Estimating the Prevalence of Myelodysplasia: A Retrospective Review of Bone Marrow Histopathology in 322 Cases of Unexplained Cytopenia(s) in a Teaching Hospital.
Notice bibliographique
Résumé
Abstract The incidence of MDS is estimated to be 20–50 per 100,000 per year in people 60 years or older. However, the prevalence of this disease has not been determined. In an analysis of data from the National Health and Nutrition Examination Survey (NHANES), the overall prevalence of anaemia in persons 65 years and over in the U.S.A. was estimated to be 10.6% (2.0% for severe anemia, defined as hgb < 110 g/L). Patients with ‘unexplained anaemia’ (UA) accounted for 33.3% of all cases; of these, at least one peripheral blood feature suggesting MDS was present in 17.2%, representing 5.8% of the total anaemic population. To further refine this estimate, we evaluated the frequency of confirmed and suspected MDS diagnoses in a 4 year retrospective survey of bone marrows (BM) done at a single tertiary care institution to investigate unexplained uni, bi or tri-cytopenias. Methods: Only bone marrows performed to investigate unexplained cytopenia(s) were included. We excluded all outside referrals and bone marrows done for staging or remission assessment in patients with preexisting or strongly suspected hemato-lymphoid diagnoses. Electronic charts were reviewed for possible concurrent confounding risk factors such as nutritional deficiencies, hypothyroidism, renal insufficiency, malignancies or inflammatory/infectious conditions. Selected hematologic parameters such as mean corpuscular volume (MCV), red cell distribution width (RDW), hemoglobin (hgb), reticulocyte count etc. at time of bone marrow were recorded. Marrow reports were graded as confirmed (FAB or WHO classification) or suspected MDS, non-diagnostic, normal or other. Results: 322/2267 (14%) bone marrows met our inclusion criteria. The median age at BM was 70 yrs with 65% > age 65. Reasons for undergoing BM included anemia (33.5%), thrombocytopenia (9.0%), neutropenia (7.4%), >1 cytopenia (44.7%) and other (5.3%). One hundred and fifty five (48.4%) had concurrent risk factors for cytopenias that included nutritional deficiencies (8.4%), hypothyroidism (7.7%), renal insufficiency (42.6%), cancer (27%), infection and chronic inflammation (35%). Overall, 21% of all BM had confirmed MDS, 13% suspected MDS. Excluding patients with confounding risk factors, 24% (31% age > 65) had confirmed and 15% (18% age > 65) had suspected MDS. Of red cell parameters (hgb, MCV and RDW ), only the MCV was predictive of MDS in patients without risk factors, p=0.031. Overall, of 72 patients aged > 65 with UA as defined by NHANES, 35% had confirmed MDS, and 15% had suspected MDS. Conclusions: In patients with unexplained anaemia who undergo BM evaluation, the frequency of confirmed and suspected MDS is high (19%), and increases with age > 65 (50%). This figure is significantly higher than the estimate of 17.2% derived from analysis of the NHANES data and, since it is based upon histopathological analysis of BM rather than indirect evidence from blood counts, may be more accurate. Extrapolation to the Canadian and U.S. populations leads to an estimated prevalence of MDS of over 21,000 cases in people >65 in Canada, and over 210,000 in the U.S. Notwithstanding the limitations of this retrospective study, the potential impact of new MDS therapeutics, both on disease and pharmaco-economic burden may therefore be much greater than hitherto anticipated. With an aging population, more accurate prospective incidence and prevalence data for MDS are needed.
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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 | 0,001 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 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 | 0,001 |
| 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 ».