key: cord-0967590-j2wpvgu0
authors: ASADUZZAMAN, MD; BHUIA, MOHAMMAD ROMEL; ALAM, Z H M NAZMUL; BARI, ZABED JILLUL; FERDOUSI, TASNIM
title: Role of hemogram-derived ratios in predicting ICU requirements in COVID-19 patients: A multicenter study
date: 2022-04-29
journal: IJID Regions
DOI: 10.1016/j.ijregi.2022.04.011
sha: 2ef7b44fc9dd2078eab6e46f264eb9bd5b465c63
doc_id: 967590
cord_uid: j2wpvgu0

Purpose :As hyperinflammation is said to be the driver of severe COVID-19 disease, checking markers of inflammation is getting more attention. The aim of this study was to evaluate the utility of cost-effective hemogram-derived ratios in the prediction of ICU requirements in COVID-19 patients. Methods :This multicenter retrospective study included hospitalized COVID-19 patients from four COVID-19 dedicated hospitals in Sylhet, Bangladesh. Data on demographics, clinical characteristics, laboratory parameters, and survival outcomes were analyzed. Logistic regression analysis was done to identify the significance of each hemogram-derived ratio in predicting ICU requirements. Results :Of included patients (n=442), 98 (22.17%) required ICU admission. At the time of admission, patients requiring ICU had higher neutrophil count (8.86 vs. 5.7 × 109/L; p <.001) and lower lymphocyte and platelet counts (1.16 vs. 1.48 × 109/L; p = 0.006 and 220 vs. 230 × 109/L; p = 0.449 respectively) than patients not requiring ICU. Peripheral capillary oxygen saturation (SpO2) at admission were significantly lower (82.7 ± 11.4 vs. 92.0 ± 7.0; <.001) in those who subsequently required ICU admission. A significantly higher level of neutrophil-to-lymphocyte ratio (NLR), derived neutrophil-to-lymphocyte ratio (d-NLR), neutrophil-to-platelet ratio (NPR), platelet-to-lymphocyte ratio (PLR) and systemic immune-inflammation index (SII). NLR (OR 1.80; 95% CI 1.28 to 2.53), d-NLR (OR 1.91; 95% CI 1.30 to 2.80), NPR (OR 2.09; 95% CI 1.32 to 3.31), and SII (OR 1.38; 95% CI 1.06 to 1.80) were found to be significant predictors of ICU requirements. Conclusion :Hemogram-derived ratios can be an effective tool in facilitating the early categorization of at-risk patients and thereby enabling timely measures to be taken early in the disease course.

The ongoing COVID-19 pandemic poses a major global threat to population health and a huge strain on the health care delivery system worldwide (Legido-Quigley et al., 2020). Even before the COVID19 pandemic, healthcare systems in low-and middle-income countries (LMICs) faced considerable challenges in providing high-quality, affordable and universally accessible care While Coronavirus can inflict direct damage to epithelial tissues through epithelial cell injury and necrosis, evidence indicates that immune system activation/perturbation is the major cause of organ/tissue damage in COVID-19 (Xu, Z et al., 2020) . The activation of multiple complement pathways, dysregulated neutrophil responses, endothelial injury, and hypercoagulability appear to be interlinked with SARS-CoV2 infection and instead serve to drive the severity of the disease [Java, A et al., 2020] . It is clear that hyper inflammation and coagulopathy contribute to disease severity and death in patients infected with SARS-CoV-2 (Merad, M and Martin, J., 2020). 5 High levels of inflammatory markers, including C-reactive protein (CRP), ferritin and Ddimer and increased levels of inflammatory cytokines and chemokines (Herold, T et The rapid spread and potential lethality of the virus generated an urgent need to identify indicators that could be used to predict the severity and risk associated with infection. These predictors can help to identify patients at high risk of developing severe disease, and thus better allocate limited human and technical resources during the ongoing pandemic and prevent unnecessary hospitalization, as well as mitigate other impacts.

Biomarkers of inflammation derived from the peripheral blood, such as white blood cell (WBC) count, neutrophil (NEU)-to-lymphocyte (LYM) ratio (NLR), platelet-to-lymphocyte ratio (PLR), and serum C reactive protein (CRP) levels have been investigated as independent predictors for prognosis of systematic inflammatory diseases (Guthrie, G et This approach can be a cost-effective one in a low-resource country. In this study, we aimed to investigate the role of different hematological ratios in predicting subsequent requirements of ICU.

