COVID-19-associated rhabdomyolysis: A scoping review

Preger, Anna; Wei, Rebecca; Berg, Brinton; Golomb, Beatrice A.

doi:10.1016/j.ijid.2023.09.002

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…The manifestation of proximal myalgias in our patient suggests a viral myositis [ 2 ]. If rashes had been present, for example, a dermatomyositis would be suspected [ 4 ]. Rhabdomyolysis with SARS-CoV-2 is associated with higher rates of ICU admission, mechanical ventilation, and mortality [ 1 , 3 ].…”

Section: Discussionmentioning

confidence: 99%

“…Risk factors for SARS-CoV-2 rhabdomyolysis include pre-existing health conditions such as advanced age, hypertension, diabetes, cardiovascular disease, and underlying kidney disease [ 4 ]. Underlying musculoskeletal disorders and prescription medications such as statins, especially lipophilic varieties, and antipsychotics are thought to increase risk [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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Profound Rhabdomyolysis and Viral Myositis Due to SARS-CoV-2: A Case Report

Dye,

Lantz

2024

Cureus

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The novel SARS-CoV-2 introduced several new inflammatory conditions including SARS-CoV-2-associated rhabdomyolysis and viral myositis. We present a 22-year-old man who noted a week of cough followed by myalgias, dark-colored urine, and decreased oral intake. He was found to have acute nontraumatic rhabdomyolysis after an acutely positive SARS-CoV-2 test. Initial creatine kinase (CK) level was above the reference range as were liver enzymes reflective of muscle breakdown. Treatment involved fluid resuscitation and pain control, with close monitoring of kidney, liver, and skeletal markers over five days of hospitalization till there was clinical and symptomatic improvement.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Profound Rhabdomyolysis and Viral Myositis Due to SARS-CoV-2: A Case Report

Dye,

Lantz

2024

Cureus

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Interpretable machine learning model for early prediction of acute kidney injury in patients with rhabdomyolysis

Zhang,

Liang,

et al. 2024

Emerg Crit Care Med

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Background Rhabdomyolysis (RM) is a complex set of clinical syndromes. RM-induced acute kidney injury (AKI) is a common illness in war and military operations. This study aimed to develop an interpretable and generalizable model for early AKI prediction in patients with RM. Methods Retrospective analyses were performed on 2 electronic medical record databases: the eICU Collaborative Research Database and the Medical Information Mart for Intensive Care III database. Data were extracted from the first 24 hours after patient admission. Data from the two datasets were merged for further analysis. The extreme gradient boosting (XGBoost) model with the Shapley additive explanation method (SHAP) was used to conduct early and interpretable predictions of AKI. Results The analysis included 938 eligible patients with RM. The XGBoost model exhibited superior performance (area under the receiver operating characteristic curve [AUC] = 0.767) compared to the other models (logistic regression, AUC = 0.711; support vector machine, AUC = 0.693; random forest, AUC = 0.728; and naive Bayesian, AUC = 0.700). Conclusion Although the XGBoost model performance could be improved from an absolute perspective, it provides better predictive performance than other models for estimating the AKI in patients with RM based on patient characteristics in the first 24 hours after admission to an intensive care unit. Furthermore, including SHAP to elucidate AKI-related factors enables individualized patient treatment, potentially leading to improved prognoses for patients with RM.

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