Accuracy of routine laboratory tests to predict mortality and deterioration to severe or critical COVID-19 in people with SARS-CoV-2
Notice bibliographique
Résumé
BACKGROUND: Identifying patients with COVID-19 disease who will deteriorate can be useful to assess whether they should receive intensive care, or whether they can be treated in a less intensive way or through outpatient care. In clinical care, routine laboratory markers, such as C-reactive protein, are used to assess a person's health status. OBJECTIVES: To assess the accuracy of routine blood-based laboratory tests to predict mortality and deterioration to severe or critical (from mild or moderate) COVID-19 in people with SARS-CoV-2. SEARCH METHODS: On 25 August 2022, we searched the Cochrane COVID-19 Study Register, encompassing searches of various databases such as MEDLINE via PubMed, CENTRAL, Embase, medRxiv, and ClinicalTrials.gov. We did not apply any language restrictions. SELECTION CRITERIA: We included studies of all designs that produced estimates of prognostic accuracy in participants who presented to outpatient services, or were admitted to general hospital wards with confirmed SARS-CoV-2 infection, and studies that were based on serum banks of samples from people. All routine blood-based laboratory tests performed during the first encounter were included. We included any reference standard used to define deterioration to severe or critical disease that was provided by the authors. DATA COLLECTION AND ANALYSIS: Two review authors independently extracted data from each included study, and independently assessed the methodological quality using the Quality Assessment of Prognostic Accuracy Studies tool. As studies reported different thresholds for the same test, we used the Hierarchical Summary Receiver Operator Curve model for meta-analyses to estimate summary curves in SAS 9.4. We estimated the sensitivity at points on the SROC curves that corresponded to the median and interquartile range boundaries of specificities in the included studies. Direct and indirect comparisons were exclusively conducted for biomarkers with an estimated sensitivity and 95% CI of ≥ 50% at a specificity of ≥ 50%. The relative diagnostic odds ratio was calculated as a summary of the relative accuracy of these biomarkers. MAIN RESULTS: We identified a total of 64 studies, including 71,170 participants, of which 8169 participants died, and 4031 participants deteriorated to severe/critical condition. The studies assessed 53 different laboratory tests. For some tests, both increases and decreases relative to the normal range were included. There was important heterogeneity between tests and their cut-off values. None of the included studies had a low risk of bias or low concern for applicability for all domains. None of the tests included in this review demonstrated high sensitivity or specificity, or both. The five tests with summary sensitivity and specificity above 50% were: C-reactive protein increase, neutrophil-to-lymphocyte ratio increase, lymphocyte count decrease, d-dimer increase, and lactate dehydrogenase increase. Inflammation For mortality, summary sensitivity of a C-reactive protein increase was 76% (95% CI 73% to 79%) at median specificity, 59% (low-certainty evidence). For deterioration, summary sensitivity was 78% (95% CI 67% to 86%) at median specificity, 72% (very low-certainty evidence). For the combined outcome of mortality or deterioration, or both, summary sensitivity was 70% (95% CI 49% to 85%) at median specificity, 60% (very low-certainty evidence). For mortality, summary sensitivity of an increase in neutrophil-to-lymphocyte ratio was 69% (95% CI 66% to 72%) at median specificity, 63% (very low-certainty evidence). For deterioration, summary sensitivity was 75% (95% CI 59% to 87%) at median specificity, 71% (very low-certainty evidence). For mortality, summary sensitivity of a decrease in lymphocyte count was 67% (95% CI 56% to 77%) at median specificity, 61% (very low-certainty evidence). For deterioration, summary sensitivity of a decrease in lymphocyte count was 69% (95% CI 60% to 76%) at median specificity, 67% (very low-certainty evidence). For the combined outcome, summary sensitivity was 83% (95% CI 67% to 92%) at median specificity, 29% (very low-certainty evidence). For mortality, summary sensitivity of a lactate dehydrogenase increase was 82% (95% CI 66% to 91%) at median specificity, 60% (very low-certainty evidence). For deterioration, summary sensitivity of a lactate dehydrogenase increase was 79% (95% CI 76% to 82%) at median specificity, 66% (low-certainty evidence). For the combined outcome, summary sensitivity was 69% (95% CI 51% to 82%) at median specificity, 62% (very low-certainty evidence). Hypercoagulability For mortality, summary sensitivity of a d-dimer increase was 70% (95% CI 64% to 76%) at median specificity of 56% (very low-certainty evidence). For deterioration, summary sensitivity was 65% (95% CI 56% to 74%) at median specificity of 63% (very low-certainty evidence). For the combined outcome, summary sensitivity was 65% (95% CI 52% to 76%) at median specificity of 54% (very low-certainty evidence). To predict mortality, neutrophil-to-lymphocyte ratio increase had higher accuracy compared to d-dimer increase (RDOR (diagnostic Odds Ratio) 2.05, 95% CI 1.30 to 3.24), C-reactive protein increase (RDOR 2.64, 95% CI 2.09 to 3.33), and lymphocyte count decrease (RDOR 2.63, 95% CI 1.55 to 4.46). D-dimer increase had higher accuracy compared to lymphocyte count decrease (RDOR 1.49, 95% CI 1.23 to 1.80), C-reactive protein increase (RDOR 1.31, 95% CI 1.03 to 1.65), and lactate dehydrogenase increase (RDOR 1.42, 95% CI 1.05 to 1.90). Additionally, lactate dehydrogenase increase had higher accuracy compared to lymphocyte count decrease (RDOR 1.30, 95% CI 1.13 to 1.49). To predict deterioration to severe disease, C-reactive protein increase had higher accuracy compared to d-dimer increase (RDOR 1.76, 95% CI 1.25 to 2.50). The neutrophil-to-lymphocyte ratio increase had higher accuracy compared to d-dimer increase (RDOR 2.77, 95% CI 1.58 to 4.84). Lastly, lymphocyte count decrease had higher accuracy compared to d-dimer increase (RDOR 2.10, 95% CI 1.44 to 3.07) and lactate dehydrogenase increase (RDOR 2.22, 95% CI 1.52 to 3.26). AUTHORS' CONCLUSIONS: Laboratory tests, associated with hypercoagulability and hyperinflammatory response, were better at predicting severe disease and mortality in patients with SARS-CoV-2 compared to other laboratory tests. However, to safely rule out severe disease, tests should have high sensitivity (> 90%), and none of the identified laboratory tests met this criterion. In clinical practice, a more comprehensive assessment of a patient's health status is usually required by, for example, incorporating these laboratory tests into clinical prediction rules together with clinical symptoms, radiological findings, and patient's characteristics.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,054 | 0,301 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,010 | 0,014 |
| Bibliométrie | 0,011 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».