gap. No models are perfect, and it is crucial to know in which use cases we can trust a model and for which cases it is less reliable., Methods: Four different algorithms are trained on the eICU Collaborative Research Database using similar features as the APACHE IV severity-of-disease scoring system to predict hospital mortality in the ICU. The training and testing procedure is repeated 100 times on the same dataset to investigate whether predictions for single patients change with small changes in the models. Features are then analysed separately to investigate potential differences between patients consistently classified correctly and incorrectly., Results: A total of 34 056 patients (58.4%) are classified as true negative, 6 527 patients (11.3%) as false positive, 3 984 patients (6.8%) as true positive, and 546 patients (0.9%) as false negatives. The remaining 13 108 patients (22.5%) are inconsistently classified across models and rounds. Histograms and distributions of feature values are compared visually to investigate differences between groups., Conclusions: It is impossible to distinguish the groups using single features alone. Considering a combination of features, the difference between the groups is clearer. Incorrectly classified patients have features more similar to patients with the same prediction rather than the same outcome., (© 2023. The Author(s).)

Introduction: Postoperative delirium is common in older cardiac surgery patients and associated with negative short-term and long-term outcomes. The alpha-2-adrenergic receptor agonist dexmedetomidine shows promise as prophylaxis and treatment for delirium in intensive care units (ICU) and postoperative settings. Clonidine has similar pharmacological properties and can be administered both parenterally and orally. We aim to study whether repurposing of clonidine can represent a novel treatment option for delirium, and the possible effects of dexmedetomidine and clonidine on long-term cognitive trajectories, motor activity patterns and biomarkers of neuronal injury, and whether these effects are associated with frailty status., Methods and Analysis: This five-centre, double-blind randomised controlled trial will include 900 cardiac surgery patients aged 70+ years. Participants will be randomised 1:1:1 to dexmedetomidine or clonidine or placebo. The study drug will be given as a continuous intravenous infusion from the start of cardiopulmonary bypass, at a rate of 0.4 µg/kg/hour. The infusion rate will be decreased to 0.2 µg/kg/hour postoperatively and be continued until discharge from the ICU or 24 hours postoperatively, whichever happens first.Primary end point is the 7-day cumulative incidence of postoperative delirium (Diagnostic and Statistical Manual of Mental Disorders, fifth edition). Secondary end points include the composite end point of coma, delirium or death, in addition to delirium severity and motor activity patterns, levels of circulating biomarkers of neuronal injury, cognitive function and frailty status 1 and 6 months after surgery., Ethics and Dissemination: This trial is approved by the Regional Committee for Ethics in Medical Research in Norway (South-East Norway) and by the Norwegian Medicines Agency. Dissemination plans include publication in peer-reviewed medical journals and presentation at scientific meetings., Trial Registration Number: NCT05029050., Competing Interests: Competing interests: HZ has served at scientific advisory boards and/or as a consultant for Alector, Eisai, Denali, Roche Diagnostics, Wave, Samumed, Siemens Healthineers, Pinteon Therapeutics, Nervgen, AZTherapies, CogRx and Red Abbey Labs, has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure and Biogen and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Programme (outside submitted work). AJCS is advisor for Prolira, a start-up company that develops an EEG-based delirium monitor. Any (future) profits from EEG-based delirium monitoring will be used for future scientific research only. GS has participated in one advisory meeting for Biogen. HMN has received lecture fees from Vifor Pharma and Orion Pharma. The other authors declare that they have no competing interests., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Machine learning (ML) holds the promise of becoming an essential tool for utilising the increasing amount of clinical data available for analysis and clinical decision support. However, the lack of trust in the models has limited the acceptance of this technology in healthcare. This mistrust is often credited to the shortage of model explainability and interpretability, where the relationship between the input and output of the models is unclear. Improving trust requires the development of more transparent ML methods., Methods: In this paper, we use the publicly available eICU database to construct a number of ML models before examining their internal behaviour with SHapley Additive exPlanations (SHAP) values. Our four models predicted hospital mortality in ICU patients using a selection of the same features used to calculate the APACHE IV score and were based on random forest, logistic regression, naive Bayes, and adaptive boosting algorithms., Results: The results showed the models had similar discriminative abilities and mostly agreed on feature importance while calibration and impact of individual features differed considerably and did in multiple cases not correspond to common medical theory., Conclusions: We already know that ML models treat data differently depending on the underlying algorithm. Our comparative analysis visualises implications of these differences and their importance in a healthcare setting. SHAP value analysis is a promising method for incorporating explainability in model development and usage and might yield better and more trustworthy ML models in the future., (© 2022. The Author(s).)

