Dataset Information

Validation of two severity scores as predictors for outcome in Coronavirus Disease 2019 (COVID-19).

ABSTRACT:

Background

An established objective and standardized reporting of clinical severity and disease progression in COVID-19 is still not established. We validated and compared the usefulness of two classification systems reported earlier-a severity grading proposed by Siddiqi and a system from the National Australian COVID-19 guideline. Both had not been validated externally and were now tested for their ability to predict complications.

Methods

In this retrospective, single-centre observational study, patients hospitalized with confirmed COVID-19 across all severity stages were enrolled. The clinical severity was graded at admission and during hospitalization. Multivariate Cox regression was used to identify independent risk factors for mortality, a composite primary (mortality, incident acute respiratory distress syndrome, incident mechanical ventilation), a secondary endpoint (mortality, incident acute myocardial injury, incident venous thrombosis, pulmonary embolism or stroke) and progression of severity grades.

Results

Of 109 patients 17 died, 31 and 48 developed the primary and secondary endpoint, respectively. Worsening of the severity grade by at least one stage occurred in 27 and 28 patients, respectively. Siddiqi and Australian classification were identified as independent predictors for the primary endpoint (adjusted hazard ratio (aHR) 2.30, p<0.001 and aHR 2.08, p<0.001), for the secondary endpoint (aHR 2.12, p<0.001 and aHR 1.79, p<0.001) and mortality (aHR 2.30, p = 0.071 and aHR 1.98, p = 0.017). Both classification systems showed very good agreement regarding initial grading and good agreement regarding progression of severity stages.

Conclusions

Standardized and objective severity grading is useful to unequivocally stratify patients presenting with COVID-19 for their individual risk of complications.

SUBMITTER: Salbach C

PROVIDER: S-EPMC7895342 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Validation of two severity scores as predictors for outcome in Coronavirus Disease 2019 (COVID-19).

Salbach Christian C Mueller-Hennessen Matthias M Biener Moritz M Stoyanov Kiril M KM Vafaie Mehrshad M Preusch Michael R MR Kihm Lars P LP Merle Uta U Schnitzler Paul P Katus Hugo A HA Giannitsis Evangelos E

PloS one 20210219 2

<h4>Background</h4>An established objective and standardized reporting of clinical severity and disease progression in COVID-19 is still not established. We validated and compared the usefulness of two classification systems reported earlier-a severity grading proposed by Siddiqi and a system from the National Australian COVID-19 guideline. Both had not been validated externally and were now tested for their ability to predict complications.<h4>Methods</h4>In this retrospective, single-centre ob ...[more]

PMID: 33606842

Similar Datasets

Project description:BackgroundPrognostic factors for the Coronavirus disease 2019 (COVID1-9) are not well established. This study aimed to summarize the available data on the association between the severity of COVID-19 and common hematological, inflammatory and biochemical parameters.MethodsEMBASE, MEDLINE, Web of sciences were searched to identify all published studies providing relevant data. Random-effects meta-analysis was used to pool effect sizes.ResultsThe bibliographic search yielded 287 citations, 31 of which were finally retained. Meta-analysis of standardized mean difference (SMD) between severe and non-severe COVID-19 cases showed that CK-MB (SMD = 0.68,95%CI: 0.48;0.87; P-value:< 0.001), troponin I (SMD = 0.71, 95%CI:0.42;1.00; P-value:< 0.001), D-dimer (SMD = 0.54,95%CI:0.31;0.77; P-value:< 0.001), prothrombin time (SMD = 0.48, 95%CI:0.23;0.73; P-value: < 0.001), procalcitonin (SMD = 0.72, 95%CI: 0.34;1,11; P-value:< 0.001), interleukin-6 (SMD = 0.93, 95%CI: 0.25;1.61;P-value: 0.007),C-reactive protein (CRP) (SMD = 1.34, 95%CI:0.83;1.86; P-value:< 0.001), ALAT (SMD = 0.53, 95%CI: 0.34;0,71; P-value:< 0.001), ASAT (SMD = 0.96, 95%CI: 0.58;1.34; P-value: < 0.001), LDH (SMD = 1.36, 95%CI: 0.75;1.98; P-value:< 0.001), CK (SMD = 0.48, 95%CI: 0.10;0.87; P-value:0.01), total bilirubin (SMD = 0.32, 95%CI: 0.18;0.47;P-value: < 0.001), γ-GT (SMD = 1.03, 95%CI: 0.83;1.22; P-value: < 0.001), myoglobin (SMD = 1.14, 95%CI: 0.81;1.47; P-value:< 0.001), blood urea nitrogen (SMD = 0.32, 95%CI: 0.18;0.47;P-value:< 0.001) and Creatininemia (SMD = 0.18, 95%CI: 0.01;0.35; P-value:0.04) were significantly more elevated in severe cases, in opposition to lymphocyte count (SMD = -0.57, 95%CI:-0.71; - 0.42; P-value: < 0.001) and proportion of lymphocytes (SMD = -0.81, 95%CI: - 1.12; - 0.49; P-value:< 0.001) which were found to be significantly lower in severe patients with other biomarker such as thrombocytes (SMD = -0.26, 95%CI: - 0.48; - 0.04; P-value:0.02), eosinophils (SMD = - 0.28, 95%CI:-0.50; - 0.06; P-value:0.01), haemoglobin (SMD = -0.20, 95%CI: - 0.37,-0.03; P-value:0.02), albuminemia (SMD-1.67,95%CI -2.40; - 0.94; P-value:< 0.001), which were also lower. Furthermore, severe COVID-19 cases had a higher risk to have lymphopenia (RR =1.66, 95%CI: 1.26;2.20; P-value:0.002), thrombocytopenia (RR = 1.86, 95%CI: 1.59;2.17; P-value: < 0.001), elevated procalcitonin level (RR = 2.94, 95%CI: 2.09-4.15; P-value:< 0.001), CRP (RR =1.41,95%CI: 1.17-1.70; P-value:0.003), ASAT(RR =2.27, 95%CI: 1.76;2.94; P-value:< 0.001), CK(RR = 2.61, 95%CI: 1.35;5.05; P-value: 0.01), Creatininemia (RR = 3.66, 95%CI: 1.53;8.81; P-value: 0.02) and LDH blood level (RR = 2.03, 95%CI: 1.42;290; P-value: 0.003).ConclusionSome inflammatory (procalcitonin, CRP), haematologic (lymphocyte, Thrombocytes), and biochemical (CK-MB, Troponin I, D-dimer, ASAT, ALAT, LDH, γ-GT) biomarkers are significantly associated with severe COVID-19. These biomarkers might help in prognostic risk stratification of patients with COVID-19.

Dataset Information

Validation of two severity scores as predictors for outcome in Coronavirus Disease 2019 (COVID-19).

Background

Methods

Results

Conclusions

Publications

Validation of two severity scores as predictors for outcome in Coronavirus Disease 2019 (COVID-19).

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure