Assessment of serum ferritin as a biomarker in COVID-19: bystander or participant? Insights by comparison with other infectious and non-infectious diseases

Biomarkers in COVID-19: An Up-To-Date Review

The ongoing pandemic of coronavirus disease 2019 (COVID-19) poses several challenges to clinicians. Timely diagnosis and hospitalization, risk stratification, effective utilization of intensive care services, selection of appropriate therapies, monitoring and timely discharge are essential to save the maximum number of lives. Clinical assessment is indispensable, but laboratory markers, or biomarkers, can provide additional, objective information which can significantly impact these components of patient care. COVID-19 is not a localized respiratory infection but a multisystem disease caused by a diffuse systemic process involving a complex interplay of the immunological, inflammatory and coagulative cascades. The understanding of what the virus does to the body and how the body reacts to it has uncovered a gamut of potential biomarkers. This review discusses the different classes of biomarkers - immunological, inflammatory, coagulation, hematological, cardiac, biochemical and miscellaneous - in terms of their pathophysiological basis followed by the current evidence. Differences between children and adults are highlighted. The role of biomarkers in the diagnosis and management of Multisystem Inflammatory Syndrome in Children (MIS-C) is reviewed. The correlation of biomarkers with clinical and radiological features and the viral load, temporal evolution and the effect of treatment remain to be studied in detail. Which biomarker needs to be evaluated when and in whom, and how best this information can contribute to patient care are questions which currently lack convincing answers. With the evidence currently available broad guidelines on the rational use of available biomarkers are presented. Integrating clinical and laboratory data, monitoring trends rather than a single value, correlating with the natural course of the disease and tailoring guidelines to the individual patient and healthcare setting are essential.

Diagnostic Utility of a Ferritin-to-Procalcitonin Ratio to Differentiate Patients With COVID-19 From Those With Bacterial Pneumonia: A Multicenter Study

Background

There is an urgent need for accurate, rapid, inexpensive biomarkers that can differentiate coronavirus disease 2019 (COVID-19) from bacterial pneumonia. We assess the role of the ferritin-to-procalcitonin (F/P) ratio to classify pneumonia cases into those due to COVID-19 vs those due to bacterial pathogens.

Methods

This multicenter case–control study compared patients with COVID-19 with those with bacterial pneumonia, admitted between March 1 and May 31, 2020. Patients with COVID-19 and bacterial pneumonia co-infection were excluded. The F/P in patients with COVID-19 vs with bacterial pneumonia were compared. Receiver operating characteristic curve analysis determined the sensitivity and specificity of various cutoff F/P values for COVID-19 vs bacterial pneumonia.

Results

A total of 242 COVID-19 pneumonia cases and 34 bacterial pneumonia controls were included. Patients with COVID-19 pneumonia had a lower mean age (57.1 vs 64.4 years; P = .02) and a higher body mass index (30.74 vs 27.15 kg/m²; P = .02) compared with patients with bacterial pneumonia. Cases and controls had a similar proportion of women (47% vs 53%; P = .5), and COVID-19 patients had a higher prevalence of diabetes mellitus (32.6% vs 12%; P = .01). The median F/P was significantly higher in patients with COVID-19 (4037.5) compared with the F/P in bacterial pneumonia (802; P < .001). An F/P ≥877, used to diagnose COVID-19, resulted in a sensitivity of 85% and a specificity of 56%, with a positive predictive value of 93.2% and a likelihood ratio of 1.92. In multivariable analyses, an F/P ≥877 was associated with greater odds of identifying a COVID-19 case (odds ratio, 11.27; 95% CI, 4–31.2; P < .001).

Conclusions

An F/P ≥877 increases the likelihood of COVID-19 pneumonia compared with bacterial pneumonia.

Ferritin is associated with the severity of lung involvement but not with worse prognosis in patients with COVID-19: data from two Italian COVID-19 units

The coronavirus 2019 disease (COVID-19) is characterised by a heterogeneous clinical presentation, a complex pathophysiology and a wide range of imaging findings, depending on disease severity and time course. We conducted a retrospective evaluation of hospitalized patients with proven SARS-CoV-2 infection, clinical signs of COVID-19 and computed tomography (CT) scan-proven pulmonary involvement, in order to identify relationships between clinical, serological, imaging data and disease outcomes in patients with COVID-19. Clinical and serological records of patients admitted to two COVID-19 Units of the Abruzzo region in Italy with proven SARS-CoV-2 pulmonary involvement investigated with CT scan, assessed at the time of admission to the hospital, were retrospectively evaluated. Sixty-one patients (22 females and 39 males) of median age 65 years were enrolled. Fifty-six patients were discharged while death occurred in 5 patients. None of the lung abnormalities detected by CT was different between discharged and deceased patients. No differences were observed in the features and extent of pulmonary involvement according to age and gender. Logistic regression analysis with age and gender as covariates demonstrated that ferritin levels over the 25th percentile were associated with the involvement of all 5 pulmonary lobes (OR = 14.5, 95% CI 2.3–90.9, p = 0.004), the presence of septal thickening (OR = 8.2, 95% CI 1.6–40.9, p = 0.011) and the presence of mediastinal lymph node enlargement (OR = 12.0, 95% CI 1.1–127.5, p = 0.039) independently of age and gender. We demonstrated that ferritin levels over the 25th percentile are associated with a more severe pulmonary involvement, independently of age and gender and not associated with disease outcomes. The identification of reliable biomarkers in patients with COVID-19 may help guiding clinical decision, tailoring therapeutic approaches and ultimately improving the care and prognosis of patients with this disease.

