Annals of African Medicine
Home About AAM Editorial board Ahead of print Current Issue Archives Instructions Subscribe Contact us Search Login 


 
Table of Contents
ORIGINAL ARTICLE
Year : 2023  |  Volume : 22  |  Issue : 2  |  Page : 204-212  

Haematological indices and coagulation profile as predictors of disease severity and associations with clinical outcome among COVID-19 patients in Lagos, Nigeria


1 Department of Medicine, College of Medicine University of Lagos; Department of Medicine, Lagos University Teaching Hospital, Idi-Araba, Nigeria
2 Department of Haematology and Blood Transfusion, Lagos University Teaching Hospital, Idi-Araba, Nigeria
3 Department of Internal Medicine, Montefiore Medical Centre, Wakefield Campus, Bronx, NY, USA
4 Department of Medicine, Lagos University Teaching Hospital, Idi-Araba, Nigeria
5 Department of Community Medicine and Primary Care, College of Medicine University of Lagos, Lagos, Nigeria

Date of Submission25-Jul-2022
Date of Decision28-Nov-2022
Date of Acceptance06-Jan-2023
Date of Web Publication4-Apr-2023

Correspondence Address:
Olufunto Olufela Kalejaiye
Department of Medicine, College of Medicine University of Lagos and Lagos University Teaching Hospital, Idi-Araba, Lagos
Nigeria
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/aam.aam_111_22

Rights and Permissions
   Abstract 


Background: This study aims to evaluate the use of haematological indices and coagulation profiles as possible low-cost predictors of disease severity and their associations with clinical outcomes in COVID-19-hospitalized patients in Nigeria. Materials and Methods: We carried out a hospital-based descriptive 3-month observational longitudinal study of 58 COVID-19-positive adult patients admitted at the Lagos University Teaching Hospital, Lagos, Nigeria. We used a structured questionnaire to obtain the participants' relevant sociodemographic and clinical data, including disease severity. Basic haematologic indices, their derivatives, and coagulation profile were obtained from patients' blood samples. Receiver Operating Characteristic (ROC) analysis was used to compare these laboratory-based values with disease severity. A P < 0.05 was considered statistically significant. Results: The mean age of the patients was 54.4 ± 14.8 years. More than half of the participants were males (55.2%, n = 32) and most had at least one comorbidity (79.3%, n = 46). Significantly higher absolute neutrophil count (ANC), neutrophil–lymphocyte ratio (NLR), systemic immune-inflammation index (SII), lower absolute lymphocyte count (ALC) and lymphocyte–monocyte ratio (LMR) were associated with severe disease (P < 0.05). Patients' hemoglobin concentration (P = 0.04), packed cell volume (P < 0.001), and mean cell hemoglobin concentration (P = 0.03) were also significantly associated with outcome. Receiver operating characteristic (ROC) analysis of disease severity was significant for the ANC, ALC, NLR, LMR, and SII. The coagulation profile did not show any significant associations with disease severity and outcomes in this study. Conclusion: Our findings identified haematological indices as possible low-cost predictors of disease severity in COVID-19 in Nigeria.

