Mid-regional Proadrenomedullin Biomarker Predicts Coronavirus Disease 2019 Clinical Outcomes: A US-Based Cohort Study

Natalie J Atallah1,2,3, Vahe S Panossian4, Christine J Atallah5

  • 1Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA.

Insights

Mid-regional proadrenomedullin (MR-proADM) effectively predicts severe COVID-19 outcomes, including mechanical ventilation or death. Elevated MR-proADM levels identify high-risk patients, aiding early intervention and improving clinical management strategies.

Area of Science:

  • Biomarkers
  • Infectious Diseases
  • Critical Care Medicine

Background:

  • Mid-regional proadrenomedullin (MR-proADM) is released upon endothelial damage and has shown potential in predicting coronavirus disease 2019 (COVID-19) outcomes.
  • Previous studies indicated a correlation between MR-proADM levels and COVID-19 prognosis.

Purpose of the Study:

  • To evaluate baseline MR-proADM as a predictor for a broad spectrum of clinical outcomes in hospitalized COVID-19 patients.
  • To compare the predictive performance of MR-proADM against other established biomarkers.

Main Methods:

  • Utilized data from the Boston Area COVID-19 Consortium (BACC) Bay Tocilizumab Trial.
  • Included patients with available biomarker data, excluding those admitted directly to the ICU.
  • Assessed an MR-proADM cutoff of 0.87 nmol/L for predicting clinical outcomes.

Main Results:

  • A total of 182 patients were analyzed; 11.0% experienced mechanical ventilation or death within 28 days.
  • Patients with MR-proADM >0.87 nmol/L had a 21.1% rate of mechanical ventilation or death, versus 4.5% for those with MR-proADM ≤0.87 nmol/L (P < .001).
  • MR-proADM >0.87 nmol/L independently predicted mechanical ventilation/death, ICU admission, prolonged hospitalization, and worse COVID-19 ordinal scale outcomes.

Conclusions:

  • MR-proADM serves as a valuable biomarker for early risk stratification in COVID-19 patients.
  • It aids in detecting severe disease progression and can outperform other common biomarkers in predicting mortality.
Abstract