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Objective We sought to evaluate the influence of several well‐documented, readily available risk factors that may influence a psychiatric consultant's decision to admit an emergency department (ED) patient reporting suicidal ideation for psychiatric hospitalization. Methods We conducted a retrospective study of adult patients presenting to six affiliated EDs within Pennsylvania from January 2015 to June 2017. We identified 533 patients reporting current active suicidal ideation and receiving a complete psychiatric consultation. Socio‐demographic characteristics, psychiatric presentation and history, and disposition were collected. Decision tree analysis was conducted with disposition as the outcome. Results Four of 27 variables emerged as most influential to decisionmaking, including psychiatric consultant determination of current suicide risk, patient age, current depressive disorder diagnosis, and patient history of physical violence. Likelihood of admission versus discharge ranged from 97% to 58%, depending on the variables considered. Post hoc analysis indicated that current suicide plan, access to means, lack of social support, and suicide attempt history were significantly associated with psychiatric consultant determination of moderate‐to‐high suicide risk, with small‐to‐medium effect sizes emerging. Conclusions Only a handful of variables drive disposition decisions for ED patients reporting current active suicidal ideation, with both high and low fidelity decisions made. Patient suicide risk, determined by considering empirically supported risk factors for suicide attempt and death, contributes the greatest influence on a psychiatric consultant's decision to admit. In line with American College of Emergency Physicians (ACEP) recommendations, this study accentuates the importance of using clinical judgment and adjunct measures to determine patient disposition within this population.

Background. SARS-CoV-2 infected patients are suggested to have a higher incidence of thrombotic events such as acute ischemic strokes (AIS). This study aimed at exploring vascular comorbidity patterns among SARS-CoV-2 infected patients with subsequent stroke. We also investigated whether the comorbidities and their frequencies under each subclass of TOAST criteria were similar to the AIS population studies prior to the pandemic. Methods. This is a report from the Multinational COVID-19 Stroke Study Group. We present an original dataset of SASR-CoV-2 infected patients who had a subsequent stroke recorded through our multicenter prospective study. In addition, we built a dataset of previously reported patients by conducting a systematic literature review. We demonstrated distinct subgroups by clinical risk scoring models and unsupervised machine learning algorithms, including hierarchical K-Means (ML-K) and Spectral clustering (ML-S). Results. This study included 323 AIS patients from 71 centers in 17 countries from the original dataset and 145 patients reported in the literature. The unsupervised clustering methods suggest a distinct cohort of patients (ML-K: 36% and ML-S: 42%) with no or few comorbidities. These patients were more than 6 years younger than other subgroups and more likely were men (ML-K: 59% and ML-S: 60%). The majority of patients in this subgroup suffered from an embolic-appearing stroke on imaging (ML-K: 83% and ML-S: 85%) and had about 50% risk of large vessel occlusions (ML-K: 50% and ML-S: 53%). In addition, there were two cohorts of patients with large-artery atherosclerosis (ML-K: 30% and ML-S: 43% of patients) and cardioembolic strokes (ML-K: 34% and ML-S: 15%) with consistent comorbidity and imaging patterns. Binominal logistic regression demonstrated that ischemic heart disease (odds ratio (OR), 4.9, 95% confidence interval (CI), 1.6–14.7), atrial fibrillation (OR, 14.0, 95% CI, 4.8–40.8), and active neoplasm (OR, 7.1, 95% CI, 1.4–36.2) were associated with cardioembolic stroke. Conclusions. Although a cohort of young and healthy men with cardioembolic and large vessel occlusions can be distinguished using both clinical sub-grouping and unsupervised clustering, stroke in other patients may be explained based on the existing comorbidities.

Background and Purpose: Stroke is reported as a consequence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in several reports. However, data are sparse regarding the details of these patients in a multinational and large scale. Methods: We conducted a multinational observational study on features of consecutive acute ischemic stroke, intracranial hemorrhage, and cerebral venous or sinus thrombosis among SARS-CoV-2–infected patients. We further investigated the risk of large vessel occlusion, stroke severity as measured by the National Institutes of Health Stroke Scale, and stroke subtype as measured by the TOAST (Trial of ORG 10172 in Acute Stroke Treatment) criteria among patients with acute ischemic stroke. In addition, we explored the neuroimaging findings, features of patients who were asymptomatic for SARS-CoV-2 infection at stroke onset, and the impact of geographic regions and countries’ health expenditure on outcomes. Results: Among the 136 tertiary centers of 32 countries who participated in this study, 71 centers from 17 countries had at least 1 eligible stroke patient. Of 432 patients included, 323 (74.8%) had acute ischemic stroke, 91 (21.1%) intracranial hemorrhage, and 18 (4.2%) cerebral venous or sinus thrombosis. A total of 183 (42.4%) patients were women, 104 (24.1%) patients were P =0.02) and higher rate of mechanical thrombectomy (12.4% versus 2%; P Conclusions: We observed a considerably higher rate of large vessel occlusions, a much lower rate of small vessel occlusion and lacunar infarction, and a considerable number of young stroke when compared with the population studies before the pandemic. The rate of mechanical thrombectomy was significantly lower in countries with lower health expenditures.

