Comorbidities Influence the Predictive Power of Hematological Markers for Mortality in Hospitalized COVID-19 Patients

Abstract

Introduction: Coronavirus disease 2019 (COVID-19) pandemic has caused unprecedented mortality and has stretched the health infrastructure thin worldwide, especially in low- and middle-income countries. There is a need to evaluate easily available biomarkers for their clinical relevance for poor outcomes in severe cases of COVID-19. It is also known that comorbidities affect these biomarkers with or without COVID-19. We aimed to unearth the influence of comorbidities on feasible hematological predictive markers for mortality in hospitalized severe COVID-19 patients. Materials and Methods: This is a retrospective study done on severe COVID-19 hospitalized patients, diagnosed with RT polymerase chain reaction (n = 205), were investigated. Comorbidities associated with the patients were tracked and scored according to Charlson comorbidity index (CCI). CCI score of zero was grouped in A, those with CCI score 1–4 into group B and those with CCI scores ≥ 5 into group C. Correlation between hematological parameters and CCI scores was analyzed using Pearson correlation coefficient. Optimal cut-off and odds ratio was derived from receiver operating characteristic (ROC) curve analysis. Results: Among the 205 severe COVID-19 patients age, C-reactive protein (CRP), neutrophil lymphocyte ratio (NLR), derived NLR (dNLR), absolute neutrophil count (ANC) and total leukocyte count (TLC) were found to be statistically significant independent risk factors for predicting COVID-19 mortality (p < 0.01). In group A, cut off for CRP was 51.5 mg/L (odds ratio [OR]: 26.7; area under curve [AUC]: 0.867), TLC was 11,850 cells/mm³ (OR: 11.7; AUC: 0.731), NLR was 11.76 (OR: 14.3; AUC: 0.756), dNLR was 5.77 (OR: 4.89; AUC: 0.659), ANC was 13,110 cells/mm³ (OR: 1.68; AUC: 0.553). In group B, cut off for CRP was 36.5 mg/L (OR: 32.1; AUC: 0.886), TLC was 11,077 cells/mm³ (OR: 12.1; AUC: 0.722), NLR was 8.27 (OR: 18.9; AUC: 0.827), dNLR was 3.79 (OR: 9.26; AUC: 0.727), ANC was 11,420 cells/mm³ (OR: 2.42; AUC: 0.564). In group C, cut-off for CRP was 23.7 mg/L (OR: 32.7; AUC: 0.904), TLC was 10,480 cells/mm³ (OR: 21.2; AUC: 0.651), NLR was 6.29 (OR: 23.5; AUC: 0.647), dNLR was 1.93 (OR: 20.8; AUC: 0.698), ANC was 6650 cells/mm³ (OR: 2.45; AUC: 0.564). Conclusions: In severe COVID-19 patients, CRP was the most reliable biomarker to predict mortality followed by NLR. Presence, type, and number of co-morbidities influence the levels of the biomarkers and the clinically relevant cut-offs associated with mortality.

Correlation Between Biomarkers and Age-Adjusted Charlson Comorbidity Index in Patients With COVID-19: A Cross-Sectional Study in a Tertiary Care Center in South India

“…These findings indicate that the number of comorbidities and age significantly influence the serum levels of biomarkers in patients with COVID-19. This is consistent with the findings of Parthasarathy et al who showed a higher impairment of laboratory determinants in the group with more number of comorbidities [ 16 ]. They also reported a similar positive correlation between the laboratory parameters and the CCI score.…”

“…The parameters taken into account in the CoLACD score combine lymphocyte percentage, age, CCI, and the presence of dyspnea. A study conducted in Mysore, India explored the effect of the presence of multiple comorbidities on laboratory cut-offs of biomarkers [ 16 ]. For this, the study categorized patients according to CCI and compared both the biomarker values as well as the cut-offs between the different categories.…”

“…The parameters taken into account in the CoLACD score combine lymphocyte percentage, age, CCI, and the presence of dyspnea. A study conducted in Mysore, India explored the effect of the presence of multiple comorbidities on laboratory cut-offs of biomarkers [16].…”

Serological surveillance reveals a high exposure to SARS-CoV-2 and altered immune response among COVID-19 unvaccinated Cameroonian individuals

“…To be noted, the association between antibody seropositivity and CD4+, IFN-γ or IL-6 was modulated by patients’ characteristics. Such modulating impact have been previously reported for immune response immune in India [ 56 ], and haematological biomarkers of COVID-19 in Cameroon and India [ 57 , 58 ]. This is not surprising as these characteristics are well known to be predictors of SARS-CoV-2 infection, severity, or deaths [ 59 – 61 ].…”

Association of Serum Albumin and Copeptin with Early Clinical Deterioration and Instability in Community-Acquired Pneumonia

“…Biomarkers are biological markers that can be measured accurately and reliably reproduced. It has been used extensively for various disease conditions [ 15 , 16 ]. The most commonly used biomarkers in CAP are C-reactive protein (CRP) and procalcitonin (PCT).…”