Lymphocyte count is a universal predictor to the health status and outcomes of patients with coronavirus disease 2019 (COVID-19): A systematic review and meta-regression analysis
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Abstract
Background
This study aimed to evaluate the prediction capabilities of clinical laboratory biomarkers to the prognosis of COVID-19 patients.
Methods
Observational studies reporting at least 30 cases of COVID-19 describing disease severity or mortality were included. Meta-data of demographics, clinical symptoms, vital signs, comorbidities, and 14 clinical laboratory biomarkers on initial hospital presentation were extracted. Taking the outcome group as the analysis unit, meta-regression analysis with the generalized estimating equations (GEE) method for clustered data was performed sequentially. The unadjusted effect of each potential predictor of the three binary outcome variables (i.e., severe vs. non-severe, critically severe vs. non-critically severe, and dead vs. alive) was examined one by one by fitting three series of simple GEE logistic regression models due to missing data. The worst one was dropped one at a time. Then, a final multiple GEE logistic regression model for each of the three outcome variables was obtained.
Findings
Meta-data was extracted from 76 articles, reporting a total of 26,627 cases of COVID-19. Patients were recruited across 16 countries. The number of studies (patients) included in the final models of the analysis for severity, critical severity, and mortality was 38 studies (9,764 patients), 21 studies (4,792 patients), and 24 studies (14,825 patients), respectively. After adjusting for the effect of age, lymphocyte count mean or median ≤ 1.03 (estimated hazard ratio [HR] = 46.2594, p < 0.0001), smaller lymphocyte count mean or median (HR < 0.0001, p = 0.0028), and lymphocyte count mean or median ≤ 0.8714 (HR = 17.3756, p = 0.0079) were the strongest predictor of severity, critical severity, and mortality, respectively.
Interpretation
Lymphocyte count should be closely watched for COVID-19 patients in clinical practice.
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SciScore for 10.1101/2021.08.02.21261505: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable Data Extraction: The following data were extracted from the qualified studies: first author, year/month of the publication, location (city-country), hospital name, definition of disease severity, subject number, number of COVID-19 patients in each health status, age, male to female ratio, vital sign, clinical feature (12 symptoms), comorbidity (any; 8 main diseases), and desired 14 lab data [Appendix I, Appendix II). Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources Literature Search Strategy: We used the search terms “COVID-19”, “2019-nCoV”, and “coronavirus” in the … SciScore for 10.1101/2021.08.02.21261505: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable Data Extraction: The following data were extracted from the qualified studies: first author, year/month of the publication, location (city-country), hospital name, definition of disease severity, subject number, number of COVID-19 patients in each health status, age, male to female ratio, vital sign, clinical feature (12 symptoms), comorbidity (any; 8 main diseases), and desired 14 lab data [Appendix I, Appendix II). Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources Literature Search Strategy: We used the search terms “COVID-19”, “2019-nCoV”, and “coronavirus” in the search field “Title/Abstract”, at the electronic databases: MEDLINE, and EMBASE. MEDLINEsuggested: (MEDLINE, RRID:SCR_002185)EMBASEsuggested: (EMBASE, RRID:SCR_001650)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:However, such data has its limitations. Routine lab data retains several advantages, which can indicate the whole body situation of a COVID-19 patient whose functions can be changed dramatically in few days.122 Additionally lab testing is easy to access, repeatable, self-explain, relatively cheap, and therefore can be a cost-effective tool under pandemic circumstances. Current criteria to judge the severity, to triage or referral COVID-19 patients, are based on imaging, demographics, comorbidities, vital signs, or symptoms.6, 123, 124 Based on our study results single lab data, lymphocyte count at administration plus age can be useful for the purposes. Early and continue monitoring lab data for a COVID-19 patient can help to understand the health state, triage the patient, predict the severity of disease, predict the health consequences, and workout treatment judgment appropriately. Our study has several limitations. Due to the lack of non-English articles, pediatric study and specific disease groups, interpretation of the results must be cautious. Ideally, all desired lab data should be collected and analyzed in all studies. However, it is not realistic in the real world because of wide-ranging medical resource deficiency that existed across countries. We suggest collecting essential data through a standardized list while clinical presentation, medical history, imaging information, comprehensive lab data, and other valuable factors, can be assembled and analyzed which will a...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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