Estimating the Prevalence of Myelodysplasia: A Retrospective Review of Bone Marrow Histopathology in 322 Cases of Unexplained Cytopenia(s) in a Teaching Hospital.
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".