: Background Thromboembolic disease is common in coronavirus disease-19 (COVID-19). There is limited evidence on association of in-hospital anticoagulation (AC) with outcomes and postmortem findings. Objective To examine association of AC with in-hospital outcomes and describe thromboembolic findings on autopsies. Methods A retrospective analysis examining association of AC with mortality, intubation and major bleeding. We also conducted sub-analyses on association of therapeutic vs prophylactic AC initiated ≤48 hours from admission. We describe thromboembolic disease contextualized by pre-mortem AC among consecutive autopsies. Results Among 4,389 patients, median age was 65 years with 44% female. Compared to no AC (n=1530, 34.9%), therapeutic (n=900, 20.5%) and prophylactic AC (n=1959, 44.6%) were associated with lower in-hospital mortality (adjusted hazard ratio [aHR]=0.53; 95%CI: 0.45-0.62, and aHR=0.50; 95%CI: 0.45-0.57, respectively), and intubation (aHR 0.69; 95%CI: 0.51-0.94, and aHR 0.72; 95% CI: 0.58-0.89, respectively). When initiated ≤48 hours from admission, there was no statistically significant difference between therapeutic (n=766) vs. prophylactic AC (n=1860) (aHR 0.86, 95%CI: 0.73-1.02; p=0.08). Overall, 89 patients (2%) had major bleeding adjudicated by clinician review, with 27/900 (3.0%) on therapeutic, 33/1959 (1.7%) on prophylactic, and 29/1,530 (1.9%) on no AC. Of 26 autopsies, 11 (42%) had thromboembolic disease not clinically suspected and 3/11 (27%) were on therapeutic AC. Conclusions AC was associated with lower mortality and intubation among hospitalized COVID-19 patients. Compared to prophylactic AC, therapeutic AC was associated with lower mortality, though not statistically significant. Autopsies revealed frequent thromboembolic disease. These data may inform trials to determine optimal AC regimens.

Background Myocardial injury is frequent among patients hospitalized with coronavirus disease-2019 (COVID-19) and is associated with a poor prognosis. However, the mechanisms of myocardial injury remain unclear and prior studies have not reported cardiovascular imaging data. Objectives This study sought to characterize the echocardiographic abnormalities associated with myocardial injury and their prognostic impact in patients with COVID-19. Methods We conducted an international, multicenter cohort study including 7 hospitals in New York City and Milan of hospitalized patients with laboratory-confirmed COVID-19 who had undergone transthoracic echocardiographic (TTE) and electrocardiographic evaluation during their index hospitalization. Myocardial injury was defined as any elevation in cardiac troponin at the time of clinical presentation or during the hospitalization. Results A total of 305 patients were included. Mean age was 63 years and 205 patients (67.2%) were male. Overall, myocardial injury was observed in 190 patients (62.3%). Compared with patients without myocardial injury, those with myocardial injury had more electrocardiographic abnormalities, higher inflammatory biomarkers and an increased prevalence of major echocardiographic abnormalities that included left ventricular wall motion abnormalities, global left ventricular dysfunction, left ventricular diastolic dysfunction grade II or III, right ventricular dysfunction and pericardial effusions. Rates of in-hospital mortality were 5.2%, 18.6%, and 31.7% in patients without myocardial injury, with myocardial injury without TTE abnormalities, and with myocardial injury and TTE abnormalities. Following multivariable adjustment, myocardial injury with TTE abnormalities was associated with higher risk of death but not myocardial injury without TTE abnormalities. Conclusions Among patients with COVID-19 who underwent TTE, cardiac structural abnormalities were present in nearly two-thirds of patients with myocardial injury. Myocardial injury was associated with increased in-hospital mortality particularly if echocardiographic abnormalities were present.

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Objectives: Approximately 20–30% of patients with COVID-19 require hospitalization, and 5–12% may require critical care in an intensive care unit (ICU). A rapid surge in cases of severe COVID-19 will lead to a corresponding surge in demand for ICU care. Because of constraints on resources, frontline healthcare workers may be unable to provide the frequent monitoring and assessment required for all patients at high risk of clinical deterioration. We developed a machine learning-based risk prioritization tool that predicts ICU transfer within 24 h, seeking to facilitate efficient use of care providers’ efforts and help hospitals plan their flow of operations. Methods: A retrospective cohort was comprised of non-ICU COVID-19 admissions at a large acute care health system between 26 February and 18 April 2020. Time series data, including vital signs, nursing assessments, laboratory data, and electrocardiograms, were used as input variables for training a random forest (RF) model. The cohort was randomly split (70:30) into training and test sets. The RF model was trained using 10-fold cross-validation on the training set, and its predictive performance on the test set was then evaluated. Results: The cohort consisted of 1987 unique patients diagnosed with COVID-19 and admitted to non-ICU units of the hospital. The median time to ICU transfer was 2.45 days from the time of admission. Compared to actual admissions, the tool had 72.8% (95% CI: 63.2–81.1%) sensitivity, 76.3% (95% CI: 74.7–77.9%) specificity, 76.2% (95% CI: 74.6–77.7%) accuracy, and 79.9% (95% CI: 75.2–84.6%) area under the receiver operating characteristics curve. Conclusions: A ML-based prediction model can be used as a screening tool to identify patients at risk of imminent ICU transfer within 24 h. This tool could improve the management of hospital resources and patient-throughput planning, thus delivering more effective care to patients hospitalized with COVID-19.

OBJECTIVE: Determine the characteristics of postintensive care syndrome in the cognitive, physical, and psychiatric domains in coronavirus disease 2019 ICU survivors. DESIGN: Single-center descriptive cohort study from April 21, to July 7, 2020. SETTING: Critical care recovery clinic at The Mount Sinai Hospital in New York City. PATIENTS: Adults who had critical illness due to coronavirus disease 2019 requiring an ICU stay of 7 days or more and who agreed to a telehealth follow-up in the critical care recovery clinic 1-month post hospital discharge. INTERVENTIONS: None. MEASURES AND MAIN RESULTS: Patient-reported outcome measures assessing physical and psychiatric domains were collected electronically, a cognitive test was performed by a clinician, and clinical data were obtained through electronic medical records. Outcome measures assessed postintensive care syndrome symptoms in the physical (Modified Rankin Scale, Dalhousie Clinical Frailty Scale, Neuro-Quality of Life Upper Extremity and Lower Extremity Function, Neuro-Quality of Life Fatigue), psychiatric (Insomnia Severity Scale; Patient Health Questionnaire-9; and Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition), and cognitive (Telephone Montreal Cognitive Assessment) domains. The 3-Level Version of Euro-QoL-5D was used to assess the physical and psychiatric domains. A diagnosis of postintensive care syndrome was made in cases with evidence of impairment in at least one postintensive care syndrome domain. We included 45 patients with a mean (sd) age of 54 (13) years, and 73% were male. Ninety-one percent of coronavirus disease 2019 ICU survivors fit diagnostic criteria for postintensive care syndrome. 86.7 % had impairments in the physical domain, 22 (48%) reported impairments in the psychiatric domain, and four (8%) had impairments on cognitive screening. We found that 58% had some degree of mobility impairment. In the psychiatric domain, 38% exhibited at least mild depression, and 18 % moderate to severe depression. Eighteen percent presented Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, scores suggestive of posttraumatic stress syndrome diagnosis. In the Telephone Montreal Cognitive Assessment, 9% had impaired cognition. CONCLUSIONS: Survivors of critical illness related to coronavirus disease 2019 are at high risk of developing postintensive care syndrome. These findings highlight the importance of planning for appropriate post-ICU care to diagnose and treat this population.