   Abstract in French 

Résumé
Contexte: Cette étude avait pour objectif d'évaluer l'utilité des indices hématologiques et profils de coagulation comme prédicteurs à faible coût de la sévérité de la maladie et leurs associations avec les résultats cliniques chez les patients hospitalisés pour COVID-19 au Nigéria. Méthodes: Nous avons mené une étude longitudinale observationnelle descriptive pendant 3 mois portant sur 58 patients adultes positifs au COVID-19, admis à Lagos University Teaching Hospital, Lagos, Nigéria. Un questionnaire structuré a été établit pour obtenir les données sociodémographiques et cliniques pertinentes des participants, y compris les données sur la sévérité de la maladie. Les indices hématologiques de base, leurs dérivés, et le profil de coagulation ont été obtenus à partir d'échantillons de sang de patients. La courbe caractéristique opérante du récepteur (ROC) a été utilisée pour comparer ces indices biologiques avec la sévérité de la maladie. Une valeur de P < 0.05 a été considéré statistiquement significatif. Résultats: L'âge moyen des patients était 54.4 ± 14.8 ans. Plus de la moitié des participants étaient des hommes (55.2 %, n = 32), et la majorité des participants présentaient au moins une comorbidité (79.3 %, n = 46). Un nombre absolu de neutrophiles (CNA), un rapport neutrophiles-lymphocytes (NLR), et une indice d'inflammation immunitaire systémique (SII) significativement élevé, et un nombre absolu de lymphocytes (ALC) et un rapport lymphocyte-monocytes (LMR) bas étaient associés à un maladie sévère (P < 0.05). La taux d'hémoglobine des patients (P = 0.04), l'hématocrite (P < 0.001), et concentration moyenne d'hémoglobine cellulaire (P = 0.03) étaient également significativement associés avec la sévérité de la maladie. L'analyse ROC de la gravité de la maladie était significative pour le ANC, ALC, NLR, LMR, et SII. Le profil de coagulation n'a montré aucune association significative avec la gravité de la maladie dans cette étude. Conclusion: Nos résultats ont identifié les indices hématologiques comme des prédicteurs potentielle à faible coût de la sévérité du COVID-19 au Nigeria.
Mots-clés: Profil de coagulation, COVID-19, indices hématologiques, Nigéria, prédicteur

Keywords: Coagulation profile, COVID-19, haematological indices, Nigeria, predictor


How to cite this article:
Kalejaiye OO, Bolarinwa AB, Amaeshi LC, Ogamba CF, Nmadu DA, Sopekan BA, Akase IE. Haematological indices and coagulation profile as predictors of disease severity and associations with clinical outcome among COVID-19 patients in Lagos, Nigeria. Ann Afr Med 2023;22:204-12

How to cite this URL:
Kalejaiye OO, Bolarinwa AB, Amaeshi LC, Ogamba CF, Nmadu DA, Sopekan BA, Akase IE. Haematological indices and coagulation profile as predictors of disease severity and associations with clinical outcome among COVID-19 patients in Lagos, Nigeria. Ann Afr Med [serial online] 2023 [cited 2023 Jun 7];22:204-12. Available from: https://www.annalsafrmed.org/text.asp?2023/22/2/204/373560




   Introduction Top


The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a novel respiratory virus that arose as a global public health emergency in 2019, has affected over 517 million people globally to date, resulting in over six million deaths.[1] The first confirmed case of the coronavirus disease 2019 (COVID-19) in sub-Saharan Africa was in Nigeria in February 2020 in Lagos, southwest Nigeria.[2] Since then, the country has experienced a rising incidence of mortalities; an estimated 255,766 confirmed cases and 3143 deaths have been reported.[3]

Lagos, the epicenter of economic activities in Nigeria and the most populous state, accounts for the highest number of cases and community transmission rates in the country.[3],[4] Unfortunately, due to inadequate health-care resources and overwhelming number of COVID-19 infections, it is necessary to to risk stratify these patients to determine and determine those who will benefit from intensive care.[5]

Sociodemographic and clinical factors such as age and presence of comorbidities have been reported to increase disease severity and mortality in COVID-19 patients.[6],[7],[8] A study by Osibogun et al. found a significant association between the disease severity and the presence of comorbidities in patients with COVID-19.[4] Studies continue to investigate the most accurate predictors of severity to assist in allocating the scarce health resources in lower- and middle-income countries like Nigeria; however, many clinical factors in these patients remain inconsistent.[7]

Serological markers have been studied to predict disease severity in patients with SARS-CoV-2 and have shown that variation in levels of haematological indices, lymphocyte–monocyte ratio (LMR), neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR), coagulation profiles, and systemic immune-inflammation index (SII), can predict disease severity and mortality in patients with the SARS-CoV-2 virus.[9],[10],[11] These parameters are preferable in low-resource settings since they are readily calculated from a routine blood test, relatively inexpensive, and easily reproducible, making them useful for risk stratification of patients.[9],[12] Most of the available studies on this subject have been carried out mainly in Caucasian and Asian populations; however, their findings are not generalizable to our population since normal ranges for these indices often vary by region and may be influenced by biological differences.[10] To our knowledge, no similar studies have been conducted in Nigeria. Therefore, this study aims to evaluate the use of these haematological indices and coagulation profiles as possible low-cost predictors of disease severity and investigate their associations with clinical outcomes among COVID-19 hospitalized patients in an academic teaching hospital in Lagos, Nigeria.


