Lactate dehydrogenase elevations is associated with severity of COVID-19: a meta-analysis

Chen, Xiaoyun; Huang, Mingyao; Xiao, Zheng-Wei; Yang, Sheng; Chen, Xiangqi

doi:10.1186/s13054-020-03161-5

Cited by 28 publications

(28 citation statements)

References 6 publications

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“…Values of LDH were elevated for all three clinical outcomes, a finding consistent with widespread tissue damage that has been shown in numerous studies to be a predictor of morbidity and mortality in a wide variety of diseases beyond COVID-19 [13][14][15][16][17] . Mortality was also predicted for by BUN.…”

Section: Discussionsupporting

confidence: 72%

Predictive modeling of morbidity and mortality in COVID-19 hospitalized patients and its clinical implications

Wang

Liu

Chen

et al. 2020

Preprint

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ObjectiveRetrospective study of COVID-19 positive patients treated at NYU Langone Health (NYULH) to identify clinical markers predictive of disease severity to assist in clinical decision triage and provide additional biological insights into disease progression.Materials and MethodsClinical activity of 3740 de-identified patients at NYULH between January and August 2020. Models were trained on clinical data during different parts of their hospital stay to predict three clinical outcomes: deceased, ventilated, or admitted to ICU.ResultsXGBoost model trained on clinical data from the final 24 hours excelled at predicting mortality (AUC=0.92, specificity=86% and sensitivity=85%). Respiration rate was the most important feature, followed by SpO2 and age 75+. Performance of this model to predict the deceased outcome extended 5 days prior with AUC=0.81, specificity=70%, sensitivity=75%. When only using clinical data from the first 24 hours, AUCs of 0.79, 0.80, and 0.77 were obtained for deceased, ventilated, or ICU admitted, respectively. Although respiration rate and SpO2 levels offered the highest feature importance, other canonical markers including diabetic history, age and temperature offered minimal gain. When lab values were incorporated, prediction of mortality benefited the most from blood urea nitrogen (BUN) and lactate dehydrogenase (LDH). Features predictive of morbidity included LDH, calcium, glucose, and C-reactive protein (CRP).ConclusionTogether this work summarizes efforts to systematically examine the importance of a wide range of features across different endpoint outcomes and at different hospitalization time points.BACKGROUND AND SIGNIFICANCEThe first cluster of SARS-CoV-2 was reported in Wuhan, Hubei Province on December 31, 2019. Inciting symptoms remarkably similar to pneumonia, the disease quickly traveled around the world, earning its pandemic status by the World Health Organization on March 11, 2020. Although the first wave has since passed for hardest-hit regions such as New York City (NYC) and most of Asia, a resurgence of cases has already been reported in Europe and record new cases tallied in the Midwest and rural United States (US). As of November 12th, the US alone logged its highest tally to date with a 317% growth over the preceding 30 days1. The coronavirus disease (COVID-19) is far from seeing the end of its days and there remains a compelling need to prioritize care and resources for patients at elevated risk of morbidity and mortality.Previous work building machine learning models used patient data from Tongji Hospital2,3 (Wuhan, China), Zhongnan Hospital4 (Wuhan China), Mount Sinai Hospital5 (NYC, US), and NYU Family Health Center6 (NYC, US). Surprisingly, clinical features selected varied widely across studies. For example, while McRae et al.’s 2-tiered model6 trained on 701 NYC patients to predict mortality was based on actual age, C-reactive protein (CRP), procalcitonin, and D-dimer, Yan et al.’s model2 trained on 485 patients from Wuhan selected lactate dehydrogenase (LDH), lymphocyte count, and CRP as the most predictive for mortality. Variations in selected features differed greatly even when trained to predict similar outcomes on data from patients of the same city. Yao et al.’s model3 was trained on 137 patients from Wuhan and relied on 28 biomarkers in their final model to predict morbidity. Given the differences among prior models, some of which were driven by domain-specific knowledge, we decided to systematically examine the importance of a wide range of features across different endpoint outcomes and at different hospitalization time points.This study analyzes retrospective PCR-confirmed COVID-19 inpatient data collected at NYU Langone Hospital spanning 1/1/2020 to 8/7/2020 to predict three sets of clinical outcomes: alive vs deceased, ventilated vs not ventilated, or ICU admitted vs not ICU admitted. The clinical information of 3740 patient encounters included demographic data (age, sex, insurance, past diagnosis of diabetes, presence of cardiovascular comorbidities), vital signs (SpO2, pulse, respiration rate, temperature, blood pressure), and the 50 most frequently ordered lab tests in our dataset. Models were developed using two methods: logistic regression with feature selection using Least Absolute Shrinkage and Selection Operator7 (LASSO) and gradient tree boosting with XGBoost8. An explainable algorithm, such as logistic regression, provides easy to interpret insights into the features of importance. Conversely, the larger model capacity of XGBoost better handles data complexities to explore the extent that predictive performance can be optimized. Together, these methods ensure a holistic survey that explores the clinical underpinnings of disease etiology and the prospects of building models that are sufficiently competent to be effective decision support tools.

