40 Increasing the between testing interval to 365 days as guided by biological variation
Notice bibliographique
Résumé
Objectives There is bountiful evidence of ambulatory patient over-testing, both in terms of the tests ordered and their frequency. To be thorough and efficient, many physicians pay short shrift to sustainable test ordering. We have developed a calculus that transforms sequential intra-patient test results into total variation comprising its preanalytical, analytical and biological components. We have used this approach to compare the analytical and clinical performance of multiple analytical systems. Here, we determine the total test variation in patients with test separation intervals of 90, 180, 270 and 365 days. We demonstrate that annual testing with phlebotomies scheduled at the same time of day minimizes the variation of the sequential test results and permits optimal discrimination of a statistically significant change in the test results. Method We studied 5 years (2014-2019) of Ottawa Hospital adult outpatient general chemistry test pairs of albumin, alanine aminotransferase, aspartate aminotransferase, calcium, chloride, bicarbonate, creatinine, potassium, magnesium, sodium, phosphate, total bilirubin and urea. Intrapatient data pairs (separated by 88-92, 178-182, 268-272 and 363-367 days), were divided into 2 groups: those sampled on weekdays at the same time of day (+ 2 hr) and those with 3 to 8 hr sampling differences . The between pair variations were derived calculated with Dahlberg’s formula. Results The median numbers of patient pairs/analyte for the matched sampling times were 1034 (90 days), 696 (180 days), 84 (270 days) and 364 (365 days). For the unmatched sampling times, the median numbers of patient pairs were much smaller, ranging between 38 to 290 and their average variations exceeded the matched pair variations by as much as 30% (phosphate and creatinine). The matched pair 90, 180, 270 and 365 day variations were highly correlated (r ranged from 0.965 to 0.977). Compared to the other periods, the 180 day variations were smaller (-7.5%) and the 270 day variations were larger (+11.9%). Overall, the 365 day magnitudes of variations were closest to the magnitudes of variation of the other periods. Conclusions At least £2.2 billion are spent annually on UK pathology services, a significant portion wasted on suboptimal testing including: 1) tests are ordered too closely, providing little new information 2) tests that are ordered 9 months later and have been influenced by seasonal factors or 3) sequential tests that are ordered at different times of the day and are influenced by diurnal variation. Biological variation can be a guide to reducing test wastage and setting a standard for testing intervals that are minimally one year with the patient being sampled at the same time of day. Simple algorithms incorporating minimal sequential data would be able to discern real laboratory trends far more readily than ‘thorough and efficient’ but over-testing clinicians.
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 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 | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».