   Materials and Methods Top


Study design and location

This study was a three-month (April to July 2021) hospital-based observational longitudinal study of COVID-19 positive patients from the time of hospitalization till discharge or death. It was carried out in a dedicated 120-bedded national COVID-19 isolation ward of the Lagos University Teaching Hospital (LUTH).[13]

Study population

The study population consisted of adult patients (18 years or older) who tested positive for SARS-COV-2 virus by polymerase chain reaction and consented to participate in the study. All patients who met the study criteria were consecutively recruited. Patients were categorized into two patient cohorts of “severe” and “not severe” according to the definitions by Cascella et al. in 2020 and with the help of the National Interim Guidelines on the clinical management of COVID-19 from the Nigeria Centre for Disease Control.[14],[15] Clinical outcomes were categorized as “Good” for patients who recovered fully and were discharged home without being admitted to the intensive care unit, “Intensive Care” for patients who had to be admitted to the intensive care unit but were eventually discharged home and “Mortality” for all patients who died.

Data collection

A predesigned and pretested, structured questionnaire was used to obtain the sociodemographic characteristics, comorbid conditions, and presenting symptoms of the participants and their length of hospital stay. Patients' vital signs were measured, and oxygen saturation was determined with a pulse oximeter. Blood pressure ≥140/90 mmHg was classified as elevated using the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 7) classification.[16]

Sample processing procedure

Blood samples were collected from the participants for full blood count (FBC), prothrombin time (PT), activated partial thromboplastin time (aPTT), and D-dimer estimation. The citrate anticoagulated samples were spun and platelet-poor plasma removed, and aliquots were put in cryovials. These were stored at 80°C pending analysis. D-dimer samples were analyzed using a human D-dimer kit from China Shangai Korain Biotech Co Limited (bioassay biotech). Full blood count was determined using an autohematology analyzer. Clotting times (PT and aPTT) were determined with a Sysmex CA-101 coagulometer.

Calculation of haematological indices

Inflammatory markers were derived from full blood count indices; the PLR was calculated by dividing the platelet count by the absolute lymphocyte count (ALC) and has been shown to be associated with disease severity.[17] The LMR was derived by dividing the ALC by the monocyte count.[9] The NLR was derived by dividing the absolute neutrophil count (ANC) by the ALC, whereas the systemic SII was calculated by multiplying the platelet count by the ANC divided by the ALC.[12]

Statistical analysis

Data were analyzed using the Statistical Package for the Social Sciences (SPSS) software version 26.0 for Windows (SPSS Inc., Chicago, IL, USA). Shapiro–Wilk test for normality was carried out on continuous variables. Continuous variables were presented as mean ± standard deviation for normally distributed data and median and interquartile range (IQR) for skewed data. Categorical variables were presented as frequencies and percentages. For continuous variables, comparison between groups was done using Independent t-tests and analysis of variance for normally distributed variables. Mann–Whitney U test and the Kruskal–Wallis H test were used for skewed variables. Chi-square tests and Fisher's exact test were used to compare categorical variables. A receiver operating characteristic (ROC) curve analysis was subsequently done on the haematological parameters and the coagulation profile, and significant variables were subjected to a Chi-square test for subgroup analysis using the optimal cutoff values from the ROC curve to determine associations with disease severity. P < 0.05 was considered statistically significant.

