Patient Trajectories Among Persons Hospitalized for COVID-19 : A Cohort Study.

BACKGROUND: Risk factors for progression of coronavirus disease 2019 (COVID-19) to severe disease or death are underexplored in U.S. cohorts. OBJECTIVE: To determine the factors on hospital admission that are predictive of severe disease or death from COVID-19. DESIGN: Retrospective cohort analysis. SETTING: Five hospitals in the Maryland and Washington, DC, area. PATIENTS: 832 consecutive COVID-19 admissions from 4 March to 24 April 2020, with follow-up through 27 June 2020. MEASUREMENTS: Patient trajectories and outcomes, categorized by using the World Health Organization COVID-19 disease severity scale. Primary outcomes were death and a composite of severe disease or death. RESULTS: Median patient age was 64 years (range, 1 to 108 years); 47% were women, 40% were Black, 16% were Latinx, and 21% were nursing home residents. Among all patients, 131 (16%) died and 694 (83%) were discharged (523 [63%] had mild to moderate disease and 171 [20%] had severe disease). Of deaths, 66 (50%) were nursing home residents. Of 787 patients admitted with mild to moderate disease, 302 (38%) progressed to severe disease or death: 181 (60%) by day 2 and 238 (79%) by day 4. Patients had markedly different probabilities of disease progression on the basis of age, nursing home residence, comorbid conditions, obesity, respiratory symptoms, respiratory rate, fever, absolute lymphocyte count, hypoalbuminemia, troponin level, and C-reactive protein level and the interactions among these factors. Using only factors present on admission, a model to predict in-hospital disease progression had an area under the curve of 0.85, 0.79, and 0.79 at days 2, 4, and 7, respectively. LIMITATION: The study was done in a single health care system. CONCLUSION: A combination of demographic and clinical variables is strongly associated with severe COVID-19 disease or death and their early onset. The COVID-19 Inpatient Risk Calculator (CIRC), using factors present on admission, can inform clinical and resource allocation decisions. PRIMARY FUNDING SOURCE: Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.

Mannequin simulation identifies common surgical intensive care unit teamwork errors long after introduction of sepsis guidelines.

INTRODUCTION: Groups of evidence-based guidelines were developed into a comprehensive treatment bundle as part of an international-based Surviving Sepsis Campaign to improve treatment of severe sepsis and septic shock. Conventional educational strategies of this sepsis treatment "bundle" may not ensure acceptable knowledge or completion of these specific tasks and may overlook other dynamic factors present during critical moments of a crisis. Simulation using multidisciplinary teams of clinicians through mannequin-based simulations (MDMS) may improve "bundle" compliance by identifying sepsis guideline errors, reinforcing knowledge, and exposing other potential causes of poor performance. METHODS: Seventy-four clinicians participated in the MDMS 14 months after hospital-wide introduction of the sepsis bundle. Additionally, each team was given a sepsis treatment-learning packet before the training session. Twelve teams underwent a MDMS of a patient in septic shock. Two evaluators recorded completed sepsis guideline tasks in real time. Sessions were videotaped and reviewed with the team in a postscenario debriefing session. Pre/posttests were also administered. RESULTS: Individual participants' pretest scores averaged 64.6% correct. Despite all but one team having at least one knowledgeable member with a pretest score of at least 80%, team task completion averaged only 60.4%. Team mean pretest scores and proportion of tasks completed were significantly correlated (P = 0.007), but correlations between specific tasks and related questions showed no relationship to knowledge. CONCLUSION: Inadequate completion of the sepsis guideline tasks during the MDMS could not be explained by inadequate pretest knowledge alone. MDMS may be a useful tool in identifying and exploring these unknown factors.