Rhabdomyolysis in Severe COVID-19: Male Sex, High Body Mass Index, and Prone Positioning Confer High Risk.

BACKGROUND: Bedside experience and studies of critically ill patients with coronavirus disease 2019 (COVID-19) indicate COVID-19 to be a devastating multisystem disease. We aim to describe the incidence, associated variables, and outcomes of rhabdomyolysis in critically ill COVID-19 patients. MATERIALS AND METHODS: Data for all critically ill adult patients (≥18 years old) admitted to the ICU at a large academic medical center with confirmed COVID-19 between March 13, 2020 and April 18, 2020 were prospectively collected. Patients with serum creatine kinase (CK) concentrations greater than 1000 U/L were diagnosed with rhabdomyolysis. Patients were further stratified as having moderate (serum CK concentration 1000-4999 U/L) or severe (serum CK concentration ≥5000 U/L) rhabdomyolysis. Univariate and multivariate analyses were performed to identify outcomes and variables associated with the development of rhabdomyolysis. RESULTS: Of 235 critically ill COVID-19 patients, 114 (48.5%) met diagnostic criteria for rhabdomyolysis. Patients with rhabdomyolysis more often required mechanical ventilation (P < 0.001), prone positioning (P < 0.001), pharmacological paralysis (P < 0.001), renal replacement therapy (P = 0.010), and extracorporeal membrane oxygenation (ECMO) (P = 0.025). They also had longer median ICU length of stay (LOS) (P < 0.001) and hospital LOS (P < 0.001). No difference in mortality was observed. Male sex, patients with morbid obesity, SOFA score, and prone positioning were independently associated with rhabdomyolysis. CONCLUSIONS: Nearly half of critically ill COVID-19 patients in our cohort met diagnostic criteria for rhabdomyolysis. Male sex, morbid obesity, SOFA score, and prone position were independently associated with rhabdomyolysis.

Multisystem outcomes and predictors of mortality in critically ill patients with COVID-19: Demographics and disease acuity matter more than comorbidities or treatment modalities.

BACKGROUND: We sought to describe characteristics, multisystem outcomes, and predictors of mortality of the critically ill COVID-19 patients in the largest hospital in Massachusetts. METHODS: This is a prospective cohort study. All patients admitted to the intensive care unit (ICU) with reverse-transcriptase-polymerase chain reaction-confirmed severe acute respiratory syndrome coronavirus 2 infection between March 14, 2020, and April 28, 2020, were included; hospital and multisystem outcomes were evaluated. Data were collected from electronic records. Acute respiratory distress syndrome (ARDS) was defined as PaO2/FiO2 ratio of ≤300 during admission and bilateral radiographic pulmonary opacities. Multivariable logistic regression analyses adjusting for available confounders were performed to identify predictors of mortality. RESULTS: A total of 235 patients were included. The median (interquartile range [IQR]) Sequential Organ Failure Assessment score was 5 (3-8), and the median (IQR) PaO2/FiO2 was 208 (146-300) with 86.4% of patients meeting criteria for ARDS. The median (IQR) follow-up was 92 (86-99) days, and the median ICU length of stay was 16 (8-25) days; 62.1% of patients were proned, 49.8% required neuromuscular blockade, and 3.4% required extracorporeal membrane oxygenation. The most common complications were shock (88.9%), acute kidney injury (AKI) (69.8%), secondary bacterial pneumonia (70.6%), and pressure ulcers (51.1%). As of July 8, 2020, 175 patients (74.5%) were discharged alive (61.7% to skilled nursing or rehabilitation facility), 58 (24.7%) died in the hospital, and only 2 patients were still hospitalized, but out of the ICU. Age (odds ratio [OR], 1.08; 95% confidence interval [CI], 1.04-1.12), higher median Sequential Organ Failure Assessment score at ICU admission (OR, 1.24; 95% CI, 1.06-1.43), elevated creatine kinase of ≥1,000 U/L at hospital admission (OR, 6.64; 95% CI, 1.51-29.17), and severe ARDS (OR, 5.24; 95% CI, 1.18-23.29) independently predicted hospital mortality.Comorbidities, steroids, and hydroxychloroquine treatment did not predict mortality. CONCLUSION: We present here the outcomes of critically ill patients with COVID-19. Age, acuity of disease, and severe ARDS predicted mortality rather than comorbidities. LEVEL OF EVIDENCE: Prognostic, level III.

