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Acute Lung Injury Biomarkers in the Prediction of COVID-19 Severity: Total Thiol, Ferritin and Lactate Dehydrogenase.

ABSTRACT: SARS-CoV-2 (COVID-19) patients who develop acute respiratory distress syndrome (ARDS) can suffer acute lung injury, or even death. Early identification of severe disease is essential in order to control COVID-19 and improve prognosis. Oxidative stress (OS) appears to play an important role in COVID-19 pathogenesis; we therefore conceived a study of the potential discriminative ability of serum biomarkers in patients with ARDS and those with mild to moderate disease (non-ARDS). 60 subjects were enrolled in a single-centre, prospective cohort study of consecutively admitted patients: 29 ARDS/31 non-ARDS. Blood samples were drawn and marker levels analysed by spectrophotometry and immunoassay techniques. C-reactive protein (CRP), lactate dehydrogenase (LDH), and ferritin were significantly higher in ARDS versus non-ARDS cases at hospital admission. Leukocytes, LDH, ferritin, interleukin 6 (IL-6) and tumour necrosis factor alpha (TNF-α) were also significantly elevated in ARDS compared to non-ARDS patients during the hospital stay. Total thiol (TT) was found to be significantly lower in ARDS. Conversely, D-dimer, matrix metalloproteinase-9 (MMP-9) and advanced glycosylated end products (AGE) were elevated. Leukocytes, LDH, CRP, ferritin and IL-6 were found to be significantly higher in non-survivors. However, lymphocyte, tumour necrosis factor beta (TGF-β), and TT were lower. In summary, our results support the potential value of TT, ferritin and LDH as prognostic biomarkers for ARDS development in COVID-19 patients, distinguishing non-ARDS from ARDS (AUCs = 0.92; 0.91; 0.89) in a fast and cost-effective manner. These oxidative/inflammatory parameters appear to play an important role in COVID-19 monitoring and can be used in the clinical management of patients.

SUBMITTER: Martinez Mesa A

PROVIDER: S-EPMC8388961 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Multiple single biomarkers have been associated with poor outcomes in acute lung injury; however, no single biomarker has sufficient discriminating power to clearly indicate prognosis. Using both derivation and replication cohorts, we tested novel risk reclassification methods to determine whether measurement of multiple plasma biomarkers at the time of acute lung injury diagnosis would improve mortality prediction in acute lung injury.Analysis of plasma biomarker levels and prospectively collected clinical data from patients enrolled in two randomized controlled trials of ventilator therapy for acute lung injury.Intensive care units of university hospitals participating in the National Institutes of Health Acute Respiratory Distress Syndrome Network.Subjects enrolled in a trial of lower tidal volume ventilation (derivation cohort) and subjects enrolled in a trial of higher vs. lower positive end-expiratory pressure (replication cohort).None.The plasma biomarkers were intercellular adhesion molecule-1, von Willebrand factor, interleukin-8, soluble tumor necrosis factor receptor-1, and surfactant protein-D. In the derivation cohort (n = 547), adding data on these biomarkers to clinical predictors (Acute Physiology and Chronic Health Evaluation III score) at the time of study enrollment improved the accuracy of risk prediction, as reflected by a net reclassification improvement of 22% (95% confidence interval 13% to 32%; p < .001). In the replication cohort (n = 500), the net reclassification improvement was 17% (95% confidence interval 7% to 26%; p < .001). A reduced set of three biomarkers (interleukin-8, soluble tumor necrosis factor receptor-1, and surfactant protein-D) had nearly equivalent prognostic value in both cohorts.When combined with clinical data, plasma biomarkers measured at the onset of acute lung injury can improve the accuracy of risk prediction. Combining three or more biomarkers may be useful for selecting a high-risk acute lung injury population for enrollment in clinical trials of novel therapies.

Project description:AimsThe risk of HeartMate II (HMII) left ventricular assist device (LVAD) thrombosis has been reported, and serum lactate dehydrogenase (LDH), a biomarker of haemolysis, increases secondary to LVAD thrombosis. This study evaluated longitudinal measurements of LDH post-LVAD implantation, hypothesizing that LDH trends could timely predict future LVAD thrombosis.Methods and resultsFrom October 2004 to October 2014, 350 HMIIs were implanted in 323 patients at Cleveland Clinic. Of these, patients on 339 HMIIs had at least one post-implant LDH value (7996 total measurements). A two-step joint model combining longitudinal biomarker data and pump thrombosis events was generated to assess the effect of changing LDH on thrombosis risk. Device-specific LDH trends were first smoothed using multivariate boosted trees, and then used as a time-varying covariate function in a multiphase hazard model to analyse time to thrombosis. Pre-implant variables associated with time-varying LDH values post-implant using boostmtree were also investigated. Standardized variable importance for each variable was estimated as the difference between model-based prediction error of LDH when the variable was randomly permuted and prediction error without permuting the values. The larger this difference, the more important a variable is for predicting the trajectory of post-implant LDH. Thirty-five HMIIs (10%) had either confirmed (18) or suspected (17) thrombosis, with 15 (43%) occurring within 3 months of implant. LDH was associated with thrombosis occurring both early and late after implant (P < 0.0001 for both hazard phases). The model demonstrated increased probability of HMII thrombosis as LDH trended upward, with steep changes in LDH trajectory paralleling trajectories in probability of pump thrombosis. The most important baseline variables predictive of the longitudinal pattern of LDH were higher bilirubin, higher pre-implant LDH, and older age. The effect of some pre-implant variables such as sodium on the post-implant LDH longitudinal pattern differed across time.ConclusionsLongitudinal trends in surveillance LDH for patients on HMII support are useful for dynamic prediction of pump thrombosis, both early after implant and late. Incorporating upward and downward trends in LDH that dynamically update a model of LVAD thrombosis risk provides a useful tool for clinical management and decisions.

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Acute Lung Injury Biomarkers in the Prediction of COVID-19 Severity: Total Thiol, Ferritin and Lactate Dehydrogenase.

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