Ethics

Ethical approval was obtained from the Health Research Ethics Committee of LUTH with the assigned number: ADM/DCST/APP/4179 before the start of the study. Informed consent was sought from patients, and the benefits and associated risks were carefully explained to all patients before recruiting them into the study.


   Results Top


Sociodemographic characteristics and clinical characteristics of patients

The sociodemographic and clinical characteristics of the patients are shown in [Table 1]. Fifty-eight patients with confirmed cases of SARS-COV-2 consented to the study and were recruited. The mean age of the patients was 54.4 ± 14.8 years, ranging from 18 to 86 years. More than half of the participants were males (55.2%, n = 32) and most had at least one comorbidity (79.3%, n = 46). The pattern of comorbidities in the respondents is shown in [Figure 1]; the most frequently occurring comorbidity was hypertension (68.9%, n = 40), followed by diabetes (34.5%, n = 20). The vital signs of the patients at presentation are also shown in [Table 1]. Only 44.8% (n = 26) of the patients had elevated blood pressure. The median respiratory rate was 30 cycles per minute (cpm) (IQR: 22–32 cpm), and oxygen saturation was 87% (IQR: 82.8–94.3%). The distribution of symptoms reported by patients is shown in [Figure 2].
Table 1: Study demographic and clinical characteristics (n=58)

Click here to view
Figure 1: Pattern of comorbidities among COVID-19 patients in LUTH. LUTH = Lagos University Teaching Hospital

Click here to view
Figure 2: Pattern of presenting symptoms

Click here to view


Almost two-thirds of the patients were on oxygen therapy (63.8%. n = 37), whereas only a minority were placed on mechanical ventilation (15.5%, n = 9). Most of the patients were fully conscious (84.5%, n = 49), and a minority of patients were unconscious (8.6%, n = 9). Seven patients (12.1%) had chest X-ray images suggestive of COVID-19, whereas 28 (48.3%) patients had chest computed tomography findings suggestive of COVID-19.

The majority of patients had severe disease (60.3%, n = 35). Regardless, most patients had good outcomes and were discharged without needing intensive care (77.6%, n = 45), seven patients were admitted to the intensive care unit and were subsequently discharged (12.1%), and six patients died (10.3%).

Haematological indices and coagulation profile of patients

The full blood count, NLR, PLR, LMR, SII, and coagulation profile of the patients are shown in [Table 2]. The mean packed cell volume (PCV%) of the patients was 37.2 ± 5.3 and ranged from 26.8 to 55.7 with hemoglobin (Hb) of 12.3 ± 1.7 g/dl. The median white cell count was 6.3 × 109/L (IQR: 4.8–10.6 × 109/L) with a median ANC of 5.1 × 109/L (IQR: 2.9–8.6) and a median ALC of 1.1 × 109/L (IQR 0.8–1.7). The mean platelet count was 258.9 ± 131.7 × 109/L and ranged from 47 to 640 × 109/L.
Table 2: Haematological indices and coagulation profile of patients (n=58)

Click here to view


The median NLR was 4.5 (IQR: 2.1–8.3), the median LMR was 3.21 (IQR: 2.2–4.6), the median PLR was 208.3 (IQR: 90.3–397.9), and SII was 933.3 (463.2–2426.9). The median PT of the patient sample was 12.2 s (IQR: 10.3–14.4 s), median aPTT was 34.9 s (23.7–53.2 s), and the median D-dimer was 79.0 ng/ml (63.3–118.4 ng/ml).

Relationship between patient demographic and clinical characteristics and severity and outcome of illness

The bivariate associations between patient demographic and clinical characteristics and the severity of illness are shown in [Table 3]. The mean age was significantly higher in patients with severe disease than in those without severe disease (58.9 ± 11.8 vs. 47.0 ± 16.3, t-test = 2.986, P = 0.01). The presence of comorbidities (P = 0.047) and use of oxygen therapy (P < 0.001) were significantly associated with severe disease. Furthermore, patients with severe disease had significantly higher respiratory rates and lower oxygen saturation than those without severe disease (Mean rank for RR: 37.9 vs. 16.8, Mann–Whitney U = −4.691, P < 0.001, Mean rank for SpO2: 19.1 vs. 45.4, Mann–Whitney U = −5.819, P < 0.001).
Table 3: Bivariate associations between participant characteristics and disease severity