This was a cross-sectional study involving those COVID-19 patients, visiting four hospitals of Sylhet city, Bangladesh from October 2020 to January 2021. The diagnosis of COVID-19 was done by a specialist based on PCR or CT scan report or suggestive clinical features. The data on clinical characteristics, results of the laboratory tests of the enrolled patients and outcome data were collected from hospital records. The inclusion criteria were as follows: age of 18 years old or older, a diagnosis of COVID-19 and needed hospitalization. The exclusion criteria included those younger than 18 years, pregnant patients and patients who lacked required data. The final analysis was done on 442 patients.

All patients were assessed at the Emergency Department where the blood sample was drawn.

Laboratory assessments consisted of complete blood count (including white blood cell count, leukocyte subtypes, hemoglobin count and platelet count) and biochemical parameters (RBS, S. ferritin, D-dimer).

Count of white blood cells (×10 9 cells/L), neutrophil (× 10 9 cells/L), lymphocytes (× 10 9 cells/L) and platelets (× 10 11 cells/L) were used to define the hemogram-derived ratios, NLR is the ratio between neutrophil and lymphocytes, d-NLR is derived NLR and calculated as: d-NLR = 7 ANC/(WBC-ANC), where ANC is the absolute neutrophil Count , NPR is the ratio between neutrophil and platelets, PLR is the ratio between platelets and lymphocytes, and finally, SII is defined as neutrophil multiplied by platelets and divided by lymphocytes.

The outcome variable was the requirement of ICU (Yes or No); a binary variable. Clinical data included were age, sex, clinical features, presence of co-morbidities like hypertension, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), Diabetes mellitus (DM), ischemic heart disease (IHD) and cerebrovascular accident (CVA), Peripheral capillary oxygen saturation (SpO2) at admission and length of hospital stay (in days). Laboratory parameters included complete blood count (CBC), D-dimer, S. Ferritin and random blood sugar (RBS). The radiographic findings included chest CT scan reports.

We used descriptive statistics to describe the data. Shapiro-Wilk test was used to assess the normality of continuous variables. We presented continuous measurements by mean and standard deviation (SD) for data that followed normal distribution, and by median and interquartile range (IQR) for data that were skewed. The mean difference between two groups (ICU vs. non-ICU) in a continuous variable was assessed using two independent sample mean test (t-test) for the normally distributed data and using non-parametric Mann-Whitney U test for the non-normally distributed data. Categorical variables were presented using frequencies and percentages (%). Chi-Square test (χ 2 test) of independence was used to determine the association 8 (difference) among categorical variables. Differences in the hemogram-derived ratios due to comorbidities were investigated using multivariate analysis of variance (MANOVA) test.

Multiple Logistic regression models were used to identify the predictors of ICU requirement.

The candidate predictors for the adjusted model were selected based on clinical relevance.

Initially, simple logistic regression models were fitted for each of the candidate predictors. The variables that were highly correlated or associated with each other were excluded from the model due to multicollinearity. Model A included Age, DM, CKD, COPD. Models B-D included previous model and RBS, D-dimer, ferritin and admission SpO2 respectively. Each hemogram derived ratio was added separately to each model and their significance tested. Model findings were presented using odds ratio (OR) and 95% confidence interval (CI). A P value <0.05 was considered statistically significant. We used ROC curve to detect optimal cut-off values of the hematological ratios in predicting ICU admission. The effects of comorbidities on the hemogram-derived ratios and ICU admission were determined using the multiple linear regression models and multiple logistic regression model respectively. Data analysis was performed using R software. This study is reported following the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) (von Elm, E et al., 2008) statements.

The final analysis included 442 patients. Clinical characteristics of the patients are summarized (overall and by ICU requirement status) and shown in Table 1 . ICU admission was required in 98 (22.17%) patients. 55 (12.44%) patients ended up with in-hospital death, among which 4 (1.2%) from non-ICU group and 51 (52%) from ICU group (p = <.001). Mean age of study 9 patients was 60.2 ± 13.7 years. Mean age in ICU group was higher than non-ICU group (65.3 ± 14.9 vs. 58.8 ± 13.1 years). Male comprises two-third of the study sample (65.8% vs. 34.2%).

Patients requiring ICU have significantly lower SpO2 at admission than those not requiring ICU (82.7± 11.4 vs. 92.0 ± 7.0; p <.001). Length of hospital stay was higher in ICU requiring patients (p = <.001).

Regarding laboratory results ( Table 2) Difference in hemogram-derived ratio between ICU and non-ICU requiring group are shown in Figure 1 ).

The result of multivariable regression models assessing the relation of different hemogramderived ratios and ICU requirements are shown in Table 4 .

Model A adjusted the hemogram-derived ratio for Age, DM, CKD and COPD. The subsequent model additionally adjusted for RBS, D-dimer, ferritin and admission SpO2 respectively. Except for PLR, all other ratios remain as a significant predictor for ICU requirements in all models.

