Aim-We examined the use of a Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA) catheter during cardiopulmonary resuscitation (CPR) after cardiac arrest (CA) to assess its effect on haemodynamics such as coronary perfusion pressure (CPP), common carotid artery blood flow (CCA-flow) and end-tidal CO 2 (PetCO 2) which are associated with increased return of spontaneous circulation (ROSC).
doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.
Background: COVID-19 has led to an unprecedented strain on health care facilities across the United States. Accurately identifying patients at an increased risk of deterioration may help hospitals manage their resources while improving the quality of patient care. Here, we present the results of an analytical model, Predicting Intensive Care Transfers and Other Unforeseen Events (PICTURE), to identify patients at high risk for imminent intensive care unit transfer, respiratory failure, or death, with the intention to improve the prediction of deterioration due to COVID-19.Objective: This study aims to validate the PICTURE model's ability to predict unexpected deterioration in general ward and COVID-19 patients, and to compare its performance with the Epic Deterioration Index (EDI), an existing model that has recently been assessed for use in patients with COVID-19. Methods:The PICTURE model was trained and validated on a cohort of hospitalized non-COVID-19 patients using electronic health record data from 2014 to 2018. It was then applied to two holdout test sets: non-COVID-19 patients from 2019 and patients testing positive for COVID-19 in 2020. PICTURE results were aligned to EDI and NEWS scores for head-to-head comparison via area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve. We compared the models' ability to predict an adverse event (defined as intensive care unit transfer, mechanical ventilation use, or death). Shapley values were used to provide explanations for PICTURE predictions.
technique on a large-scale clinical study (UK Biobank), which includes imaging, cognitive, 47and clinical assessment data. The UK Biobank archive presents several difficult challenges 48 related to the aggregation, harmonization, modeling, and interrogation of the information. 49These problems are related to the complex longitudinal structure, feature heterogeneity, 50 multicollinearity, incongruency, and missingness, as well as violations of classical parametric 51 assumptions that require novel health analytical approaches. 53Our results showcase the scalability, efficiency and potential of CBDA to compress complex 54 data into structural information leading to derived knowledge and translational action. The 55 results of the real case-study suggest new and exciting avenues of research in the context of 56 identifying, tracking, and treating mental health and aging-related disorders. Following open-57 science principles, we share the entire end-to-end protocol, source-code, and results. This 58 facilitates independent validation, result reproducibility, and team-based collaborative 59 discovery. 60 61 Figure 5: Dissimilarity and variance analysis of the coefficients/weights distributions of the ensemble predictor. Binomial (Panels A-C) and Null (Panels B-D)datasets analysis (each with 10,000 cases and 1,000 features). The x axis displays the top-ranked models (from 50 to 5,000). The y axis shows the mean value of the * Bray-Curtis dissimilarity distance within the SuperLearner coefficients (Panels A-B) and the variance of the SuperLearner coefficients (Panels C and D). *
When using tree-based methods to develop predictive analytics and early warning systems for preventive healthcare, it is important to use an appropriate imputation method to prevent learning the missingness pattern. To demonstrate this, we developed a novel simulation that generated synthetic electronic health record data using a variational autoencoder with a custom loss function, which took into account the high missing rate of electronic health data. We showed that when tree-based methods learn missingness patterns (correlated with adverse events) in electronic health record data, this leads to decreased performance if the system is used in a new setting that has different missingness patterns. Performance is worst in this scenario when the missing rate between those with and without an adverse event is the greatest. We found that randomized and Bayesian regression imputation methods mitigate the issue of learning the missingness pattern for tree-based methods. We used this information to build a novel early warning system for predicting patient deterioration in general wards and telemetry units: PICTURE (Predicting Intensive Care Transfers and other UnfoReseen Events). To develop, tune, and test PICTURE, we used labs and vital signs from electronic health records of adult patients over four years (n=133,089 encounters). We analyzed primary outcomes of unplanned intensive care unit transfer, emergency vasoactive medication administration, cardiac arrest, and death. We compared PICTURE with existing early warning systems and logistic regression at multiple levels of granularity. When analyzing PICTURE on the testing set using all observations within a hospital encounter (event rate = 3.4%), PICTURE had an area under the receiver operating characteristic curve (AUROC) of 0.83 and an adjusted (event rate = 4%) area under the precision-recall curve (AUPR) of 0.27, while the next best-tested method-regularized logistic regression-had an AUROC of 0.80 and an adjusted AUPR of 0.22. To ensure system interpretability, we applied a state-of-the-art prediction explainer that provided a ranked list of features contributing most to the prediction. Though it is currently difficult to compare machine learning-based early warning systems, a rudimentary comparison with published scores demonstrated that PICTURE is on par with state-of-the-art machine learning systems. To facilitate more robust comparisons and development of early warning systems in the future, we have released our variational autoencoder's code and weights so researchers can (a) test their models on data similar to our institution and (b) make their own synthetic datasets.
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