Background: Developing a decision support system based on advances in machine learning is one area for strategic innovation in healthcare. Predicting a patient&rsquo, s progression to septic shock is an active field of translational research. The goal of this study was to develop a working model of a clinical decision support system for predicting septic shock in an acute care setting for up to 6 h from the time of admission in an integrated healthcare setting. Method: Clinical data from Electronic Health Record (EHR), at encounter level, were used to build a predictive model for progression from sepsis to septic shock up to 6 h from the time of admission, that is, T = 1, 3, and 6h from admission. Eight different machine learning algorithms (Random Forest, XGBoost, C5.0, Decision Trees, Boosted Logistic Regression, Support Vector Machine, Logistic Regression, Regularized Logistic, and Bayes Generalized Linear Model) were used for model development. Two adaptive sampling strategies were used to address the class imbalance. Data from two sources (clinical and billing codes) were used to define the case definition (septic shock) using the Centers for Medicare &amp, Medicaid Services (CMS) Sepsis criteria. The model assessment was performed using Area under Receiving Operator Characteristics (AUROC), sensitivity, and specificity. Model predictions for each feature window (1, 3 and 6 h from admission) were consolidated. Results: Retrospective data from April 2005 to September 2018 were extracted from the EHR, Insurance Claims, Billing, and Laboratory Systems to create a dataset for septic shock detection. The clinical criteria and billing information were used to label patients into two classes-septic shock patients and sepsis patients at three different time points from admission, creating two different case-control cohorts. Data from 45,425 unique in-patient visits were used to build 96 prediction models comparing clinical-based definition versus billing-based information as the gold standard. Of the 24 consolidated models (based on eight machine learning algorithms and three feature windows), four models reached an AUROC greater than 0.9. Overall, all the consolidated models reached an AUROC of at least 0.8820 or higher. Based on the AUROC of 0.9483, the best model was based on Random Forest, with a sensitivity of 83.9% and specificity of 88.1%. The sepsis detection window at 6 h outperformed the 1 and 3-h windows. The sepsis definition based on clinical variables had improved performance when compared to the sepsis definition based on only billing information. Conclusion: This study corroborated that machine learning models can be developed to predict septic shock using clinical and administrative data. However, the use of clinical information to define septic shock outperformed models developed based on only administrative data. Intelligent decision support tools can be developed and integrated into the EHR and improve clinical outcomes and facilitate the optimization of resources in real-time.

Stroke is the fifth leading cause of death in the United States and a major cause of severe disability worldwide. Yet, recognizing the signs of stroke in an acute setting is still challenging and leads to loss of opportunity to intervene, given the narrow therapeutic window. A decision support system using artificial intelligence (AI) and clinical data from electronic health records combined with patients' presenting symptoms can be designed to support emergency department providers in stroke diagnosis and subsequently reduce the treatment delay. In this article, we present a practical framework to develop a decision support system using AI by reflecting on the various stages, which could eventually improve patient care and outcome. We also discuss the technical, operational, and ethical challenges of the process. Geisinger Health Plan Quality Fund; National Institute of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R56HL116832] This work was sponsored in part by funds from the Geisinger Health Plan Quality Fund and National Institute of Health R56HL116832 (subaward) to VA and RZ. The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.

The average survival time for untreated primary carcinoma of the lung, from the onset of symptoms, is 8.2 months [1]. Lung cancer is the second most common cancer in both men and women. Early detection of malignancy and treatment of lung nodules increases chances of survival. In a medium to large sized health system, hundreds of chest, thorax, and abdominal CT scans are performed daily. Many of these scans are looking for problems with organs other than the lung. The radiologist, while reading the CT scans, makes notes about findings on pulmonary nodules — an incidental finding. Additional follow-up is often delayed or missed all together, as the pulmonary nodule was not the chief complaint for the visit or the reason for the imaging study. Geisinger's Close-the-Loop clinical program uses Natural Language Processing (NLP) of radiology notes to isolate pulmonary nodule mentions and the RADS score associated with the nodule to determine the urgency of follow-up recommendations. Close-the-Loop care managers, using information output from the NLP, contact patients for follow-up visits and treatments. The NLP algorithm is complicated by variations in style, content, context, and negations of the text being analyzed by the machine. The other complication arises with processing large number of notes efficiently and in a timely manner. Geisinger's lung nodule NLP solution analyzed millions of radiology notes and found thousands of patients with need for follow-up care with a validated accuracy of 90%.