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Section: Discussionsupporting

confidence: 72%

Predictive modeling of morbidity and mortality in COVID-19 hospitalized patients and its clinical implications

Wang

Liu

Chen

et al. 2020

Preprint

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“…Procalcitonin and blood leucocytes have both been previously described as predictive of adverse disease courses and mortality in COVID-19 and indicate an exaggerated inflammatory response. 37 D-dimer and lactate dehydrogenase, additional established risk factors of severe COVID-19 courses, 38 , 39 , 40 were not independently associated with severe AKI, although they were predictive in simplified models. A noteworthy finding is that we did not observe severe AKI outside the ICU setting, confirming the close association of AKI with the overall COVID-19 disease severity and arguing against an isolated development of severe AKI in COVID-19 patients who are not critically ill. Our data are consistent with Hirsch et al.…”

Section: Discussionmentioning

confidence: 87%

Critical Illness and Systemic Inflammation Are Key Risk Factors of Severe Acute Kidney Injury in Patients With COVID-19

Hardenberg

Stockmann

Aigner

et al. 2021

Kidney International Reports

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Introduction Acute kidney injury (AKI) is an important complication in COVID-19, but its precise etiology has not fully been elucidated. Insights into AKI mechanisms may be provided by analyzing the temporal associations of clinical parameters reflecting disease processes and AKI development. Methods We performed an observational cohort study of 223 consecutive COVID-19 patients treated at 3 sites of a tertiary care referral center to describe the evolvement of severe AKI (Kidney Disease: Improving Global Outcomes stage 3) and identify conditions promoting its development. Descriptive statistics and explanatory multivariable Cox regression modeling with clinical parameters as time-varying covariates were used to identify risk factors of severe AKI. Results Severe AKI developed in 70 of 223 patients (31%) with COVID-19, of which 95.7% required kidney replacement therapy. Patients with severe AKI were older, predominantly male, had more comorbidities, and displayed excess mortality. Severe AKI occurred exclusively in intensive care unit patients, and 97.3% of the patients developing severe AKI had respiratory failure. Mechanical ventilation, vasopressor therapy, and inflammatory markers (serum procalcitonin levels and leucocyte count) were independent time-varying risk factors of severe AKI. Increasing inflammatory markers displayed a close temporal association with the development of severe AKI. Sensitivity analysis on risk factors of AKI stage 2 and 3 combined confirmed these findings. Conclusion Severe AKI in COVID-19 was tightly coupled with critical illness and systemic inflammation and was not observed in milder disease courses. These findings suggest that traditional systemic AKI mechanisms rather than kidney-specific processes contribute to severe AKI in COVID-19.

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“…According to these ndings, MDW may represent a novel in ammatory marker, readily exploitable in the clinical-laboratory monitoring of COVID-19 patients, along with cytokines, acute-phase reactants and other components of humoral innate immunity [19,[22][23][24]. When compared with these classical in ammatory markers (plasma proteins), MDW basically differs in being a cell-based (monocyte) cytometric parameter.…”

Section: Discussionmentioning

confidence: 99%

Monocyte Distribution Width (MDW) as novel inflammatory marker with prognostic significance in COVID-19 patients

Riva

Castellano

Nasillo

et al. 2021

Preprint

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Background: Monocyte Distribution Width (MDW), a new cytometric parameter correlating with cytomorphologic changes occurring during massive monocyte activation, has recently been described as promising early biomarker of sepsis. Similar to sepsis, in SARS-CoV-2-associated disease (COVID-19) monocyte/macrophage subsets are considered key mediators of the life-threatening hyper-inflammatory disorder –commonly defined as ‘cytokine storm’– which is part of the complex infection-associated immune dysregulation observed in severe COVID-19 cases (possibly constituting a kind of viral sepsis). Therefore, in this work, we aimed at investigating, for the first time, possible roles of MDW testing in the monitoring of COVID-19 patients.Methods: We longitudinally measured MDW values (readily available along with automated blood cell count) in a cohort of 87 patients with COVID-19 diagnosis, consecutively admitted to our clinics in early 2020, due to aggravation of their clinical status. Statistical analyses were then applied to correlate MDW values with inflammatory markers, disease severity, clinical trajectories and final outcome.Results: We initially found significant direct correlations between MDW and different inflammatory markers routinely assessed during hospitalization, namely CRP (p<0.001), fibrinogen (p<0.001) and ferritin (p<0.01). Moreover, high MDW values resulted remarkably associated with fatal outcome of severe COVID-19 patients (AUC=0.76, 95% CI: 0.66-0.87, sensitivity 0.75, specificity 0.70, MDW threshold 26.4; RR=4.91, 95% CI: 1.73-13.96; OR=7.14, 95% CI: 2.06-24.71). Furthermore, when evaluating MDW dynamics in COVID-19 cases with longer follow-up, we frequently observed progressive MDW increment in patients with worsening inflammation, while clinical recovery was consistently associated with MDW decrease. Of note, MDW evaluation may also help to assess the response to immunomodulatory treatments, such as tocilizumab. Conclusions: Our pilot study shows that MDW can be useful in the monitoring of COVID-19 patients, as this innovative hematologic biomarker is (i) easy and rapid to obtain, (ii) directly related to the activation state of a fundamental inflammatory cell subset (i.e. monocytes, pivotal in both cytokine storm and sepsis immunopathogenesis), (iii) well correlated with clinical severity of COVID-19-associated inflammatory disorder, and, in turn, (iv) endowed with relevant prognostic significance. Additional studies are needed to define further the clinical impact of MDW testing in the management of COVID-19 patients.

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Lactate dehydrogenase elevations is associated with severity of COVID-19: a meta-analysis

Cited by 28 publications

References 6 publications

Predictive modeling of morbidity and mortality in COVID-19 hospitalized patients and its clinical implications

Predictive modeling of morbidity and mortality in COVID-19 hospitalized patients and its clinical implications

Critical Illness and Systemic Inflammation Are Key Risk Factors of Severe Acute Kidney Injury in Patients With COVID-19

Monocyte Distribution Width (MDW) as novel inflammatory marker with prognostic significance in COVID-19 patients

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