Differential white blood cell count in the COVID-19: A cross-sectional study of 148 patients

Anurag, Aditya; Jha, Prakash Kumar; Kumar, Abhishek

doi:10.1016/j.dsx.2020.10.029

Cited by 70 publications

(60 citation statements)

References 14 publications

(15 reference statements)

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“…[20] As for cause-specific survival analysis, we have found even more factors to be associated with the rate of mortality or rate of discharge of a COVID-19 patient. Almost all factors we measured are associated with the rate of event (Table 2, [21][22][23][24][25][26][27][28][29][30][31][32][33][34] The fact that every single one of the factors we found to be significantly associated with rate of mortality/ discharge in Tables 2 and 3 had also been discussed in the literature, not only confirm our findings, but also show an extensive dysregulation in COVID-19 patients. Outpatients, with their zero hospitalization stay time, do not contribute to my analysis concerning stay time.…”

Section: Discussionsupporting

confidence: 85%

A Study on Factors Impacting Length of Hospital Stay of COVID-19 Inpatients

Çetin

Ülgen

Şıvgın

et al. 2021

Journal of Contemporary Medicine

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Knowing the typical length of hospital stay of COVID-19 patientsand which factors affecting the stay time are important for hospital management.A factor can impact the stay time in conflicting ways: a condition may,by the same mechanism, increase the stay time for a severe patient while decrease the time for an even more severe and deceased patient.Material and Method: 3184 COVID-19 patients from the Tokat State Hospital were examined on arrival to determine if they are required further treatment in hospital or not. Among them,404 are inpatients whose hospital stay time is available. By a simple and conditional linear regressions, we examine which factors statistically impact the hospital stay time,both overall and after taking into account the patient's age or eventual survival status.We also use causespecific Cox proportional-hazard regression for competing risk to examine contribution from a factor to the rate of event of mortality, and to the event of discharge.Results: Surviving ICU patients have longer hospital stay time than nonsurviving ICU patients, which in turn longer than non-ICU patients. Older age within acertain range is always correlated with a longer hospital stay. The factors with strongest signal in our dataset are C-reactive protein (CRP), hemoglobin (HGB)and calcium level: increased CRP level, decreased HGB level and calcium level are associated with longer hospital stay, independent from the contribution from surviving status. Other potentially staytime-impacting factors include d-dimer, urea, glucose, white blood cell (WBC) count, neutrophil, but these signals may not be robust against logtransformation of the stay time, or confounding with age. We also observed that glucose is more important than HbA1C or diabetes status in its influence on hospital stay time. Conclusion:Almost all factors we collected contribute to a faster/slower mortality or discharge rate, in particular C-reactive protein and hemoglobin. Measurement of the associated factors in COVID-19 patients could be used for a better hospital bed management.

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Section: Discussionsupporting

confidence: 85%

A Study on Factors Impacting Length of Hospital Stay of COVID-19 Inpatients

Çetin

Ülgen

Şıvgın

et al. 2021

Journal of Contemporary Medicine

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“…CRP is made by the liver and elevated level of CRP is a sign of inflammation [ 38 ]. Elevated white blood cell count is indicative of cytokine and immune response, including in COVID-19 infection [ 39 ]. Lymphocyte count is known to be lower in more severe COVID-19 patients and is associated with high mortality rate [ 20 , 24 , 40 – 42 ] and is associated with hospital-acquired AKI [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

Longitudinal prediction of hospital-acquired acute kidney injury in COVID-19: a two-center study

