Early predictors and screening tool developing for severe patients with COVID-19

Fang, Le; Xie, Huashan; Ling-yun, Liu; Lu, Shaoying; Lv, Fangfang; Zhou, Jiancang; Xu, Yue; Ge, Huiqing; Yu, Min; Liu, Limin

doi:10.1186/s12879-021-06662-y

Cited by 6 publications

(2 citation statements)

References 25 publications

(36 reference statements)

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“…Although EWS has been used in the evaluation of COVID-19 patients ( Aygun and Eraybar, 2022 ; Fang et al, 2021 ; Huespe et al, 2021 ; Liang et al, 2020 ; Sarkar et al, 2022 ), there are clinical limitations to its use. For instance, the COVID-19 severity index ( Huespe et al, 2021 ) employs vital signs, laboratory parameters, and chest X-ray findings to predict the risk of ICU admission within the next 24 hr.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of COEWS (COVID-19 Early Warning Score) for hospitalized COVID-19 with laboratory features: A multicontinental retrospective study

Klén,

Huespe,

Gregalio

et al. 2023

eLife

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Background: The emergence of new SARS-CoV-2 variants with significant immune-evasiveness, the relaxation of measures for reducing the number of infections, the waning of immune protection (particularly in high-risk population groups), and the low uptake of new vaccine boosters, forecast new waves of hospitalizations and admission to intensive care units (ICUs). There is an urgent need for easily implementable and clinically effective early warning scores (EWSs) that can predict the risk of complications within the next 24 to 48 hours. Although EWSs have been used in the evaluation of COVID-19 patients, there are several clinical limitations to their use. Moreover, no models have been tested on geographically distinct populations or population groups with varying levels of immune protection.Methods: We developed and validated COEWS, an EWS that is automatically calculated solely from laboratory parameters that are widely available and affordable. We benchmarked COEWS against the widely used NEWS2. We also evaluated the predictive performance of vaccinated and unvaccinated patients.Results: The variables of the COEWS predictive model were selected based on their predictive coefficients and on the wide availability of these laboratory variables. The final model included complete blood count, blood glucose, and oxygen saturation features. To make COEWS more actionable in real clinical situations, we transformed the predictive coefficients of the COEWS model into individual scores for each selected feature. The global score serves as an easy-to-calculate measure indicating the risk of a patient developing the combined outcome of mechanical ventilation or death within the next 48 hours. The discrimination in the external validation cohort was 0.743 (95% confidence interval [CI]: 0.703-0.784) for the COEWS score performed with coefficients and 0.700 (95% CI: 0.654-0.745) for the COEWS performed with scores. The area under the receiver operating characteristic curve (AUROC) was similar in vaccinated and unvaccinated patients. Additionally, we observed that the AUROC of the NEWS2 was 0.677 (95% CI: 0.601-0.752) in vaccinated patients and 0.648 (95% CI: 0.608-0.689) in unvaccinated patients.Conclusions: The COEWS score predicts death or mechanical ventilation within the next 48 hours based on routine and widely available laboratory measurements. The extensive external validation, its high performance, its ease of use, and its positive benchmark in comparison with the widely used NEWS2 position COEWS as a new reference tool for assisting clinical decisions and improving patient care in the upcoming pandemic waves.Funding: University of Vienna.

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Section: Introductionmentioning

confidence: 99%

Development and validation of COEWS (COVID-19 Early Warning Score) for hospitalized COVID-19 with laboratory features: A multicontinental retrospective study

Klén,

Huespe,

Gregalio

et al. 2023

eLife

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“…Furthermore, previous reports suggest there is an association between COVID-19 and both of NLR and PLR. 14 An efficient immune response against SARS-CoV-2 is considered as necessary for the treatment of COVID-19, and more attention has been paid to the identification and development of predictive inflammation biomarkers for the resolution of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Hematological features and risk factors of hospitalized COVID-19 patients: A retrospective analysis

Hua

Yang

et al. 2022

Eur J Inflamm

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Background: Coronavirus disease 2019 (COVID-19) became pandemic in 2020 and recently, mutated coronaviruses have emerged in many countries. The aim of this study was to identify the clinical characteristics and risk factors for critical illness in hospitalized COVID-19 patients in Zhengzhou for clinical prevention and management. Materials and methods: A total of 70 patients hospitalized with COVID-19 were enrolled between 21 January and 29 February 2020, in Zhengzhou, China. Clinical characteristics, hematological findings, neutrophil lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), and inflammatory index on admission were obtained from medical records, COVID-19 patients with different outcomes were compared. Results: The median age was 55 years. Forty-three (61.0%) patients were classified as having severe or critical cases. Eighteen (25.7%) patients died in hospital and the remaining 52 were discharged. Patients who died tend to be old with expectoration and chronic obstructive pulmonary disease. Compared to survivor, non-survivor had significantly higher numbers of leucocytes and neutrophils, NLR, aspartate aminotransferase (AST), γ-glutamyl transpeptidase, total bilirubin, direct bilirubin, lactate dehydrogenase (LDH), prothrombin time, D-dimer, C-reactive protein, and decreased platelets, lymphocytes, uric acid, and albumin (ALB). Logistic regression analysis identified leucocytes, platelets, PLR, NLR, AST, and ALB as independent predictive factors for poor outcomes. The area under curve of the combination of leucocytes, PLR, NLR, and AST was 0.87, with a sensitivity of 0.83 and specificity of 0.81. Conclusion: Our results identified risk factors among COVID-19 patients for in-hospital mortality. Leucocytes, PLR, NLR, and AST could have important reference value for predicting prognosis, especially in low-resource countries.

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Sex-adjusted approach to baseline variables demonstrated some improved predictive capabilities for disease severity and survival in patients with Coronavirus Disease 19

Batmunkh

Ravjir

Lkhagvasuren

et al. 2022

Informatics in Medicine Unlocked

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Early predictors and screening tool developing for severe patients with COVID-19

Cited by 6 publications

References 25 publications

Development and validation of COEWS (COVID-19 Early Warning Score) for hospitalized COVID-19 with laboratory features: A multicontinental retrospective study

Development and validation of COEWS (COVID-19 Early Warning Score) for hospitalized COVID-19 with laboratory features: A multicontinental retrospective study

Hematological features and risk factors of hospitalized COVID-19 patients: A retrospective analysis

Sex-adjusted approach to baseline variables demonstrated some improved predictive capabilities for disease severity and survival in patients with Coronavirus Disease 19

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