Background: COVID-19 is a novel disease caused by SARS-CoV-2. Methods: We conducted a retrospective evaluation of patients admitted with COVID-19 to one site in March 2020. Patients were stratified into 3 groups: survivors who did not receive mechanical ventilation (MV), survivors who received MV, and those who received MV and died during hospitalization. Results: There were 140 hospitalizations; 22 deaths (mortality rate 15.7%), 83 (59%) survived and did not receive MV, 35 (25%) received MV and survived; 18 (12.9%) received MV and died. Thee mean age of each group was 57.8, 55.8 and 72.7 years, respectively (P = .0001). Of those who received MV and died, 61% were male (P = .01). More than half the patients (n = 90, 64%) were African American. First measured d-dimer >575.5 ng/mL, procalcitonin > 0.24 ng/mL, lactate dehydrogenase >445.6 units/L, and brain natriuretic peptide (BNP) >104.75 pg/mL had odds ratios of 10.5, 5, 4.5 and 2.9, respectively for MV (P < .05 for all). Peak BNP >167.5 pg/mL had an odds ratio of 6.7 for inpatient mortality when mechanically ventilated (P = .02). Conclusions: Age and gender may impact outcomes in COVID-19. D-dimer, procalcitonin, lactate dehydrogenase and BNP may serve as early indicators of disease trajectory.

Patient movements following hospitalization are difficult to track. In a large Midwestern academic institution, we analyzed data using network statistics for patients discharged by the hospitalist service between June 2016-June 2018. We retrieved all major patient movements logged in the patient throughput management system following admission. The 4,869 patients discharged by the hospitalist service during the study period experienced 6,832 movements. The mean was 1.4 movements per patient while the maximum was 8. Most patients (72.3%) moved once following hospitalization while 27.7% moved more than once. The predominant movement type was downgrades which comprised 51.8% (n = 3,543) of all movements. Lateral movements were the next most common (25.9%, n = 1,771). Network statistics revealed progressive care units to be central to patient flow across the system. Transfers following hospitalization are common. Visualizing these transfers using network statistics may provide valuable insights to enhance patient safety.