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Abstract Details

Acute Skeletal Muscle Injury after SARS-CoV-2 Infection: A Cerner Real-World COVID-19 De-identified Dataset Analysis
Neuromuscular and Clinical Neurophysiology (EMG)
Neuromuscular and Clinical Neurophysiology (EMG) Posters (7:00 AM-5:00 PM)
121

Investigate the occurrence of acute skeletal muscle injury in COVID-19 patients.
Mounting evidence suggests that COVID-19 patients are at risk for acute skeletal muscle injury, including myopathy, myositis, and rhabdomyolysis.
Using the Cerner COVID-19 de-identified dataset, we identified patients with COVID-19 infection and acute skeletal muscle injury—myopathy, myositis, rhabdomyolysis (COVID-19A) using ICD-10-CM. We compared outcomes of COVID-19A to: COVID-19 patients without acute skeletal muscle injury (COVID-19NA), non-COVID-19 patients with acute skeletal muscle injury (non-COVID-19A), and non-COVID-19 patients without acute skeletal muscle injury (non-COVID-19NA).

Among a total of 117,496 patients, 25,261 were diagnosed with COVID-19. The incidence of acute skeletal muscle injury was higher in COVID-19 patients compared to non-COVID-19 patients (1.5% vs 1.3%). The average length of hospitalization in days (LoS) and mortality rate were significantly higher in COVID-19A compared to COVID-19NA and non-COVID-19A as well as non-COVID-19N, respectively 12.7, 25.8%; 5.3, 8.2%; 3.7, 14.9%; 2.5, 3.4% (p<0.05) Discharge rate to skilled nursing facilities (SNF) and to hospices were significantly higher in COVID-19A compared to COVID-19NA and non-COVID-19A as well as non-COVID-19N, respectively 11.3%, 15.7%; 3.1%, 6.8%; 0.65, 5.2%; 0.34%, 1.77% (p<0.05).

Our study demonstrated an increased incidence of acute skeletal muscle injury in COVID-19 patients and that acute skeletal muscle injury is associated with factors of poor prognosis: increased mortality rate, longer LoS, and a higher rate of discharge to SNF and to hospices. Work is in progress to assess whether the occurrence of acute skeletal muscle injury in COVID-19 patients is an independent factor of poor prognosis. Furthermore, risk stratification machine learning (ML)-based models are under development to predict the likelihood of a COVID-19 patient with and without acute skeletal muscle injury experiencing poor outcomes.

 
Authors/Disclosures

PRESENTER 		No disclosure on file
Parisorn Thepmankorn (Rutgers New Jersey Medical School) Ms. Thepmankorn has received personal compensation for serving as an employee of Johnson and Johnson.
Keyvan Heshmati, MD Dr. Heshmati has nothing to disclose.
Rishita Patlolla, MD (Rishita Patlolla) Ms. Patlolla has nothing to disclose.
Claire Ruane Ms. Ruane has nothing to disclose.
No disclosure on file
No disclosure on file
Nizar Souayah, MD, FÂ鶹´«Ã½Ó³»­ (NJMS) Dr. Souayah has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Takeda. Dr. Souayah has received publishing royalties from a publication relating to health care.