A-223 Laboratory Process Tracker (LPT): a tool for real-time tracking of samples, instruments, and workflow steps used in clinical mass spectrometry testing
Notice bibliographique
Résumé
Abstract Background Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has seen ever increasing adoption by clinical laboratories. Most MS-based tests are laboratory-developed tests (LDTs). Common pre-analytical workflows include multiple method-specific steps such as sample aliquoting, extraction, dry-down, reconstitution and data acquisition on LC-MS/MS. Even when individual steps are automated, most laboratories find it challenging to track them to detect and correct errors in real-time. This remains a largely manual process. Here we describe features and benefits of the in-house developed software, Laboratory Process Tracker (LPT), which uses a system of barcodes to enable real-time tracking of LC-MS/MS batches throughout the sample preparation and data acquisition steps. Methods LPT is software developed using .NET 6 and Visual Studio Code. The LPT software settings were customized to reflect method-specific workflow and step-specific acceptance criteria. The following describes how it works for each method. 1. LPT generates 2D barcode labels that are assigned to each instrument and each trained user. 2. A new batch is created in LPT by scanning the user barcode, selecting the pre-programmed method name, uploading the batch specific sample list, and entering the batch number. The batch-specific barcodes are printed to label primary sample racks and 96-well plates for secondary samples. 3. The batch processing is then tracked step-by-step by scanning the barcodes of instruments, users, and rack/plate(s)/sample at the beginning of each step. LPT flags a step if the value entered fails to meet the passing criteria. The user can determine how to correct the error; it may require restarting the step, the whole batch, or even aborting the batch. 4. Finally, batches with addressed error flags are made available for manual review and sign-off after the batch is completed. Batches without errors are auto signed off by LPT. Results LPT was extensively validated and has been used in our laboratory for six LC-MS/MS methods since 2022. During this time, LPT has been used to successfully track more than 200 batches of samples per month. Less than 10% of all batches were flagged since they failed one or more acceptance criteria built into the software. The most common errors are due to batch mix-up, one step skipped or repeated, wrong instrument used, and processing time not matching the time allowance. Since its implementation, LPT has helped the lab achieve time and cost savings in error detection and mitigation. The additional benefits of LPT include a daily dashboard for tracking the status of all batches, summary of common operation errors, help with investigation and troubleshooting. Conclusion LPT software is an end-to-end pre-analytical workflow tracking tool. It is intuitive and user-friendly. In our clinical MS laboratory, it contributes to quality improvement, risk management and cost reduction. Collaboration among operations, clinical/scientific, and IT teams is essential in development, validation and ongoing improvement of the tool. We propose that software like LPT could be applied to any clinical laboratory workflow that includes a linear sequence of steps if gaps exist in process tracking.
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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 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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.
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