Absolute Lymphocytes, Ferritin, C-Reactive Protein, and Lactate Dehydrogenase Predict Early Invasive Ventilation in Patients With COVID-19

Payán-Pernía, Salvador; Pérez, Lucía; Sevilla, A.; Gil, J.M.; Novelli, Silvana

doi:10.1093/labmed/lmaa105

Cited by 36 publications

(38 citation statements)

References 13 publications

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“…Indeed, abnormal LDH is commonly seen in idiopathic lung fibrosis [ 34 ]. During the course of COVID-19, higher levels of LDH were observed during both alveolitis and fibrosis stages [ 35 ], highlighting it as a candidate for predicting the need for invasive ventilation [ 36 ] and mortality. In addition, a robust immune response to SARS-CoV-2 infection and subsequent cytokine storm could cause multi-organ damage, which leads to further elevation of LDH levels [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

A machine learning based exploration of COVID-19 mortality risk

et al. 2021

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Early prediction of patient mortality risks during a pandemic can decrease mortality by assuring efficient resource allocation and treatment planning. This study aimed to develop and compare prognosis prediction machine learning models based on invasive laboratory and noninvasive clinical and demographic data from patients’ day of admission. Three Support Vector Machine (SVM) models were developed and compared using invasive, non-invasive, and both groups. The results suggested that non-invasive features could provide mortality predictions that are similar to the invasive and roughly on par with the joint model. Feature inspection results from SVM-RFE and sparsity analysis displayed that, compared with the invasive model, the non-invasive model can provide better performances with a fewer number of features, pointing to the presence of high predictive information contents in several non-invasive features, including SPO2, age, and cardiovascular disorders. Furthermore, while the invasive model was able to provide better mortality predictions for the imminent future, non-invasive features displayed better performance for more distant expiration intervals. Early mortality prediction using non-invasive models can give us insights as to where and with whom to intervene. Combined with novel technologies, such as wireless wearable devices, these models can create powerful frameworks for various medical assignments and patient triage.

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Section: Discussionmentioning

confidence: 99%

A machine learning based exploration of COVID-19 mortality risk

et al. 2021

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“…Elevated serum LDH level is present in many diseases including COVID-19. Compared to other laboratory biomarkers including CRP, lymphocytes, and AST for their predictive value in COVID-19 disease severity and clinical outcome, LDH was found to have higher accuracy and greater area under the curve (AUC) [9][10][11].…”

Section: Discussionmentioning

confidence: 92%

“…Coronavirus disease 2019 is highly infectious with higher fatality rate than other respiratory tract viral infectious diseases. Many biomarkers are currently investigated for their prognostic role in COVID-19 patients' disease severity and mortality [9][10][11].…”

Section: Discussionmentioning

confidence: 99%

“…Lactate dehydrogenase (LDH) is one of the biomarkers under investigation for its role in prediction of COVID-19 patients' prognosis. Compared to other prognostic laboratory biomarkers for COVID-19 disease severity including CRP, lymphocytes, and AST, LDH was found to have higher accuracy and greater area under the curve (AUC) for predicting COVID-19 disease severity [9][10][11].…”

Section: Introductionmentioning

confidence: 96%

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Comparative evaluation of semi-quantitative CT-severity scoring versus serum lactate dehydrogenase as prognostic biomarkers for disease severity and clinical outcome of COVID-19 patients

Magdy

Saad

Khateeb

et al. 2021

Egypt J Radiol Nucl Med

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Background Coronavirus disease 2019 pandemic causes significant strain on healthcare infrastructure and medical resources. So, it becomes crucial to identify reliable predictor biomarkers for COVID-19 disease severity and short term mortality. Many biomarkers are currently investigated for their prognostic role in COVID-19 patients. Our study is retrospective and aims to evaluate role of semi-quantitative CT-severity scoring versus LDH as prognostic biomarkers for COVID-19 disease severity and short-term clinical outcome. Results Two hundred sixty-six patients between April 2020 and November 2020 with positive RT-PCR results underwent non-enhanced CT scan chest in our hospital and were retrospectively evaluated for CT severity scoring and serum LDH level measurement. Data were correlated with clinical disease severity. CT severity score and LDH were significantly higher in severe and critical cases compared to mild cases (P value < 0.001). High predictive significance of CT severity score for COVID-19 disease course noted, with cut-off value ≥ 13 highly predictive of severe disease (96.96% accuracy); cut-off value ≥ 16 highly predictive of critical disease (94.21% accuracy); and cut-off value ≥ 19 highly predictive of short-term mortality (92.56% accuracy). CT severity score has higher sensitivity, specificity, positive, and negative predictive values as well as overall accuracy compared to LDH level in predicting severe, critical cases, and short-term mortality. Conclusion Semi-quantitative CT severity scoring has high predictive significance for COVID-19 disease severity and short-term mortality with higher sensitivity, specificity, and overall accuracy compared to LDH. Our study strongly supports the use of CT severity scoring as a powerful prognostic biomarker for COVID-19 disease severity and short-term clinical outcome to allow triage of need for hospital admission, earlier medical interference, and to effectively prioritize medical resources for cases with high mortality risk for better decision making and clinical outcome.

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“…Systemic Inflammation : Laboratory based parameters indicating inflammation in the serum, such as D-Dimer and Ferritin, have been shown to lead to a reduction in blood oxygen saturation levels, reflecting inadequate oxygenation in the lungs [35, 36].…”

Section: Methodsmentioning

confidence: 99%

Predicting the severity of disease progression in COVID-19 at the individual and population level: A mathematical model

Chirmule¹,

Nair

Desai

et al. 2021

Preprint

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The impact of COVID-19 disease on health and economy has been global, and the magnitude of devastation is unparalleled in modern history. Any potential course of action to manage this complex disease requires the systematic and efficient analysis of data that can delineate the underlying pathogenesis. We have developed a mathematical model of disease progression to predict the clinical outcome, utilizing a set of causal factors known to contribute to COVID-19 pathology such as age, comorbidities, and certain viral and immunological parameters. Viral load and selected indicators of a dysfunctional immune response, such as cytokines IL-6 and IFNab; which contribute to the cytokine storm and fever, parameters of inflammation d-dimer and ferritin, aberrations in lymphocyte number, lymphopenia, and neutralizing antibodies were included for the analysis. The model provides a framework to unravel the multi-factorial complexities of the immune response manifested in SARS-CoV-2 infected individuals. Further, this model can be valuable to predict clinical outcome at an individual level and to develop strategies for allocating appropriate resources to mitigate severe cases at a population level.

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Absolute Lymphocytes, Ferritin, C-Reactive Protein, and Lactate Dehydrogenase Predict Early Invasive Ventilation in Patients With COVID-19

Cited by 36 publications

References 13 publications

A machine learning based exploration of COVID-19 mortality risk

A machine learning based exploration of COVID-19 mortality risk

Comparative evaluation of semi-quantitative CT-severity scoring versus serum lactate dehydrogenase as prognostic biomarkers for disease severity and clinical outcome of COVID-19 patients

Predicting the severity of disease progression in COVID-19 at the individual and population level: A mathematical model

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