Click here to view


The bivariate associations between patient demographic and clinical characteristics and the outcome of the illness are shown in [Table 4]. The age of patients (F (2,54) = 4.864, P = 0.01) was significantly associated with outcome. A Tukey post hoc test revealed that age was statistically significantly higher in patients who died (71.0 ± 13.3, P = 0.01) than in patients with good outcomes (52.2 ± 14.6). There were no statistically significant differences in age between patients when other categories of outcomes were compared. The use of mechanical ventilation (χ2 = 31.576, P < 0.001) and level of consciousness (χ2 = 25.221, P < 0.001) were also significantly associated with outcome. In addition, oxygen saturation (Kruskal–Wallis H (2) = 8.083, P = 0.018) and respiratory rate (Kruskal–Wallis H (2) = 6.161, P = 0.046) were significantly associated with outcomes.
Table 4: Bivariate associations between participant characteristics and disease outcome

Click here to view


Relationship between patient haematological and coagulation indices with disease severity and outcome

The bivariate associations between patient haematological indices and coagulation profile and the severity and outcome of the illness are shown in [Table 5]. Higher ANC was significantly associated with the severity of the disease (Mean rank: 28.6 vs. 18.8, Mann–Whitney U = −2.395, P = 0.02). Furthermore, a lower ALC was significantly associated with disease severity (Mean rank: 20.9 vs. 29.5, Mann–Whitney U = −2.081, P = 0.04). The NLR (Mann–Whitney U = −2.562, P = 0.01), LMR (Mann–Whitney U = −2.583, P = 0.01) and SII (Mann–Whitney U = −2.060, P = 0.04) were significantly associated with the severity of disease with higher NLR (Mean rank: 28.9 vs. 18.4) and SII (Mean rank: 28.0 vs. 19.6) and lower LMR (Mean rank: 20.1 vs. 30.7) associated with severe disease than in nonsevere disease. Furthermore, patients' hemoglobin concentration (F (2,47) = 3.351, P = 0.04), packed cell volume (F (2,47) = 5.530, P < 0.001), and mean cell hemoglobin concentration (MCHC) (F (2,47) = 3.921, P = 0.03) were significantly associated with outcome of illness. Tukey post hoc tests revealed statistically significantly higher hemoglobin concentration in patients who were admitted to intensive care (13.9 ± 2.3, P = 0.04) than in those who had good outcomes (12.0 ± 1.5), higher packed cell volumes in patients admitted to intensive care (43.5 ± 6.5, P = 0.01) than in those who had good outcomes (36.3 ± 4.7), and higher MCHC in patients who died (34.2 ± 0.9, P = 0.02) than in patients admitted to intensive care (31.9 ± 1.9). No other significant mean differences were observed.
Table 5: Bivariate associations between patient haematological indices and coagulation profile and illness severity and outcome

Click here to view


Receiver operating characteristic curve analysis for the severity of COVID-19 infection

The receiver operating characteristic (ROC) curve analysis for the severity of illness is shown in [Table 6]. The analysis proposed cutoff values for the ANC [Figure 3], ALC [Figure 4], NLR [Figure 5], LMR [Figure 6], and SII [Figure 7]. The ANC cutoff value was ≥3.60 (area under the ROC curve [AUC], 0.704; 95% confidence interval [CI], 0.552–0.857; P = 0.02) with 82.1% sensitivity and 60.0% specificity. The ALC cutoff value was ≤1.25 (AUC, 0.678; 95% CI, 0.525–0.831; P = 0.04) with 71.4% sensitivity and 65.0% specificity. The LMR cutoff value was ≤3.46 (AUC, 0.721; 95% CI, 0.573–0.868; P = 0.01) with 75.0% sensitivity and 70.0% specificity. The NLR cutoff value was ≥3.27 (AUC, 0.719; 95% CI, 0.569–0.868; P = 0.01) with 78.6% sensitivity and 65.0% specificity and the SII cutoff value was ≥812.65 (AUC, 0.676; 95% CI, 0.524–0.828; P = 0.04) with 67.9% sensitivity and 60.0% specificity.
Table 6: Receiver operating characteristic curve analysis for the severity of COVID-19 infections