Prediction of COVID-19 severity using laboratory findings on admission: informative values, thresholds, ML model performance

BACKGROUND

Despite the necessity, there is no reliable biomarker to predict disease severity and prognosis of patients with COVID-19. The currently published prediction models are not fully applicable to clinical use.

OBJECTIVES

To identify predictive biomarkers of COVID-19 severity and to justify their threshold values for the stratification of the risk of deterioration that would require transferring to the intensive care unit (ICU).

METHODS

The study cohort (560 subjects) included all consecutive patients admitted to Dubai Mediclinic Parkview Hospital from February to May 2020 with COVID-19 confirmed by the PCR. The challenge of finding the cut-off thresholds was the unbalanced dataset (eg, the disproportion in the number of 72 patients admitted to ICU vs 488 non-severe cases). Therefore, we customised supervised machine learning (ML) algorithm in terms of threshold value used to predict worsening.

RESULTS

With the default thresholds returned by the ML estimator, the performance of the models was low. It was improved by setting the cut-off level to the 25th percentile for lymphocyte count and the 75th percentile for other features. The study justified the following threshold values of the laboratory tests done on admission: lymphocyte count <2.59×10⁹/L, and the upper levels for total bilirubin 11.9 μmol/L, alanine aminotransferase 43 U/L, aspartate aminotransferase 32 U/L, D-dimer 0.7 mg/L, activated partial thromboplastin time (aPTT) 39.9 s, creatine kinase 247 U/L, C reactive protein (CRP) 14.3 mg/L, lactate dehydrogenase 246 U/L, troponin 0.037 ng/mL, ferritin 498 ng/mL and fibrinogen 446 mg/dL.

CONCLUSION

The performance of the neural network trained with top valuable tests (aPTT, CRP and fibrinogen) is admissible (area under the curve (AUC) 0.86; 95% CI 0.486 to 0.884; p<0.001) and comparable with the model trained with all the tests (AUC 0.90; 95% CI 0.812 to 0.902; p<0.001). Free online tool at https://med-predict.com illustrates the study results.

Elevated serum ferritin level effectively discriminates severity illness and liver injury of coronavirus disease 2019 pneumonia

Aim

Ferritin is a hepatic protein that plays vital roles in diagnosing and predicting diseases, but its potential in coronavirus disease 2019 (COVID-19) remains unknown.

Method

We collected clinical records from 79 COVID-19 patients at Wuhan Union hospital (China). Spearman’s correlation analysis, receiver operating characteristic (ROC) curve and Kaplan–Meier survival curves were employed.

Results

Patients with elevated ferritin levels had a higher incidence of severity illness (50.0 vs 2.9%) and liver injury (52.3 vs 20.0%) when compared with patients with normal ferritin levels (p < 0.05). Ferritin could effectively identify the severity of illness (ROC area 0.873) and liver injury (ROC area 0.752). The elevated ferritin group showed longer viral clearance time (median 16 vs 6 days, p < 0.001) and in-hospital length (median 18 vs 10 days, p < 0.001).

Conclusions

It suggests that ferritin could act as an easy-to-use tool to identify liver injury and severity illness and predict the prognosis of COVID-19 patients. Intensive surveillance is necessary for patients with abnormal ferritin levels.

When Does the Cytokine Storm Begin in COVID-19 Patients? A Quick Score to Recognize It

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that is responsible for coronavirus disease 2019 (COVID-19), which has rapidly spread across the world, becoming a pandemic. The "cytokine storm" (CS) in COVID-19 leads to the worst stage of illness, and its timely control through immunomodulators, corticosteroids, and cytokine antagonists may be the key to reducing mortality. After reviewing published studies, we proposed a Cytokine Storm Score (CSs) to identify patients who were in this hyperinflammation state, and at risk of progression and poorer outcomes. We retrospectively analyzed 31 patients admitted to Infectious Disease Department in "St. Maria" Hospital in Terni with confirmed SARS-CoV-2 infections, and analyzed the "CS score" (CSs) and the severity of COVID-19. Then we conducted a prospective study of COVID-19 patients admitted after the definition of the CSscore. This is the first study that proposes and applies a new score to quickly identify COVID-19 patients who are in a hyperinflammation stage, to rapidly treat them in order to reduce the risk of intubation. CSs can accurately identify COVID-19 patients in the early stages of a CS, to conduct timely, safe, and effect administration of immunomodulators, corticosteroids, and cytokine antagonists, to prevent progression and reduce mortality.