Lactate-dehydrogenase associated with mortality in hospitalized patients with COVID-19 in Mexico: a multi-centre retrospective cohort study

Vidal-Cevallos, Paulina; Higuera-de-la-Tijera, Fátima; Chávez-Tapia, Norberto C.; Sanchez-Giron, Francisco; Cerda-Reyes, Eira; Rosales-Salyano, Víctor Hugo; Servín-Caamaño, A.; Vázquez-Medina, Martín Uriel; Méndez-Sánchez, Nahúm

doi:10.1016/j.aohep.2021.100338

Cited by 29 publications

(26 citation statements)

References 34 publications

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“…According to studies, LDH is the only best measure that may predict ICU admission or fatality situations. LDH increase up to sixfold in COVID-19 patients with severe disease related up to 16-fold increase in fatality [ 148 – 152 ]. Serum LDH levels in diabetic COVID-19 patients aged 70 or older also predicted fatality.…”

Section: Ldha: the Target Protein For Covid-19 Therapymentioning

confidence: 99%

The Lactate and the Lactate Dehydrogenase in Inflammatory Diseases and Major Risk Factors in COVID-19 Patients

Gupta

2022

Inflammation

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— Lactate dehydrogenase (LDH) is a terminating enzyme in the metabolic pathway of anaerobic glycolysis with end product of lactate from glucose. The lactate formation is crucial in the metabolism of glucose when oxygen is in inadequate supply. Lactate can also be formed and utilised by different cell types under fully aerobic conditions. Blood LDH is the marker enzyme, which predicts mortality in many conditions such as ARDS, serious COVID-19 and cancer patients. Lactate plays a critical role in normal physiology of humans including an energy source, a signaling molecule and a pH regulator. Depending on the pH, lactate exists as the protonated acidic form (lactic acid) at low pH or as sodium salt (sodium lactate) at basic pH. Lactate can affect the immune system and act as a signaling molecule, which can provide a “danger” signal for life. Several reports provide evidence that the serum lactate represents a chemical marker of severity of disease similar to LDH under inflammatory conditions. Since the mortality rate is much higher among COVID-19 patients, associated with high serum LDH, this article is aimed to review the LDH as a therapeutic target and lactate as potential marker for monitoring treatment response of inflammatory diseases. Finally, the review summarises various LDH inhibitors, which offer potential applications as therapeutic agents for inflammatory diseases, associated with high blood LDH. Both blood LDH and blood lactate are suggested as risk factors for the mortality of patients in serious inflammatory diseases.

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Section: Ldha: the Target Protein For Covid-19 Therapymentioning

confidence: 99%

The Lactate and the Lactate Dehydrogenase in Inflammatory Diseases and Major Risk Factors in COVID-19 Patients

Gupta

2022

Inflammation

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“…Inflammatory responses and thromboembolic events hinder microcirculatory function and compromise oxygen delivery, increasing the likelihood of gluconeogenesis and DNA metabolism. Higher serum LDH might, therefore, be used to indicate COVID-19 disease severity and predict mortality risk [ 105 , 106 , 107 ].…”

Section: Organ Damage Indicatormentioning

confidence: 99%

Biomarkers during COVID-19: Mechanisms of Change and Implications for Patient Outcomes

et al. 2022

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As the COVID-19 (Coronavirus disease 19) pandemic spreads worldwide, the massive numbers of COVID-19 patients have created a considerable healthcare burden for every country. The clinical spectrum of SARS-CoV-2 infection is broad, ranging from asymptomatic to mild, moderate, severe, and critical. Most COVID-19 patients present with no or mild symptoms, but nearly one-fifth of all patients develop severe or life-threatening complications. In addition to localized respiratory manifestations, severe COVID-19 cases also show extra-pulmonary complications or induce multiorgan failure. Identifying, triaging, and treating patients at risk early is essential and urgent. This article reviews the potential prognostic value of various biomarkers at different clinical spectrum stages of COVID-19 infection and includes information on fundamental prognostic mechanisms as well as potential clinical implications. Biomarkers are measurable biochemical substances used to recognize and indicate disease severity or response to therapeutic interventions. The information they provide is objective and suitable for delivering healthcare providers with a means of stratifying disease state in COVID-19 patients. This, in turn, can be used to help select and guide intervention efforts as well as gauge the efficacy of therapeutic approaches. Here, we review a number of potential biomarkers that may be used to guide treatment, monitor treatment efficacy, and form individualized therapeutic guidance based on patient response. Implementation of the COVID-19 biomarkers discussed here may lead to significantly improved quality of care and patient outcomes for those infected with SARS-CoV-2 worldwide.

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“…Since December 2019, the coronavirus disease 2019 (COVID-19) has been spreading aggressively worldwide, and the health systems have encountered an increasing shortage of critical care resources, such as personal protective equipment, intensive care unit (ICU) beds, and ventilators (1). So far, this virus has affected almost all countries, with more than two million deaths worldwide (2). Approximately 20% of COVID-19 patients need hospitalization, and 20%-30% of COVID-19 in-hospital patients are admitted to ICU (3).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Tools for the Prediction of ICU Admission in COVID-19 Hospitalized Patients

et al. 2022

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Background: The rapid coronavirus disease 2019 (COVID-19) outbreak has overwhelmed many healthcare systems worldwide and put them at the edge of collapsing. As the capacity of intensive care units (ICUs) is limited, deciding on the proper allocation of required resources is crucial. Objectives: This study aimed to create a machine learning (ML)-based predictive model of ICU admission among COVID-19 in-hospital patients at the initial presentation. Methods: This retrospective study was conducted on 1225 laboratory-confirmed COVID-19 hospitalized patients during January 9, 2020 - January 20, 2021. The top clinical parameters contributing to COVID-19 ICU admission were identified based on a correlation coefficient at P-value < 0.05. Next, the predictive models were constructed using five ML algorithms. Finally, to evaluate the performances of models, the metrics derived from the confusion matrix, classification error, and receiver operating characteristic were calculated. Results: Following feature selection, a total of 11 parameters were selected as the top predictors to build the prediction models. The results showed that the best performance belonged to the random forest (RF) algorithm with the mean accuracy of 99.5%, mean specificity of 99.7%, mean sensitivity of 99.4%, Kappa metric of 95.7%, and root mean squared error of 0.015. Conclusions: The ML algorithms, particularly RF, enable a reasonable level of accuracy and certainty in predicting disease progression and ICU admission for COVID-19 patients. The proposed models have the potential to inform frontline clinicians and health authorities with quantitative tools to assess illness severity and optimize resource allocation under time-sensitive and resource-constrained situations.

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Lactate-dehydrogenase associated with mortality in hospitalized patients with COVID-19 in Mexico: a multi-centre retrospective cohort study

Cited by 29 publications

References 34 publications

The Lactate and the Lactate Dehydrogenase in Inflammatory Diseases and Major Risk Factors in COVID-19 Patients

The Lactate and the Lactate Dehydrogenase in Inflammatory Diseases and Major Risk Factors in COVID-19 Patients

Biomarkers during COVID-19: Mechanisms of Change and Implications for Patient Outcomes

Comparison of Machine Learning Tools for the Prediction of ICU Admission in COVID-19 Hospitalized Patients

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