Click here to view
Figure 3: ROC Curve of ANC. ANC = Absolute neutrophil count

Click here to view
Figure 4: ROC Curve of ALC. ALC = Absolute lymphocyte count

Click here to view
Figure 5: ROC Curve of NLR. NLR = Neutrophil–lymphocyte ratio

Click here to view
Figure 6: ROC Curve of LMR. LMR = Lymphocyte–monocyte ratio

Click here to view
Figure 7: ROC Curve of SII. SII = Systemic immune-inflammation index

Click here to view


Finally, when the bivariate analysis was done using the calculated ROC cutoff values of ANC, ALC, LMR, and NLR as a threshold for predicting the severity of illness, there was a significantly higher proportion of patients with severe disease [Table 7]. The association was not significant for the SII.
Table 7: Bivariate analysis of severity and nonseverity with receiver operating characteristic cutoff values for absolute neutrophil count, absolute lymphocyte count, neutrophil-lymphocyte ratio and systemic immune inflammation index

Click here to view



   Discussion Top


The mean age in our study population is comparable to other studies, many of which have reported infections in patients older than 60 years.[18],[19] Also, higher mean age was associated with disease severity and mortality. This association is consistent with other observations, which reported worse clinical outcomes including mortality in older patients.[19],[20]

One of the major reasons that have been proposed to explain this association is the prevalence of comorbidities among older patients which increases their susceptibility to severe infections.[21],[22] A high prevalence of hypertension and diabetes were observed in our study. The presence of comorbidities was also significantly associated with severity of disease. Similar studies have also reported worse COVID-19 outcomes in patients with co-morbidities.[7],[22]

The effects of COVID-19 on the haematological system have been reported in various studies, with reports of coagulopathy presenting as disseminated intravascular coagulopathy and venous thromboembolism in severe cases of the disease.[23],[24] In the current study, the median D-dimer level was not significantly elevated. However, studies have shown elevated d-dimer levels in COVID -19 patients to be predictive of mortality.[25],[26] Perhaps the lack of statistical significance in the current study was a result of the relatively smaller size compared to other studies.

Similarly, the relationship between coagulation profile and disease severity and outcome in our study is in contrast to the study by Zou et al. in Shanghai, China, who found significantly higher PT and aPTT in cases of severe disease compared to mild disease.[27] However, a previous study reported the actual prevalence of coagulation abnormalities to be about 20% and found no correlation between severity risk and both aPTT and PT levels.[28]

Our study showed significantly higher ANC, NLR, and SII as well as significantly lower ALC and LMR in patients with severe disease confirmed by ROC analysis. This corroborates previous studies which have shown that these direct and calculated indices can be used as low-cost predictors of severity in patients, especially in low-resource countries like Nigeria.[10],[11],[12] However, the high prevalence of comorbidities in our sample was not accounted for. As such, these parameters should be further explored to confirm their use as clinical predictors of disease severity in patients with COVID-19 infection in Nigeria.

Although we found significantly elevated hemoglobin concentration and PCV in patients admitted to intensive care compared to those who were not, as well as significantly higher MCHC in patients who died, previous studies have reported varying findings regarding the effect of COVID-19 on red blood cell indices.[29],[30] The variation observed in our study sample, therefore, merits further exploration.

Strengths and limitations

The strength of our study consists in its being the first to highlight the utility of these haematological indices to determine severity in COVID-19 patients in Nigeria. The limitation of this study is the small sample size which limited further subgroup analyses as well as the consecutive hospital-based sample which limit the generalizability of the findings. Our findings should therefore be treated as hypothesis generating.


