Artificial intelligence sepsis prediction algorithm learns to say "I don't know".

Sepsis is a leading cause of morbidity and mortality worldwide. Early identification of sepsis is important as it allows timely administration of potentially life-saving resuscitation and antimicrobial therapy. We present COMPOSER (COnformal Multidimensional Prediction Of SEpsis Risk), a deep learning model for the early prediction of sepsis, specifically designed to reduce false alarms by detecting unfamiliar patients/situations arising from erroneous data, missingness, distributional shift and data drifts. COMPOSER flags these unfamiliar cases as indeterminate rather than making spurious predictions. Six patient cohorts (515,720 patients) curated from two healthcare systems in the United States across intensive care units (ICU) and emergency departments (ED) were used to train and externally and temporally validate this model. In a sequential prediction setting, COMPOSER achieved a consistently high area under the curve (AUC) (ICU: 0.925-0.953; ED: 0.938-0.945). Out of over 6 million prediction windows roughly 20% and 8% were identified as indeterminate amongst non-septic and septic patients, respectively. COMPOSER provided early warning within a clinically actionable timeframe (ICU: 12.2 [3.2 22.8] and ED: 2.1 [0.8 4.5] hours prior to first antibiotics order) across all six cohorts, thus allowing for identification and prioritization of patients at high risk for sepsis

Stenotrophomonas maltophilia Necrotizing Soft Tissue Infection in an Immunocompromised Patient

Introduction. To report on the first recorded case of necrotizing soft tissue infection (NSTI) in an immunocompromised individual caused by Stenotrophomonas maltophilia in the Western Hemisphere and highlight the challenges that medical providers face in promptly diagnosing and treating NSTI in this highly vulnerable patient population. Case Presentation. We report a case of NSTI caused by S. maltophilia in a neutropenic patient admitted for treatment of acute lymphoblastic leukemia. The patient presented with laboratory and clinical findings atypical for a NSTI that may have confounded its diagnosis and delayed surgical intervention. Despite aggressive medical care and surgical debridement, the patient unfortunately passed away due to overwhelming septic shock. Conclusions. Providers should consider atypical organisms as causative in NSTI in immunocompromised patients and recognize that these patients may present without classic clinical and laboratory findings

Outcomes for in-hospital cardiac arrest for COVID-19 patients at a rural hospital in Southern California.

BackgroundIn-hospital cardiac arrest (IHCA) carries a high mortality and providing resuscitation to COVID-19 patients presents additional challenges for emergency physicians. Our objective was to describe outcomes of COVID-19 patients suffering IHCA at a rural hospital in Southern California.MethodsSingle-center retrospective observational study. A hospital registry of COVID-19 patients was queried for all patients who suffered IHCA and received cardiopulmonary resuscitation (CPR) between May 1st and July 31st, 2020. A manual chart review was performed to obtain patient demographics, oxygen requirement prior to cardiac arrest (CA), details of the resuscitation including presence of an emergency physician, and final disposition.ResultsTwenty-one patients were identified, most of whom were Hispanic, male, and aged 50-70. The most common medical comorbidities were diabetes and hypertension. Most patients suffered respiratory arrest, with an initial rhythm of pulseless electrical activity or asystole. Return of spontaneous circulation (ROSC) was achieved in 3/9 patients already receiving mechanical ventilation, but all 3 expired within the following 24 h. ROSC was achieved in 10/12 patients not already intubated, though most also expired within a few days. The only 2 patients who survived to discharge suffered respiratory arrest after their oxygen delivery device dislodged.ConclusionAt a small rural hospital with limited resources and a predominantly Hispanic population, cardiac arrest in a COVID-19 patient portends an extremely poor prognosis. A better appreciation of these outcomes should help inform emergency providers and patients when discussing code status and attempts at resuscitation, particularly in resource limited settings

Anti-N-Methyl-D-Aspartate Receptor Encephalitis, an Underappreciated Disease in the Emergency Department

Anti-N-Methyl-D-Aspartate Receptor (NMDAR) Encephalitis is a novel disease discovered within the past 10 years. Antibodies directed at the NMDAR cause the patient to develop a characteristic syndrome of neuropsychiatric symptoms. Patients go on to develop autonomic dysregulation and often have prolonged hospitalizations and intensive care unit stays. There is little literature in the emergency medicine community regarding this disease process, so we report on a case we encountered in our emergency department to help raise awareness of this disease process

Anti-N-Methyl-D-Aspartate Receptor Encephalitis, an Underappreciated Disease in the Emergency Department

Anti-N-Methyl-D-Aspartate Receptor (NMDAR) Encephalitis is a novel disease discovered within the past 10 years. Antibodies directed at the NMDAR cause the patient to develop a characteristic syndrome of neuropsychiatric symptoms. Patients go on to develop autonomic dysregulation and often have prolonged hospitalizations and intensive care unit stays. There is little literature in the emergency medicine community regarding this disease process, so we report on a case we encountered in our emergency department to help raise awareness of this disease process