Hou

Duong

2021

Infection

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Background To investigate the temporal characteristics of clinical variables of hospital-acquired acute kidney injury (AKI) in COVID-19 patients and to longitudinally predict AKI onset. Methods There were 308 hospital-acquired AKI and 721 non-AKI (NAKI) COVID-19 patients from Stony Brook Hospital (New York, USA) data, and 72 hospital-acquired AKI and 303 NAKI COVID-19 patients from Tongji Hospital (Wuhan, China). Demographic, comorbidities, and longitudinal (3 days before and 3 days after AKI onset) clinical variables were used to compute odds ratios for and longitudinally predict hospital-acquired AKI onset. Results COVID-19 patients with AKI were more likely to die than NAKI patients (31.5% vs 6.9%, adjusted p < 0.001, OR = 4.67 [95% CI 3.1, 7.0], Stony Brook data). AKI developed on average 3.3 days after hospitalization. Procalcitonin was elevated prior to AKI onset ( p < 0.05), peaked, and remained elevated ( p < 0.05). Alanine aminotransferase, aspartate transaminase, ferritin, and lactate dehydrogenase peaked the same time as creatinine, whereas d -dimer and brain natriuretic peptide peaked a day later. C-reactive protein, white blood cell and lymphocyte showed group differences − 2 days prior ( p < 0.05). Top predictors were creatinine, procalcitonin, white blood cells, lactate dehydrogenase, and lymphocytes. They predicted AKI onset with areas under curves (AUCs) of 0.78, 0.66, and 0.56 at 0, − 1, and − 2 days prior, respectively. When tested on the Tongji Hospital data, the AUCs were 0.80, 0.79, and 0.77, respectively. Conclusions Time-locked longitudinal data provide insight into AKI progression. Commonly clinical variables reasonably predict AKI onset a few days prior. This work may lead to earlier recognition of AKI and treatment to improve clinical outcomes.

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“…Clinical data on these patients are important. Indeed, many researchers have examined the relationship between severe courses of COVID-19 and white blood cell counts in the body [ 6 , 7 ]. In these studies, it was revealed that some patients with COVID-19 had increased WBC and neutrophil counts with a decrease in lymphocyte count.…”

Section: Introductionmentioning

confidence: 99%

“…The study consisted of data on 148 SARS-CoV-2 infections confirmed by physicians. As a result, they stated that leukocytes, neutrophils, lymphocytes, monocytes and eosinophils, a high neutrophil-monocyte ratio (NMR) and a neutrophil-lymphocyte ratio (NLR), are indicators of disease severity in COVID-19 [ 6 ]. Zhao et al evaluated the clinical findings of COVID-19 patients with increased leukocytes.…”

Section: Introductionmentioning

confidence: 99%

Scat-NET: COVID-19 diagnosis with a CNN model using scattergram images

Tuncer

Ayyıldız

Kalaycı

et al. 2021

Computers in Biology and Medicine

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The acute respiratory syndrome COVID-19 disease, which is caused by SARS-CoV-2, has infected many people over a short time and caused the death of more than 2 million people. The gold standard in detecting COVID-19 is to apply the reverse transcription polymerase chain reaction (RT-PCR) test. This test has low sensitivity and produces false results of approximately 15%–20%. Computer tomography (CT) images were checked as a result of suspicious RT-PCR tests. If the virus is not infected in the lung, the virus is not observed on CT lung images. To overcome this problem, we propose a 25-depth convolutional neural network (CNN) model that uses scattergram images, which we call Scat-NET. Scattergram images are frequently used to reveal the numbers of neutrophils, eosinophils, basophils, lymphocytes and monocytes, which are measurements used in evaluating disease symptoms, and the relationships between them. To the best of our knowledge, using the CNN together with scattergram images in the detection of COVID-19 is the first study on this subject. Scattergram images obtained from 335 patients in total were classified using the Scat-NET architecture. The overall accuracy was 92.4%. The most striking finding in the results obtained was that COVID-19 patients with negative RT-PCR tests but positive CT test results were positive. As a result, we emphasize that the Scat-NET model will be an alternative to CT scans and could be applied as a secondary test for patients with negative RT-PCR tests.

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Differential white blood cell count in the COVID-19: A cross-sectional study of 148 patients

Cited by 70 publications

References 14 publications

A Study on Factors Impacting Length of Hospital Stay of COVID-19 Inpatients

A Study on Factors Impacting Length of Hospital Stay of COVID-19 Inpatients

Longitudinal prediction of hospital-acquired acute kidney injury in COVID-19: a two-center study

Scat-NET: COVID-19 diagnosis with a CNN model using scattergram images

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