Discriminating Acute Respiratory Distress Syndrome from other forms of respiratory failure via iterative machine learning.

Acute Respiratory Distress Syndrome (ARDS) is associated with high morbidity and mortality. Identification of ARDS enables lung protective strategies, quality improvement interventions, and clinical trial enrolment, but remains challenging particularly in the first 24 hours of mechanical ventilation. To address this we built an algorithm capable of discriminating ARDS from other similarly presenting disorders immediately following mechanical ventilation. Specifically, a clinical team examined medical records from 1263 ICU-admitted, mechanically ventilated patients, retrospectively assigning each patient a diagnosis of "ARDS" or "non-ARDS" (e.g., pulmonary edema). Exploiting data readily available in the clinical setting, including patient demographics, laboratory test results from before the initiation of mechanical ventilation, and features extracted by natural language processing of radiology reports, we applied an iterative pre-processing and machine learning framework. The resulting model successfully discriminated ARDS from non-ARDS causes of respiratory failure (AUC = 0.85) among patients meeting Berlin criteria for severe hypoxia. This analysis also highlighted novel patient variables that were informative for identifying ARDS in ICU settings.

Automated Pulmonary Embolism Risk Assessment Using the Wells Criteria: Validation Study.

BACKGROUND: Computed tomography pulmonary angiography (CTPA) is frequently used in the emergency department (ED) for the diagnosis of pulmonary embolism (PE), while posing risk for contrast-induced nephropathy and radiation-induced malignancy. OBJECTIVE: We aimed to create an automated process to calculate the Wells score for pulmonary embolism for patients in the ED, which could potentially reduce unnecessary CTPA testing. METHODS: We designed an automated process using electronic health records data elements, including using a combinatorial keyword search method to query free-text fields, and calculated automated Wells scores for a sample of all adult ED encounters that resulted in a CTPA study for PE at 2 tertiary care hospitals in New York, over a 2-month period. To validate the automated process, the scores were compared to those derived from a 2-clinician chart review. RESULTS: A total of 202 ED encounters resulted in a completed CTPA to form the retrospective study cohort. Patients classified as "PE likely" by the automated process (126/202, 62%) had a PE prevalence of 15.9%, whereas those classified as "PE unlikely" (76/202, 38%; Wells score >4) had a PE prevalence of 7.9%. With respect to classification of the patient as "PE likely," the automated process achieved an accuracy of 92.1% when compared with the chart review, with sensitivity, specificity, positive predictive value, and negative predictive value of 93%, 90.5%, 94.4%, and 88.2%, respectively. CONCLUSIONS: This was a successful development and validation of an automated process using electronic health records data elements, including free-text fields, to classify risk for PE in ED visits.

A Geriatrics-Focused Hospitalist Trauma Comanagement Program Improves Quality of Care for Older Adults.

This study aimed to determine whether a geriatrics-focused hospitalist trauma comanagement program improves quality of care. A pre-/post-implementation study compared older adult trauma patients who were comanaged by a hospitalist with those prior to comanagement at a level 1 trauma center. One-to-one propensity score matching was performed based on age, gender, Injury Severity Score, comorbidity index, and critical illness on admission. Outcomes included orders for geriatrics-focused quality indicators, as well as hospital mortality and length of stay. Wilcoxon rank-sum test (continuous variables) and chi-square or Fisher exact test (categorical variables) were used to assess differences. Propensity score matching resulted in 290 matched pairs. The intervention group had decreased use of restraints (P = 0.04) and acetaminophen (P = 0.01), and earlier physical therapy (P = 0.01). Three patients died in the intervention group compared with 14 in the control (P = 0.0068). This study highlights that a geriatrics-focused hospitalist trauma comanagement program improves quality of care.

Anti-N-methyl-d-aspartate receptor encephalitis triggered by emotional stress.

A 22-year-old woman presented with disorganized behaviors, restlessness, and subacute decline in mental status in the setting of stress. Extensive workup for autoimmune diseases disclosed positive anti-N-methyl-d-aspartate (NMDA) receptor antibodies. We recommend that fertility preservation should be discussed and stress management should be considered in patients with a history of anti-NMDA autoimmune encephalitis because this can help in preventing relapse.

Assessing the Global Burden of Ischemic Heart Disease: Part 1: Methods for a Systematic Review of the Global Epidemiology of Ischemic Heart Disease in 1990 and 2010.

BACKGROUND: Ischemic heart disease (IHD) is the leading cause of death worldwide. The GBD (Global Burden of Disease, Injuries, and Risk Factors) study (GBD 2010 Study) conducted a systematic review of IHD epidemiology literature from 1980 to 2008 to inform estimates of the burden on IHD in 21 world regions in 1990 and 2010. METHODS: The disease model of IHD for the GBD 2010 Study included IHD death and 3 sequelae: myocardial infarction, heart failure, and angina pectoris. Medline, EMBASE, and LILACS were searched for IHD epidemiology studies in GBD high-income and low- and middle-income regions published between 1980 and 2008 using a systematic protocol validated by regional IHD experts. Data from included studies were supplemented with unpublished data from selected high-quality surveillance and survey studies. The epidemiologic parameters of interest were incidence, prevalence, case fatality, and mortality. RESULTS: Literature searches yielded 40,205 unique papers, of which 1,801 met initial screening criteria. Upon detailed review of full text papers, 137 published studies were included. Unpublished data were obtained from 24 additional studies. Data were sufficient for high-income regions, but missing or sparse in many low- and middle-income regions, particularly Sub-Saharan Africa. CONCLUSIONS: A systematic review for the GBD 2010 Study provided IHD epidemiology estimates for most world regions, but highlighted the lack of information about IHD in Sub-Saharan Africa and other low-income regions. More complete knowledge of the global burden of IHD will require improved IHD surveillance programs in all world regions.