Background: Circulatory failure after out-of-hospital cardiac arrest (OHCA) as part of the postcardiac arrest syndrome (PCAS) is believed to be caused by an initial myocardial depression that later subsides into a superimposed vasodilatation. However, the relative contribution of myocardial dysfunction and systemic inflammation has not been established. Our objective was to describe the macrocirculatory and microcirculatory failure in PCAS in more detail., Methods: We included 42 comatose patients after OHCA where circulatory variables were invasively monitored from admission until day 5. We measured the development in cardiac power output (CPO), stroke work (SW), aortic elastance, microcirculatory metabolism, inflammatory and cardiac biomarkers and need for vasoactive medications. We used survival analysis and Cox regression to assess time to norepinephrine discontinuation and negative fluid balance, stratified by inflammatory and cardiac biomarkers., Results: CPO, SW and oxygen delivery increased during the first 48 hours. Although the estimated afterload fell, the blood pressure was kept above 65 mmHg with a diminishing need for norepinephrine, indicating a gradually re-established macrocirculatory homoeostasis. Time to norepinephrine discontinuation was longer for patients with higher pro-brain natriuretic peptide concentration (HR 0.45, 95% CI 0.21 to 0.96), while inflammatory biomarkers and other cardiac biomarkers did not predict the duration of vasoactive pressure support. Markers of microcirculatory distress, such as lactate and venous-to-arterial carbon dioxide difference, were normalised within 24 hours., Conclusion: The circulatory failure was initially characterised by reduced CPO and SW, however, microcirculatory and macrocirculatory homoeostasis was restored within 48 hours. We found that biomarkers indicating acute heart failure, and not inflammation, predicted longer circulatory support with norepinephrine. Taken together, this indicates an early and resolving, rather than a late and emerging vasodilatation., Trial Registration: NCT02648061., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Whole body ischemia and reperfusion injury after cardiac arrest leads to the massive inflammation clinically manifested in the post-cardiac arrest syndrome. Previous studies on the inflammatory effect on circulatory failure after cardiac arrest have either investigated a selected patient group or a limited part of the inflammatory mechanisms. We examined the association between cardiac arrest characteristics and inflammatory biomarkers, and between inflammatory biomarkers and circulatory failure after cardiac arrest, in an unselected patient cohort., Methods: This was a prospective study of 50 consecutive patients with out-of-hospital cardiac arrest. Circulation was invasively monitored from admission until day five, whereas inflammatory biomarkers, i.e. complement activation, cytokines and endothelial injury, were measured daily. We identified predictors for an increased inflammatory response, and associations between the inflammatory response and circulatory failure., Results: We found a marked and broad inflammatory response in patients after cardiac arrest, which was associated with clinical outcome. Long time to return of spontaneous circulation and high lactate level at admission were associated with increased complement activation (TCC and C3bc), pro-inflammatory cytokines (IL-6, IL-8) and endothelial injury (syndecan-1) at admission. These biomarkers were in turn significantly associated with lower mean arterial blood pressure, lower cardiac output and lower systemic vascular resistance, and increased need of circulatory support in the initial phase. High levels of TCC and IL-6 at admission were significantly associated with increased 30-days mortality., Conclusion: Inflammatory biomarkers, including complement activation, cytokines and endothelial injury, were associated with increased circulatory failure in the initial period after cardiac arrest., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)