Evidence map›Paper›PMID 33010111›Full record

SynthesisInternational journal of laboratory hematology2021

A meta-analysis of SARS-CoV-2 patients identifies the combinatorial significance of D-dimer, C-reactive protein, lymphocyte, and neutrophil values as a predictor of disease severity.

Kunwar Singh, Sasha Mittal, Sumanth Gollapudi, Alexandra Butzmann, Jyoti Kumar, Robert S Ohgami

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in International journal of laboratory hematology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  7. The Saga of COVID-19 in Chronic Lung Disease: History Repeats Itself.Annals of the American Thoracic Society · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Kunwar SinghDepartment of Pathology, Stanford University, Stanford, CA, USA.ORCID https://orcid.org/0000-0002-9024-5751
Sasha MittalDepartment of Pathology, University of California, San Francisco, San Francisco, CA, USA.
Sumanth GollapudiDepartment of Pathology, University of California, San Francisco, San Francisco, CA, USA.
Alexandra ButzmannDepartment of Pathology, University of California, San Francisco, San Francisco, CA, USA.
Jyoti KumarDepartment of Pathology, Stanford University, Stanford, CA, USA.
Robert S OhgamiDepartment of Pathology, University of California, San Francisco, San Francisco, CA, USA.ORCID https://orcid.org/0000-0003-1881-3440

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), known to be the causative agent of COVID-19, has led to a worldwide pandemic. At presentation, individual clinical laboratory blood values, such as lymphocyte counts or C-reactive protein (CRP) levels, may be abnormal and associated with disease severity. However, combinatorial interpretation of these laboratory blood values, in the context of COVID-19, remains a challenge.

methodsTo assess the significance of multiple laboratory blood values in patients with SARS-CoV-2 and develop a COVID-19 predictive equation, we conducted a literature search using PubMed to seek articles that included defined laboratory data points along with clinical disease progression. We identified 9846 papers, selecting primary studies with at least 20 patients for univariate analysis to identify clinical variables predicting nonsevere and severe COVID-19 cases. Multiple regression analysis was performed on a training set of patient studies to generate severity predictor equations, and subsequently tested on a validation cohort of 151 patients who had a median duration of observation of 14 days.

resultsTwo COVID-19 predictive equations were generated: one using four variables (CRP, D-dimer levels, lymphocyte count, and neutrophil count), and another using three variables (CRP, lymphocyte count, and neutrophil count). In adult and pediatric populations, the predictive equations exhibited high specificity, sensitivity, positive predictive values, and negative predictive values.

conclusionUsing the generated equations, the outcomes of COVID-19 patients can be predicted using commonly obtained clinical laboratory data. These predictive equations may inform future studies evaluating the long-term follow-up of COVID-19 patients.

Indexed as

Automation, LaboratoryBiomarkersCOVID-19C-Reactive ProteinFemaleFibrin Fibrinogen Degradation ProductsHematologyHumansLeukocyte CountMaleModels, StatisticalNeutrophilsPredictive Value of TestsRetrospective StudiesSARS-CoV-2Severity of Illness IndexBiomarkersC-Reactive ProteinFibrin Fibrinogen Degradation Productsfibrin fragment DbloodCBCCOVID-19CRPD-dimerlymphocyteneutrophilSARS-CoV-2

Identifiers

PMID33010111
PMCPMC7675731

What OpenQuestion holds

Textmetadata
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.