ArticleEJIFCC2021
Predictive Model of Severity in SARS CoV-2 Patients at Hospital Admission Using Blood-Related Parameters.
Article in EJIFCC, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- Prognostic models in COVID-19 infection that predict severity: a systematic review.European journal of epidemiology · 2023Pooled it
- Lactate dehydrogenase and aspartate aminotransferase levels associated with the severity of COVID‑19: A systematic review and meta‑analysis.Experimental and therapeutic medicine · 2023Article
- Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionBlood test alterations are crucial in SARS CoV-2 (COVID-19) patients. Blood parameters, such as lymphocytes, C reactive protein (CRP), creatinine, lactate dehydrogenase, or D-dimer, are associated with severity and prognosis of SARS CoV-2 patients. This study aims to identify blood-related predictors of severe hospitalization in patients diagnosed with SARS CoV-2.
methodsObservational retrospective study of all rt-PCR and blood-test positive (at 48 hours of hospitalization) SARS CoV-2 diagnosed inpatients between March-May 2020. Deceased and/or ICU inpatients were considered as severe cases, whereas those patients after hospital discharge were considered as non-severe. Multivariate logistic regression was used to identify predictors of severity, based on bivariate contrast between severe and mild inpatients.
resultsThe overall sample comprised 540 patients, with 374 mild cases (69.26%), and 166 severe cases (30.75%). The multivariate logistic regression model for predicting SARS CoV-2 severity included lymphocytes, C reactive protein (CRP), creatinine, total protein levels, glucose and aspartate aminotransferase as predictors, showing an area under the curve (AUC) of 0.895 at a threshold of 0.29, with 81.5% of sensitivity and 81% of specificity. DISCUSSION: Our results suggest that our predictive model allows identifying and stratifying SARS CoV-2 patients in risk of developing severe medical complications based on blood-test parameters easily measured at hospital admission, improving health-care resources management and distribution.
Indexed as
Identifiers
34421494PMC8343039W3195237236What OpenQuestion holds
Registered trials
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.