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Objectives: Prognostication of neurologic status among survivors of in-hospital cardiac arrests remains a challenging task for physicians. Although models such as the Cardiac Arrest Survival Post-Resuscitation In-hospital score are useful for predicting neurologic outcomes, they were developed using traditional statistical techniques. In this study, we derive and compare the performance of several machine learning models with each other and with the Cardiac Arrest Survival Post-Resuscitation In-hospital score for predicting the likelihood of favorable neurologic outcomes among survivors of resuscitation.Design: Analysis of the Get With The Guidelines-Resuscitation registry.Setting: Seven-hundred fifty-five hospitals participating in Get With The Guidelines-Resuscitation from January 1, 2001, to January 28, 2017.Patients: Adult in-hospital cardiac arrest survivors.Interventions: None.Measurements and Main Results: Of 117,674 patients in our cohort, 28,409 (24%) had a favorable neurologic outcome, as defined as survival with a Cerebral Performance Category score of less than or equal to 2 at discharge. Using patient characteristics, pre-existing conditions, prearrest interventions, and periarrest variables, we constructed logistic regression, support vector machines, random forests, gradient boosted machines, and neural network machine learning models to predict favorable neurologic outcome. Events prior to October 20, 2009, were used for model derivation, and all subsequent events were used for validation. The gradient boosted machine predicted favorable neurologic status at discharge significantly better than the Cardiac Arrest Survival Post-Resuscitation In-hospital score (C-statistic: 0.81 vs 0.73; p < 0.001) and outperformed all other machine learning models in terms of discrimination, calibration, and accuracy measures. Variables that were consistently most important for prediction across all models were duration of arrest, initial cardiac arrest rhythm, admission Cerebral Performance Category score, and age.Conclusions: The gradient boosted machine algorithm was the most accurate for predicting favorable neurologic outcomes in in-hospital cardiac arrest survivors. Our results highlight the utility of machine learning for predicting neurologic outcomes in resuscitated patients. [ABSTRACT FROM AUTHOR]

Abstract Background Data generated from liquid chromatography coupled to high-resolution mass spectrometry (LC-MS)-based studies of a biological sample can contain large amounts of biologically significant information in the form of proteins, peptides, and metabolites. Interpreting this data involves inferring the masses and abundances of biomolecules injected into the instrument. Because of the inherent complexity of mass spectral patterns produced by these biomolecules, the analysis is significantly enhanced by using visualization capabilities to inspect and confirm results. In this paper we describe Decon2LS, an open-source software package for automated processing and visualization of high-resolution MS data. Drawing extensively on algorithms developed over the last ten years for ICR2LS, Decon2LS packages the algorithms as a rich set of modular, reusable processing classes for performing diverse functions such as reading raw data, routine peak finding, theoretical isotope distribution modelling, and deisotoping. Because the source code is openly available, these functionalities can now be used to build derivative applications in relatively fast manner. In addition, Decon2LS provides an extensive set of visualization tools, such as high performance chart controls. Results With a variety of options that include peak processing, deisotoping, isotope composition, etc, Decon2LS supports processing of multiple raw data formats. Deisotoping can be performed on an individual scan, an individual dataset, or on multiple datasets using batch processing. Other processing options include creating a two dimensional view of mass and liquid chromatography (LC) elution time features, generating spectrum files for tandem MS data, creating total intensity chromatograms, and visualizing theoretical peptide profiles. Application of Decon2LS to deisotope different datasets obtained across different instruments yielded a high number of features that can be used to identify and quantify peptides in the biological sample. Conclusion Decon2LS is an efficient software package for discovering and visualizing features in proteomics studies that require automated interpretation of mass spectra. Besides being easy to use, fast, and reliable, Decon2LS is also open-source, which allows developers in the proteomics and bioinformatics communities to reuse and refine the algorithms to meet individual needs. Decon2LS source code, installer, and tutorials may be downloaded free of charge at http://http:/ncrr.pnl.gov/software/.

Objective: To investigate whether a patient's proximity to the nurse's station or ward entrance at time of admission was associated with increased risk of adverse outcomes. Method: We conducted a retrospective cohort study of consecutive adult inpatients to 13 medical–surgical wards at an academic hospital from 2009 to 2013. Proximity of admission room to the nurse's station and to the ward entrance was measured using Euclidean distances. Outcomes of interest include development of critical illness (defined as cardiac arrests or transfer to an intensive care unit), inhospital mortality, and increase in length of stay (LOS). Results: Of the 83,635 admissions, 4,129 developed critical illness and 1,316 died. The median LOS was 3 days. After adjusting for admission severity of illness, ward, shift, and year, we found no relationship between proximity at admission to nurse's station our outcomes. However, patients admitted to end of the ward had higher risk of developing critical illness (odds ratio [ OR ] = 1.15, 95% confidence interval [CI] = [1.08, 1.23]), mortality (OR = 1.16, 95% CI [1.03, 1.33]), and a higher LOS (13-hr increase, 95% CI [10, 15] hours) compared to patients admitted closer to the ward entrance. Similar results were observed in sensitivity analyses adjusting for isolation room patients and considering patients without room transfers in the first 48 hr. Conclusions: Our study suggests that being away from the nurse's station did not increase the risk of these adverse events in ward patients, but being farther from the ward entrance was associated with increase in risk of adverse outcomes. Patient safety can be improved by recognizing this additional risk factor. [ABSTRACT FROM AUTHOR]

The clinical course of cerebral cavernous malformations (CCMs) is highly variable. Based on recent discoveries implicating angiogenic and inflammatory mechanisms, we hypothesized that serum biomarkers might reflect chronic or acute disease activity. This single-site prospective observational cohort study included 85 CCM patients, in whom 24 a priori chosen plasma biomarkers were quantified and analyzed in relation to established clinical and imaging parameters of disease categorization and severity. We subsequently validated the positive correlations in longitudinal follow-up of 49 subjects. Plasma levels of matrix metalloproteinase-2 and intercellular adhesion molecule 1 were significantly higher ( P = 0.02 and P = 0.04, respectively, FDR corrected), and matrix metalloproteinase-9 was lower ( P = 0.04, FDR corrected) in patients with seizure activity at any time in the past. Vascular endothelial growth factor and endoglin (both P = 0.04, FDR corrected) plasma levels were lower in patients who had suffered a symptomatic bleed in the prior 3 months. The hierarchical clustering analysis revealed a cluster of four plasma inflammatory cytokines (interleukin 2, interferon gamma, tumor necrosis factor alpha, and interleukin 1 beta) separating patients into what we designated 'high' and 'low' inflammatory states. The 'high' inflammatory state was associated with seizure activity ( P = 0.02) and more than one hemorrhagic event during a patient's lifetime ( P = 0.04) and with a higher rate of new hemorrhage, lesion growth, or new lesion formation ( P < 0.05) during prospective follow-up. Peripheral plasma biomarkers reflect seizure and recent hemorrhagic activity in CCM patients. In addition, four clustered inflammatory biomarkers correlate with cumulative disease aggressiveness and predict future clinical activity. [ABSTRACT FROM AUTHOR]

Here, we investigate if adding longitudinal trends for pCART variables improves model performance. We recently developed pCART, a gradient-boosted machine (GBM) learning model to predict the risk of deterioration using vital signs and laboratory results from the electronic health record. The trend--based GBM model slightly outperformed pCART in predicting clinical deterioration events within the next 12 hours (AUC 0.85 vs. 0.84, P< 0.001). [Extracted from the article]