   Conclusion Top


Our findings show with proposed cutoffs, that higher ANC, NLR, and SII, as well as lower ALC and LMR, may correlate significantly with severity of COVID-19 infection in Nigerian patients and are thus possible, easily accessible low-cost predictors of disease severity in COVID-19 patients in Nigeria, as they can be derived from routine full blood count measurements.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
   References Top

1.
COVID Live – Coronavirus Statistics – Worldometer; 2022. Available from: https://www.worldometers.info/coronavirus/. [Last accessed on 2022 May 08].  Back to cited text no. 1
    
2.
Adepoju P. Nigeria responds to COVID-19; first case detected in Sub-Saharan Africa. Nat Med 2020;26:444-8.  Back to cited text no. 2
    
3.
NCDC Coronavirus COVID-19 Microsite; 2022. Available from: https://covid19.ncdc.gov.ng/. [Last accessed on 2022 May 08].  Back to cited text no. 3
    
4.
Osibogun A, Balogun M, Abayomi A, Idris J, Kuyinu Y, Odukoya O, et al. Outcomes of COVID-19 patients with comorbidities in Southwest Nigeria. PLoS One 2021;16:e0248281.  Back to cited text no. 4
    
5.
Norton AJ, Wiysonge CS, Habarugira JM, White N, Bayona MT, Hagen H, et al. Priorities for COVID-19 research response and preparedness in low-resource settings. Lancet 2021;397:1866-8.  Back to cited text no. 5
    
6.
Szklanna PB, Altaie H, Comer SP, Cullivan S, Kelliher S, Weiss L, et al. Routine hematological parameters may be predictors of COVID-19 severity. Front Med (Lausanne) 2021;8:682843.  Back to cited text no. 6
    
7.
Yang J, Zheng Y, Gou X, Pu K, Chen Z, Guo Q, et al. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: A systematic review and meta-analysis. Int J Infect Dis 2020;94:91-5.  Back to cited text no. 7
    
8.
Sanyaolu A, Okorie C, Marinkovic A, Patidar R, Younis K, Desai P, et al. Comorbidity and its impact on patients with COVID-19. SN Compr Clin Med 2020;2:1069-76.  Back to cited text no. 8
    
9.
Waris A, Din M, Khalid A, Abbas Lail R, Shaheen A, Khan N, et al. Evaluation of hematological parameters as an indicator of disease severity in COVID-19 patients: Pakistan's experience. J Clin Lab Anal 2021;35:e23809.  Back to cited text no. 9
    
10.
Ghahramani S, Tabrizi R, Lankarani KB, Kashani SM, Rezaei S, Zeidi N, et al. Laboratory features of severe versus non-severe COVID-19 patients in Asian populations: A systematic review and meta-analysis. Eur J Med Res 2020;25:30.  Back to cited text no. 10
    
11.
Mousavi SA, Rad S, Rostami T, Rostami M, Mousavi SA, Mirhoseini SA, et al. Hematologic predictors of mortality in hospitalized patients with COVID-19: A comparative study. Hematology 2020;25:383-8.  Back to cited text no. 11
    
12.
Usul E, Şan İ, Bekgöz B, Şahin A. Role of hematological parameters in COVID-19 patients in the emergency room. Biomark Med 2020;14:1207-15.  Back to cited text no. 12
    
13.
Akinbolagbe YO, Otrofanowei E, Akase IE, Akintan PE, Ima-Edomwonyi UE, Olopade BO, et al. Predictors and outcomes of COVID-19 patients with hypoxemia in Lagos, Nigeria. J Pan Afr Thorac Soc 2022;3:42-50.  Back to cited text no. 13
    
14.
Cascella M, Rajnik M, Cuomo A, Dulebohn SC, Di Napoli R. Features, evaluation and treatment coronavirus (COVID-19). In: StatPearls. Treasure Island (FL): StatPearls Publishing; 2020. Available from: https://www.ncbi.nlm.nih.gov/books/NBK554776/. [Last accessed on 2022 May 12].  Back to cited text no. 14
    
