A COVID-19 specific multiparametric and ECG-based score for the prediction of in-hospital mortality: ELCOVID score
摘要
We aimed to develop and validate a COVID-19 specific scoring system, also including some ECG features, to predict all-cause in-hospital mortality at admission. Patients were retrieved from the ELCOVID study (ClinicalTrials.gov identifier: NCT04367129), a prospective, multicenter Italian study enrolling COVID-19 patients between May to September 2020. For the model validation, we randomly selected two-thirds of participants to create a derivation dataset and we used the remaining one-third of participants as the validation set. Over the study period, 1014 hospitalized COVID-19 patients (mean age 74 years, 61% males) met the inclusion criteria and were included in this analysis. During a median follow-up of 12 (IQR 7–22) days, 359 (35%) patients died. Age (HR 2.25 [95%CI 1.72–2.94], p < 0.001), delirium (HR 2.03 [2.14–3.61], p = 0.012), platelets (HR 0.91 [0.83–0.98], p = 0.018), D-dimer level (HR 1.18 [1.01–1.31], p = 0.002), signs of right ventricular strain (RVS) (HR 1.47 [1.02–2.13], p = 0.039) and ECG signs of previous myocardial necrosis (HR 2.28 [1.23–4.21], p = 0.009) were independently associated to in-hospital all-cause mortality. The derived risk-scoring system, namely EL COVID score, showed a moderate discriminatory capacity and good calibration. A cut-off score of ≥ 4 had a sensitivity of 78.4% and 65.2% specificity in predicting all-cause in-hospital mortality. ELCOVID score represents a valid, reliable, sensitive, and inexpensive scoring system that can be used for the prognostication of COVID-19 patients at admission and may allow the earlier identification of patients having a higher mortality risk who may be benefit from more aggressive treatments and closer monitoring.