COVID‐19 and hematology findings based on the current evidences: A puzzle with many missing pieces

Karimi‐Shahri, Mehdi; Niazkar, Hamid Reza; Rad, Fariba

doi:10.1111/ijlh.13412

Cited by 60 publications

(54 citation statements)

References 58 publications

(91 reference statements)

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“…These factors were not particularly included in our prediction model, although the “hemato” rubric on our risk matrix considers all abnormalities discovered in each patient evaluated. Here, a systematic review carried by Shahri et al [ 20 ] discovered that leukopenia seems to be directly proportional correlated to COVID-19 disease severity, while lymphopenia can be used as a prognostic prediction factor. Another systematic review conducted by Slomka et al [ 21 ] proves that D-dimer levels are markedly increased in patients with severe COVID-19 infection.…”

Section: Discussionmentioning

confidence: 99%

Predictive Value of Comorbid Conditions for COVID-19 Mortality

Marincu

Bratosin

Vidican

et al. 2021

JCM

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In this paper, we aim at understanding the broad spectrum of factors influencing the survival of infected patients and the correlations between these factors to create a predictive probabilistic score for surviving the COVID-19 disease. Initially, 510 hospital admissions were counted in the study, out of which 310 patients did not survive. A prediction model was developed based on this data by using a Bayesian approach. Following the data collection process for the development study, the second cohort of patients totaling 541 was built to validate the risk matrix previously created. The final model has an area under the curve of 0.773 and predicts the mortality risk of SARS-CoV-2 infection based on nine disease groups while considering the gender and age of the patient as distinct risk groups. To ease medical workers’ assessment of patients, we created a visual risk matrix based on a probabilistic model, ranging from a score of 1 (very low mortality risk) to 5 (very high mortality risk). Each score comprises a correlation between existing comorbid conditions, the number of comorbid conditions, gender, and age group category. This clinical model can be generalized in a hospital context and can be used to identify patients at high risk for whom immediate intervention might be required.

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Section: Discussionmentioning

confidence: 99%

Predictive Value of Comorbid Conditions for COVID-19 Mortality

Marincu

Bratosin

Vidican

et al. 2021

JCM

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“…2,3 Interestingly, significant differences in morphology and function of monocytes have been recently observed between COVID-19 patients and healthy individuals. 4 Therefore, in this article, we aim to provide a pooled analysis of studies that have addressed the potential clinical utility of MDW for predicting SARS-CoV-2 infection at hospital admission.…”

mentioning

confidence: 99%

Pooled analysis of monocyte distribution width in subjects with SARS‐CoV‐2 infection

Lippi

Sanchís-Gomar

Henry

2021

Int J Lab Hematology

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“…Recently, Y. Liu et al, 2020d;Shahri et al, 2021;Yang et al, 2020b have found that thrombocytopenia could increase the mortality risk among COVID-19 patients. It was also noticed that the daily count of recovery cases exceeded the con rmed cases after mid-September, 2020 and this parallel pattern continued up to the end of January, 2021 when the AT was continuously decreasing.…”

Section: Resultsmentioning

confidence: 99%

Does Environment has any role in recovery from COVID-19 pandemic? A case study from India.

Gupta¹,

Saha

2021

Preprint

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The present study has investigated the role of meteorology and air quality for recovering from the COVID-19 pandemic in India. Using Pearson’s correlation method, we look into if there is any significant association occurs between the district level recovery case counts and different remote sensing based environmental variables. Among weather parameters, only precipitation and air temperature found to be significantly correlated with recovery situation. However, all the pollutants’ concentration was negatively correlated with count of recovery cases. It depicts that air quality might has greater importance in recovery from this disease. During late monsoon onwards, recovery rate was getting more than the infections which indicate that lesser temperature and good rainfall could help the air to be freshen. Through air pollution was greater during winter and post monsoon than the summer season in India the higher recovery rate was counted during post-monsoon and winter which suggest that patients may require lesser temperate ambient for better recovery. Spatial patterns also suggest that north-eastern hilly region followed by districts located in the northern mountain had better recovery where the pollutants’ concentration was also quite lower during the study period. Therefore, improving air quality with proper preventive precaution could help to combat the pandemic situation in India.

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COVID‐19 and hematology findings based on the current evidences: A puzzle with many missing pieces

Cited by 60 publications

References 58 publications

Predictive Value of Comorbid Conditions for COVID-19 Mortality

Predictive Value of Comorbid Conditions for COVID-19 Mortality

Pooled analysis of monocyte distribution width in subjects with SARS‐CoV‐2 infection

Does Environment has any role in recovery from COVID-19 pandemic? A case study from India.

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