B-136 From Result to Response: The Development of Laboratory-Based Scoring Models to Predict COVID-19 Patient Outcomes
Notice bibliographique
Résumé
Abstract Background Patient characteristics relating to increased risk for COVID-19 severity are well-documented, including older age and pre-existing health concerns; however, methods to accurately predict patients’ acute reaction to SARS-CoV-2 infections remain lacking. Identification of specific laboratory tests informative of patient outcomes could effectively screen incoming patients and better inform physicians regarding optimal treatment plans. The aim of this study was to examine associations between frequently ordered laboratory markers and COVID-19 patient mortality to develop an objective laboratory-based scoring system that estimates patients’ risk of mortality. Methods Study participants included inpatients from 3 hospitals recruited into the GENCOV project based in Ontario, Canada. Participants (n=325) were at least 18 years of age, provided consent, and were hospitalized within 1 month of having a PCR confirmed COVID-19 infection between January 2020 and February 2022. Extensive clinical data including patient demographics, laboratory results, and treatment outcomes were extracted from patient’s electronic medical records (EMR). Results for 32 biochemical and hematological tests including complete blood cell counts, coagulation (activated partial thromboplastin time, D-dimer, fibrinogen, prothrombin time), general chemistry (albumin, blood gases, creatine kinase, electrolytes, glucose, triglycerides), inflammatory (lactate dehydrogenase, ferritin, C-reactive protein), liver (alanine aminotransferase, aspartate aminotransferase, total bilirubin), renal (creatinine, urea) and cardiac (troponin, NT-proBNP, BNP) markers were collected from each chart. Univariable logistic regression with nested likelihood ratio tests were used to determine significant associations between laboratory results and patient outcomes. Significant markers were incorporated into multivariable models and variable risk values were assigned. Total calculated risk scores for each participant were compared using univariable and multivariable risk values. Validation of each scoring method was performed on a 20% subset of inpatients. Receiver operating characteristic (ROC) curves evaluated model performance. Results Six laboratory markers were associated with COVID-19 patient mortality when controlling for age and sex. Univariable regression showed that elevated creatinine, elevated lactate, elevated white blood cell, low base excess, low bicarbonate, or low pH results upon admission were significantly associated with increased odds of mortality, in comparison to test results within the reference range for these same markers. Multivariable regression revealed fewer markers (only low bicarbonate and low base excess) showing significant associations with COVID-19 mortality. Area under the ROC curves (AUC) determined that risk scores derived from univariable values performed similarly to multivariable values in validation (0.800 vs 0.802) and development cohorts (0.821 vs 0.829). Total risk scores calculated from the univariable vs multivariable models suggest higher sensitivity (90% vs 85%) than specificity (58% vs 67%) in the validation cohort, whereas the development cohort had higher sensitivity (88%) than specificity (66%) with univariable scores, and higher specificity (81%) than sensitivity (74%) with multivariable scores. Conclusions Laboratory results associated with COVID-19 mortality following both univariable and multivariable analyses suggest hospitalized patients infected with SARS-CoV-2 may present with acidosis. Total risk scores derived from univariable and multivariable values performed similarly in predicting mortality; however, differences in marker significance between models indicate discrepancies with risk interpretation. Further comparison of the created risk score assessments with equivalent models in other populations is warranted.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».