Novel Systemic Inflammation Markers to Predict COVID-19 Prognosis.
Frontiers in immunology. 2021;:741061
Coronavirus disease 2019 (COVID-19) has resulted in a global pandemic, challenging both the medical and scientific community for the development of novel vaccines and a greater understanding of the effects of the SARS-CoV-2 virus. COVID-19 has been associated with a pronounced and out-of-control inflammatory response. Studies have sought to understand the effects of inflammatory response markers to prognosticate the disease. Herein, we aimed to review the evidence of 11 groups of systemic inflammatory markers for risk-stratifying patients and prognosticating outcomes related to COVID-19. Numerous studies have demonstrated the effectiveness of neutrophil to lymphocyte ratio (NLR) in prognosticating patient outcomes, including but not limited to severe disease, hospitalization, intensive care unit (ICU) admission, intubation, and death. A few markers outperformed NLR in predicting outcomes, including 1) systemic immune-inflammation index (SII), 2) prognostic nutritional index (PNI), 3) C-reactive protein (CRP) to albumin ratio (CAR) and high-sensitivity CAR (hsCAR), and 4) CRP to prealbumin ratio (CPAR) and high-sensitivity CPAR (hsCPAR). However, there are a limited number of studies comparing NLR with these markers, and such conclusions require larger validation studies. Overall, the evidence suggests that most of the studied markers are able to predict COVID-19 prognosis, however NLR seems to be the most robust marker.
Combined lymphocyte/monocyte count, D-dimer and iron status predict COVID-19 course and outcome in a long-term care facility.
Journal of translational medicine. 2021;(1):79
BACKGROUND The Sars-CoV-2 can cause severe pneumonia with multiorgan disease; thus, the identification of clinical and laboratory predictors of the progression towards severe and fatal forms of this illness is needed. Here, we retrospectively evaluated and integrated laboratory parameters of 45 elderly subjects from a long-term care facility with Sars-CoV-2 outbreak and spread, to identify potential common patterns of systemic response able to better stratify patients' clinical course and outcome. METHODS Baseline white blood cells, granulocytes', lymphocytes', and platelets' counts, hemoglobin, total iron, ferritin, D-dimer, and interleukin-6 concentration were used to generate a principal component analysis. Statistical analysis was performed by using R statistical package version 4.0. RESULTS We identified 3 laboratory patterns of response, renamed as low-risk, intermediate-risk, and high-risk, strongly associated with patients' survival (p < 0.01). D-dimer, iron status, lymphocyte/monocyte count represented the main markers discriminating high- and low-risk groups. Patients belonging to the high-risk group presented a significantly longer time to ferritin decrease (p: 0.047). Iron-to-ferritin-ratio (IFR) significantly segregated recovered and dead patients in the intermediate-risk group (p: 0.012). CONCLUSIONS Our data suggest that a combination of few laboratory parameters, i.e. iron status, D-dimer and lymphocyte/monocyte count at admission and during the hospital stay, can predict clinical progression in COVID-19.
Iron metabolism and lymphocyte characterisation during Covid-19 infection in ICU patients: an observational cohort study.
World journal of emergency surgery : WJES. 2020;(1):41
BACKGROUND Iron metabolism and immune response to SARS-CoV-2 have not been described yet in intensive care patients, although they are likely involved in Covid-19 pathogenesis. METHODS We performed an observational study during the peak of pandemic in our intensive care unit, dosing D-dimer, C-reactive protein, troponin T, lactate dehydrogenase, ferritin, serum iron, transferrin, transferrin saturation, transferrin soluble receptor, lymphocyte count and NK, CD3, CD4, CD8 and B subgroups of 31 patients during the first 2 weeks of their ICU stay. Correlation with mortality and severity at the time of admission was tested with the Spearman coefficient and Mann-Whitney test. Trends over time were tested with the Kruskal-Wallis analysis. RESULTS Lymphopenia is severe and constant, with a nadir on day 2 of ICU stay (median 0.555 109/L; interquartile range (IQR) 0.450 109/L); all lymphocytic subgroups are dramatically reduced in critically ill patients, while CD4/CD8 ratio remains normal. Neither ferritin nor lymphocyte count follows significant trends in ICU patients. Transferrin saturation is extremely reduced at ICU admission (median 9%; IQR 7%), then significantly increases at days 3 to 6 (median 33%, IQR 26.5%, p value 0.026). The same trend is observed with serum iron levels (median 25.5 μg/L, IQR 69 μg/L at admission; median 73 μg/L, IQR 56 μg/L on days 3 to 6) without reaching statistical significance. Hyperferritinemia is constant during intensive care stay: however, its dosage might be helpful in individuating patients developing haemophagocytic lymphohistiocytosis. D-dimer is elevated and progressively increases from admission (median 1319 μg/L; IQR 1285 μg/L) to days 3 to 6 (median 6820 μg/L; IQR 6619 μg/L), despite not reaching significant results. We describe trends of all the abovementioned parameters during ICU stay. CONCLUSIONS The description of iron metabolism and lymphocyte count in Covid-19 patients admitted to the intensive care unit provided with this paper might allow a wider understanding of SARS-CoV-2 pathophysiology.