Identification of COVID-19 patients at risk of hospital admission and mortality: a European multicentre retrospective analysis of mid-regional pro-adrenomedullin

Abstract

Background: Mid-Regional pro-Adrenomedullin (MR-proADM) is an inflammatory biomarker that improves the prognostic assessment of patients with sepsis, septic shock and organ failure. Previous studies of MR-proADM have primarily focussed on bacterial infections. A limited number of small and monocentric studies have examined MR-proADM as a prognostic factor in patients infected with SARS-CoV-2, however there is need for multicenter validation. An evaluation of its utility in predicting need for hospitalisation in viral infections was also performed.

Methods: An observational retrospective analysis of 1861 patients, with SARS-CoV-2 confirmed by RT-qPCR, from 10 hospitals across Europe was performed. Biomarkers, taken upon presentation to Emergency Departments (ED), clinical scores, patient demographics and outcomes were collected. Multiclass random forest classifier models were generated as well as calculation of area under the curve analysis. The primary endpoint was hospital admission with and without death.

Results: Patients suitable for safe discharge from Emergency Departments could be identified through an MR-proADM value of ≤ 1.02 nmol/L in combination with a CRP (C-Reactive Protein) of ≤ 20.2 mg/L and age ≤ 64, or in combination with a SOFA (Sequential Organ Failure Assessment) score < 2 if MR-proADM was ≤ 0.83 nmol/L regardless of age. Those at an increased risk of mortality could be identified upon presentation to secondary care with an MR-proADM value of > 0.85 nmol/L, in combination with a SOFA score ≥ 2 and LDH > 720 U/L, or in combination with a CRP > 29.26 mg/L and age ≤ 64, when MR-proADM was > 1.02 nmol/L.

Conclusions: This international study suggests that for patients presenting to the ED with confirmed SARS-CoV-2 infection, MR-proADM in combination with age and CRP or with the patient's SOFA score could identify patients at low risk where outpatient treatment may be safe.

Keywords: Emergency department; Hospital admission; MR-proADM; Mortality; SARS-CoV-2.

Fig. 1

Importance ranking of predictors for the developed multiclass random forest classifier

Fig. 2

Conditional decision tree developed to explain the predictive performance of the multiclass random forest classifier

Fig. 3

A ROC curve for admission avoidance, where clinical scores were not considered. B ROC curve for mortality, where clinical scores were not considered

Fig. 4

Importance ranking of predictors for the developed multiclass random forest classifier

Fig. 5

Conditional decision tree developed to explain the predictive performance of the multiclass random forest classifier

Fig. 6

A ROC curve for admission avoidance in the subgroup where clinical scores were additionally considered. B ROC curve for mortality in the subgroup where clinical scores were additionally considered

Fig. 7

Proposed workflows for managing COVID-19 patients based on results of conditional decision trees. Values presented are rounded for ease of future clinical implementation. Workflows presented are for safe admission avoidance (actual values were: CRP ≤ 20.2 mg/L, MR-proADM ≤ 1.02 nmol/L) and for identifying those at increased risk of mortality (actual values were: CRP > 29.26 mg/L, MR-proADM > 1.02 nmol/L)