We analyzed the optimal cut-off values of NLR, d-NLR, NPR, PLR and SII, calculated by the ROC analysis and presented in (Figure: Table A1 in the Appendix. The Pillai's trace test statistic demonstrated that the values of the hemogram-derived ratios significantly varied due to IHD, COPD and CVA.

The effects of comorbidities on the hemogram-derived ratios and ICU admission are presented in Table 6 . The results of multiple linear regression models showed that IHD had significant effects on higher level of NLR (p< .001), d-NLR (p< .001) and SII (p< .001); COPD had significant 11 effects on higher level of NLR (p = 0.02), d-NLR (p< .001) and NPR (p< .001); and CVA had significant effects on higher level of PLR (p< .001) only. Furthermore, the multiple logistic regression model revealed that IHD and COPD were significant associated with ICU admission.

Patients with IHD and COPD were 3.1 times (p< .001) and 2.1 times (p = 0.03) more likely to require ICU admission respectively than the patient who did not have these conditions.

This study described clinical characteristics and laboratory parameters of hospitalized COVID-19 patients and investigated the role of hemogram-derived ratios in predicting ICU requirements.

Compared to patients who did not require ICU, the ICU requiring patients were of higher age and having higher co-morbidities like hypertension, CKD, IHD, COPD, DM and CVA. Patients with IHD and COPD were 3.1 times and 2.1 times more likely to require ICU admission respectively than the patient who did not have these conditions. SpO2 measured at admission was significantly lower in patients who subsequently required ICU admission. The death rate was significantly higher in ICU requiring patients. Adjusted multivariable models revealed that NLR, d-NLR, NPR and SII were significant predictors of ICU requirement.

As evolving clinical and pathologic observation found hyper inflammation and immunothrombosis are key pathogenic mechanisms in cell injury in COVID-19, addressing this process is a must in the management of COVID-19. Current researches are mainly targeting to combat these processes. Risk-stratifying the patients at the earliest can allow effective intervention to be applied at the outset which may substantially improve the outcome. The findings of the present study will be a great tool in this regard. At the same time, these tools are within the reach of all hospital, even at peripheral center.

However, this study has got some limitations. This study did not take into account the effect of other inflammatory markers like CRP, LDH, procalcitonin, troponin, IL-6 because it is a retrospective study, and these measures were not available in the dataset. As a result, their effects on disease course may be underestimated here. We used the only admission laboratory parameters, while did not evaluate the dynamic change of these biomarkers.

The current COVID-19 pandemic has turned out to be a global nightmare. The global health 

With unforeseen and unprecedented challenges to the global health system imposed by COVID- 19 , there has been an urgent need to find out a strategy to mitigate the loss of human lives with the existing health care resources. As COVID-19 is still an evolving disease, research directed at identifying at-risk people at the time of their hospital admission using easily available and lowcost parameters is of paramount importance. We mainly focused on simple and cost-effective investigation. We studied several hemogram-derived parameters and analyzed their ability to predict ICU admission. This finding can guide policymakers and clinicians to risk-stratify the patients at admission. We suggest multinational studies be done to check the validity of these predictors on different races and different geographic areas. Future studies should also 16 investigate the dynamic changes of these markers in the clinical course of the disease. Our study findings can help policy-makers to adopt appropriate strategies which are likely to address the atrisk patient better and substantially decrease the health expenditure.

This present study investigated the efficacy of inflammatory markers in predicting ICU requirements in COVID-19 patients. These markers are easily measurable, widely available at low cost and can be calculated easily from the routinely measured blood test. This study found a higher value of NLR, d-NLR, NPR, SII and lower values of SII are significant predictors of subsequent ICU requirement of COVID-19 patients. We suggest using these markers on admission for triaging patients at high risk of developing severe disease and requiring ICU and appropriately allocating available resources to them.

This research received no external funding.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

The datasets analyzed during the current study are not publicly available because of having no permission of the hospitals from where data were collected.

MA contributed to data acquisition, conception and design of the study, data analysis, data interpretation and manuscript writing. MRB contributed to all the stages of development of this paper including data analysis and report writing. ZJB, NA and TF contributed to the acquisition of data, conception and design of the study and revising the manuscript critically. All authors have approved the submitted. Dr. Md. Shafiqul Bari and Dr. M M Jahangir Alam who have inspired us to do this work. Our deep respect and endless gratitude to those patients whose data we have worked with.

 The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Area Under Abbreviations: NLR, neutrophil-to-lymphocyte ratio; d-NLR, derived neutrophil-to-lymphocyte ratio; NPR, neutrophil-to-platelet ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immune-inflammation index 

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Hospital, Sylhet, Bangladesh from where we collected data. We acknowledge the contributions