Early prediction of level-of-care requirements in patients with COVID-19.

This study examined records of 2566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several classification methods were applied and compared against standard pneumonia severity scores. The need for hospitalization, ICU care, and mechanical ventilation were predicted with a validation accuracy of 88%, 87%, and 86%, respectively. Pneumonia severity scores achieve respective accuracies of 73% and 74% for ICU care and ventilation. When predictions are limited to patients with more complex disease, the accuracy of the ICU and ventilation prediction models achieved accuracy of 83% and 82%, respectively. Vital signs, age, BMI, dyspnea, and comorbidities were the most important predictors of hospitalization. Opacities on chest imaging, age, admission vital signs and symptoms, male gender, admission laboratory results, and diabetes were the most important risk factors for ICU admission and mechanical ventilation. The factors identified collectively form a signature of the novel COVID-19 disease.

The new coronavirus (now named SARS-CoV-2) causing the disease pandemic in 2019 (COVID-19), has so far infected over 35 million people worldwide and killed more than 1 million. Most people with COVID-19 have no symptoms or only mild symptoms. But some become seriously ill and need hospitalization. The sickest are admitted to an Intensive Care Unit (ICU) and may need mechanical ventilation to help them breath. Being able to predict which patients with COVID-19 will become severely ill could help hospitals around the world manage the huge influx of patients caused by the pandemic and save lives. Now, Hao, Sotudian, Wang, Xu et al. show that computer models using artificial intelligence technology can help predict which COVID-19 patients will be hospitalized, admitted to the ICU, or need mechanical ventilation. Using data of 2,566 COVID-19 patients from five Massachusetts hospitals, Hao et al. created three separate models that can predict hospitalization, ICU admission, and the need for mechanical ventilation with more than 86% accuracy, based on patient characteristics, clinical symptoms, laboratory results and chest x-rays. Hao et al. found that the patients' vital signs, age, obesity, difficulty breathing, and underlying diseases like diabetes, were the strongest predictors of the need for hospitalization. Being male, having diabetes, cloudy chest x-rays, and certain laboratory results were the most important risk factors for intensive care treatment and mechanical ventilation. Laboratory results suggesting tissue damage, severe inflammation or oxygen deprivation in the body's tissues were important warning signs of severe disease. The results provide a more detailed picture of the patients who are likely to suffer from severe forms of COVID-19. Using the predictive models may help physicians identify patients who appear okay but need closer monitoring and more aggressive treatment. The models may also help policy makers decide who needs workplace accommodations such as being allowed to work from home, which individuals may benefit from more frequent testing, and who should be prioritized for vaccination when a vaccine becomes available.

A low dose heparinized saline protocol is associated with improved duration of arterial line patency in critically ill COVID-19 patients.

PURPOSE: Critically ill patients with Coronavirus Disease 2019 (COVID-19) have high rates of line thrombosis. Our objective was to examine the safety and efficacy of a low dose heparinized saline (LDHS) arterial line (a-line) patency protocol in this population. MATERIALS AND METHODS: In this observational cohort study, patients ≥18 years with COVID-19 admitted to an ICU at one institution from March 20-May 25, 2020 were divided into two cohorts. Pre-LDHS patients had an episode of a-line thrombosis between March 20-April 19. Post-LDHS patients had an episode of a-line thrombosis between April 20-May 25 and received an LDHS solution (10 units/h) through their a-line pressure bag. RESULTS: Forty-one patients (pre-LDHS) and 30 patients (post-LDHS) were identified. Baseline characteristics were similar between groups, including age (61 versus 54 years; p = 0.24), median Sequential Organ Failure Assessment score (6 versus 7; p = 0.67) and systemic anticoagulation (47% versus 32%; p = 0.32). Median duration of a-line patency was significantly longer in post-LDHS versus pre-LDHS patients (8.5 versus 2.9 days; p < 0.001). The incidence of bleeding complications was similar between cohorts (13% vs. 10%; p = 0.71). CONCLUSIONS: A LDHS protocol was associated with a clinically significant improvement in a-line patency duration in COVID-19 patients, without increased bleeding risk.