Observational studyPloS one2020
Pre-test probability for SARS-Cov-2-related infection score: The PARIS score.
Observational study in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 4 of them syntheses that pooled it.
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Evaluation of Diagnostic Strategies for Identifying SARS-CoV-2 Infection in Clinical Practice: a Systematic Review and Compliance with the Standards for Reporting Diagnostic Accuracy Studies Guideline (STARD).Microbiology spectrum · 2022Pooled it
- Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19.The Cochrane database of systematic reviews · 2022Pooled it
- Clinical prediction models for diagnosis of COVID-19 among adult patients: a validation and agreement study.BMC infectious diseases · 2022Pooled it
- Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19.The Cochrane database of systematic reviews · 2021Pooled it
- New Simplified White Blood Cells Score Improves Mortality Prediction in Severe COVID-19 Patients.Journal of clinical medicine · 2026Article
- Article
- Eosinophils as potential biomarkers in respiratory viral infections.Frontiers in immunology · 2023Review
- Article
- Machine learning approaches in Covid-19 severity risk prediction in Morocco.Journal of big data · 2022Article
- Prediction of COVID-19 using long short-term memory by integrating principal component analysis and clustering techniques.Informatics in medicine unlocked · 2022Article
- Symptom-Based COVID-19 Prognosis through AI-Based IoT: A Bioinformatics Approach.BioMed research international · 2022Article
- Asthma and COVID-19: an update.European respiratory review : an official journal of the European Respiratory Society · 2021Review
- CT Examinations for COVID-19: A Systematic Review of Protocols, Radiation Dose, and Numbers Needed to Diagnose and Predict.Taehan Yongsang Uihakhoe chi · 2021Article
- Assessing the capacity of symptom scores to predict COVID-19 positivity in Nigeria: a national derivation and validation cohort study.BMJ open · 2021Article
- A systematic review of prediction models to diagnose COVID-19 in adults admitted to healthcare centers.Archives of public health = Archives belges de sante publique · 2021Review
- Eosinophils and COVID-19: diagnosis, prognosis, and vaccination strategies.Seminars in immunopathology · 2021Review
- Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review.Informatics in medicine unlocked · 2021Review
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
introductionIn numerous countries, large population testing is impossible due to the limited availability of RT-PCR kits and CT-scans. This study aimed to determine a pre-test probability score for SARS-CoV-2 infection.
methodsThis multicenter retrospective study (4 University Hospitals) included patients with clinical suspicion of SARS-CoV-2 infection. Demographic characteristics, clinical symptoms, and results of blood tests (complete white blood cell count, serum electrolytes and CRP) were collected. A pre-test probability score was derived from univariate analyses of clinical and biological variables between patients and controls, followed by multivariate binary logistic analysis to determine the independent variables associated with SARS-CoV-2 infection.
results605 patients were included between March 10th and April 30th, 2020 (200 patients for the training cohort, 405 consecutive patients for the validation cohort). In the multivariate analysis, lymphocyte (<1.3 G/L), eosinophil (<0.06 G/L), basophil (<0.04 G/L) and neutrophil counts (<5 G/L) were associated with high probability of SARS-CoV-2 infection but no clinical variable was statistically significant. The score had a good performance in the validation cohort (AUC = 0.918 (CI: [0.891-0.946]; STD = 0.014) with a Positive Predictive Value of high-probability score of 93% (95%CI: [0.89-0.96]). Furthermore, a low-probability score excluded SARS-CoV-2 infection with a Negative Predictive Value of 98% (95%CI: [0.93-0.99]). The performance of the score was stable even during the last period of the study (15-30th April) with more controls than infected patients.
conclusionsThe PARIS score has a good performance to categorize the pre-test probability of SARS-CoV-2 infection based on complete white blood cell count. It could help clinicians adapt testing and for rapid triage of patients before test results.
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