B-159 Four Years of Ottawa Hospital Outpatient Chemistry Data Transformed into Seasonally Adjusted Intrapatient Biological, Analytical and Preanalytical Variation
Notice bibliographique
Résumé
Abstract Background The European Federation for Laboratory Medicine has conducted series of short term, biologic variation studies (7 to 10 weekly blood draws). This approach has significant shortcomings including small number of nonrandomly selected subjects, short periods of repeated samplings, rigid exclusion criteria, and the issue of non-intervening seasonal variation. Not only is seasonal variation associated with significant changes of blood and plasma volumes, but also with altered activity levels and food intake. We used a numerical approach (PMID:35137000) to derive the seasonal biologic variation of selected outpatient chemistry analytes. We compare our estimates to those of the EFLM. Methods De-identified Siemens Vista sodium, chloride, bicarbonate (CO2), potassium, creatinine, urea, and phosphate results were obtained from Ottawa Hospital outpatients for November 2009 to January 2014. All measurements were identified with unique patient identifiers and date and time of testing. Results outside their reference interval were excluded from analysis. For each season and test, we tabulated consecutive presumably nonemergent intrapatient paired results that were analyzed between 6am and 6pm on weekdays and separated by up to 2190 hr (3 months). The separation time (hours) was converted into a modulus 24 time yielding hourly intervals of separation from 0 to 1 hr to 23 to 24 hours. For each interval, we determined the standard deviation of duplicates (SDD) between the paired intrapatient results. SDD was graphed against the midpoints of the hourly separation and linear regression was used to determine the y-intercept which constitutes the total of analytical, intra-patient biological and preanalytical variation for each season. The y-intercept was normalized with the grand mean to provide the total seasonal intrapatient CVanalytical+biological+preanalytical. We compared these total patient seasonal variations to the magnitude of the sum of the EFLM intrapatient CV and their usual analytical variation. Results For electrolytes, 95,000 outpatients provided 288,000 electrolyte panels; mean age was 58.5(SD=18.5) years. Between 1000 and 2000 paired observations were used to determine each electrolyte intrapatient total seasonal CVi. The Figure compares the seasonal variation to the EFLM CVi with added analytical variation, based on that of the Roche Cobas. The average standard errors follow: potassium (5.9%), CO2 (8.8%), chloride (4.8%), sodium (1.4%), creatinine (3.1%), urea (8.6%) and phosphate (9.3%). All the outpatient biological variations demonstrate seasonality. The EFLM sodium, chloride and urea variations are close to, but less than the total seasonal CV. The CO2 graph demonstrates higher variation in the winter and spring, coincident with increased respiratory infections and respiratory distress. Conclusion Originally, the EFLM normal subject variation database was used appropriately to determine optimal analytical performance. Despite the bountiful literature on seasonal variation, the database’s purpose has morphed to an all-season tool for determining reference change values of (normal) subjects and recently, to monitoring (normal) subject variation. With these newer applications, the EFLM variation estimates have become less fit for purpose. We must embrace the transformation of real patient data into useful information that will improve patient care.
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 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 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 ».