15.
National Interim Guidelines for Clinical Management of COVID-19. Available from: https://covid19.ncdc.gov.ng/media/files/COVIDCaseMgtVersion 4.pdf. [Last accessed on 2022 Jun 23].  Back to cited text no. 15
    
16.
Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure; 2022. Available from: https://read.qxmd.com/read/14656957/seventh-report-of-the-joint-national-committee-on-prevention-detection-evaluation. [Last accessed on 2022 May 12].  Back to cited text no. 16
    
17.
Qu R, Ling Y, Zhang YH, Wei LY, Chen X, Li XM, et al. Platelet-to-lymphocyte ratio is associated with prognosis in patients with coronavirus disease-19. J Med Virol 2020;92:1533-41.  Back to cited text no. 17
    
18.
Yanez ND, Weiss NS, Romand JA, Treggiari MM. COVID-19 mortality risk for older men and women. BMC Public Health 2020;20:1742.  Back to cited text no. 18
    
19.
Liu K, Chen Y, Lin R, Han K. Clinical features of COVID-19 in elderly patients: A comparison with young and middle-aged patients. J Infect 2020;80:e14-8.  Back to cited text no. 19
    
20.
Davies NG, Klepac P, Liu Y, Prem K, Jit M, CMMID COVID-19 Working Group, et al. Age-dependent effects in the transmission and control of COVID-19 epidemics. Nat Med 2020;26:1205-11.  Back to cited text no. 20
    
21.
Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: A single-centered, retrospective, observational study. Lancet Respir Med 2020;8:475-81.  Back to cited text no. 21
    
22.
Abayomi A, Osibogun A, Kanma-Okafor O, Idris J, Bowale A, Wright O, et al. Morbidity and mortality outcomes of COVID-19 patients with and without hypertension in Lagos, Nigeria: A retrospective cohort study. Glob Health Res Policy 2021;6:26.  Back to cited text no. 22
    
23.
Tang N, Li D, Wang X, Sun Z. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost 2020;18:844-7.  Back to cited text no. 23
    
24.
Middeldorp S, Coppens M, van Haaps TF, Foppen M, Vlaar AP, Müller MC, et al. Incidence of venous thromboembolism in hospitalized patients with COVID-19. J Thromb Haemost 2020;18:1995-2002.  Back to cited text no. 24
    
25.
Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 2020;323:1061-9.  Back to cited text no. 25
    
26.
Zhang L, Yan X, Fan Q, Liu H, Liu X, Liu Z, et al. D-dimer levels on admission to predict in-hospital mortality in patients with COVID-19. J Thromb Haemost 2020;18:1324-9.  Back to cited text no. 26
    
27.
Zou Y, Guo H, Zhang Y, Zhang Z, Liu Y, Wang J, et al. Analysis of coagulation parameters in patients with COVID-19 in Shanghai, China. Biosci Trends 2020;14:285-9.  Back to cited text no. 27
    
28.
Lin J, Yan H, Chen H, He C, Lin C, He H, et al. COVID-19 and coagulation dysfunction in adults: A systematic review and meta-analysis. J Med Virol 2021;93:934-44.  Back to cited text no. 28
    
29.
Palladino M. Complete blood count alterations in COVID-19 patients: A narrative review. Biochem Med (Zagreb) 2021;31:030501.  Back to cited text no. 29
    
30.
Cai SH, Liao W, Chen SW, Liu LL, Liu SY, Zheng ZD. Association between obesity and clinical prognosis in patients infected with SARS-CoV-2. Infect Dis Poverty 2020;9:80.  Back to cited text no. 30
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]



 

Top
 
 
  Search
 
    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

 
  In this article
    Abstract
   Introduction
    Materials and Me...
   Results
   Discussion
   Conclusion
    References
    Article Figures
    Article Tables

 Article Access Statistics
    Viewed608    
    Printed4    
    Emailed0    
    PDF Downloaded2    
    Comments [Add]    

Recommend this journal