Application of Spectral Density/Periodogram Analysis to Serial Neutrophil Counts to Diagnose Cyclic Neutropenia
Notice bibliographique
Résumé
Abstract Background: Cyclic neutropenia is characterized by oscillatory fluctuations in blood neutrophil counts, usually with nadirs <0.2 x 109/L at approximately 3 week intervals. Visual inspection of graphs of serial counts is usually the basis for diagnosis. Detection of mutations in ELANE is helpful but not diagnostic because of the overlap of the specific mutation patterns with those associated with severe congenital neutropenia. Making the correct diagnosis of cyclic neutropenia is important because these patients are not thought to be at risk of developing myelodysplasia or acute myeloid leukemia (MDS/AML). In contrast, patients with severe congenital neutropenia, whose counts are usually lower, are at risk of developing MDS/AML. Methods: We have implemented a website application for easy and direct data entry of serial blood counts to detect statistically significant periodicities using the Lomb periodogram. Physicians, nurses, other healthcare providers or patients can directly enter the blood count data for analysis on a website to allow immediate visualization of the serial counts and calculation of the probability of statistically significant cycling and the period, i.e., length of the cycle. Results: We have analyzed the counts from 42 patients (21 ELANE positive, 8 ELANE negative, 13 ELANE unknown) enrolled in the Severe Chronic Neutropenia International Registry with a clinical diagnosis of cyclic neutropenia to determine the accuracy of clinical diagnoses based on this form of statistical analysis. Our preliminary results showed that it is easy to learn how to use this program. We estimate that at least 20 counts obtained at 2-3 day intervals for 6 weeks are the minimum needed to detect cyclic neutropenia on a statistically sound basis, while 20-40 counts obtained at 2-3 day intervals over an 8-10 week period was more likely to yield statistical and clinical certainty about the diagnosis. The figure below shows readouts for the periodogram analysis for one patient. It shows the influence of 17 counts versus 31 counts for a patient with the clinical diagnosis of cyclic neutropenia and a mutation in ELANE. The confidence intervals (95%) and (99%) are exceeded for the series of 31 counts but not for the shorter series. The peak, approximate cycle length is 22 days for this series of counts. As of yet, we do not have the sufficient daily count data to determine if more frequent testing (e.g. daily testing) is better than testing every 2-3 days. We are currently testing the patterns of neutrophil fluctuations in patients on G-CSF to see if cyclic neutropenia can be diagnosed in patients that are on (or during) treatment. We have learned that many patients with the clinical diagnosis of CyN do not have sufficient serial blood cell count data to confirm this diagnosis on a statistical basis. Conclusion: We have developed a simple method for making periodogram analysis much more widely available to clinicians and patients on a world-wide basis. Statistical analysis of carefully collected serial data will help to secure the diagnosis of cyclic neutropenia and provide patients with important prognostic information. Figure 1. Figure 1. Disclosures Dale: Amgen: Consultancy, Honoraria, Research Funding.
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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 | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 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) | 0,002 | 0,001 |
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 ».