Systematic review with meta-analysis of mid-regional pro-adrenomedullin (MR-proadm) as a prognostic marker in Covid-19-hospitalized patients.
Bartosz FialekCharles De RoquetailladeMichal PrucAlla NavolokinaAleksandra GaseckaJerzy Robert LadnyFrank William PeacockAgnieszka SzarpakPublished in: Annals of medicine (2023)
The main finding of this study is that mortality of COVID-19 is linked to MR-proADM levels, according to this meta-analysis. The use of MR-proADM might be extremely beneficial in triaging, assessing probable therapy escalation, predicting potential complications during therapy or significant clinical deterioration of patients, and avoiding admission which may not be necessary. Nevertheless, in order to confirm the obtained data, it is necessary to conduct large prospective studies that will address the potential diagnostic role of MR-proADM as a marker of COVID-19 severity.KEY MESSAGESSeverity of COVID-19 seems to be linked to MR-proADM levels and can be used as a potential marker for predicting a patient's clinical course.The use of MR-proADM might be beneficial in triaging, assessing probable therapy escalation, predicting potential complications during therapy or significant clinical deterioration of patients, and avoiding admission which may not be necessary.For patients with COVID-19, MR-proADM may be an excellent prognostic indicator because it is a marker of endothelial function that may predict the precise impact on the equilibrium between vascular relaxation and contraction and lowers platelet aggregation inhibitors, coagulation inhibitors, and fibrinolysis activators in favor of clotting factors.
Keyphrases
- coronavirus disease
- sars cov
- contrast enhanced
- end stage renal disease
- magnetic resonance
- systematic review
- newly diagnosed
- ejection fraction
- emergency department
- chronic kidney disease
- risk factors
- magnetic resonance imaging
- respiratory syndrome coronavirus
- patient reported outcomes
- case report
- clinical trial
- machine learning
- case control
- mesenchymal stem cells
- single molecule
- computed tomography
- deep learning
- smooth muscle