Validation of a Scoring System to Establish the Pretest Probability of Myelodysplastic Syndrome in Patients with Unexplained Cytopenias or Macrocytosis.
Bibliographic record
Abstract
Abstract Abstract 1761 Poster Board I-787 Introduction Myelodysplastic syndromes (MDS) are clonal hematopoietic stem cell disorders characterized by ineffective hematopoiesis culminating in peripheral blood cytopenias and a propensity to Acute Myelogenous Leukemia (AML). The gold standard diagnostic test is the bone marrow aspirate and biopsy but clinicians are often hesitant to refer patients with unexplained cytopenias or macrocytosis for this test. Consequently undiagnosed patients are deprived access to effective treatments. Previously (Buckstein et al., Leukemia Research 33 (2009) 1313–1318) we identified factors which were independently predictive of diagnosing MDS at the time of bone marrow, including; age, mean corpuscular volume (MCV), red cell distribution (RDW), and lactate dehydrogenase (LDH). In this current study, we set out to validate our findings and determine the sensitivity and specificity of our scoring system for routine clinical practise. Methods We reviewed all bone marrow reports conducted at a tertiary care center for the years (January through December) 2006–2008 inclusive. Our inclusion criteria included bone marrows done for unexplained cytopenias and/or macrocytosis. We excluded all outside consultative referrals and bone marrows done for staging or remission assessment in patients with pre-existing or strongly suspected hemato-lymphoid diagnoses. In cases where the patient had more than one bone marrow, only the most recent marrow was used. The bone marrows were reviewed by two experienced hematopathologists, and the diagnosis of MDS was made using the WHO and/or FAB classification systems. Marrows were classified as ‘confirmed MDS’, ‘suspected MDS’ or ‘Not MDS’. Electronic patient charts were reviewed to determine the age, MCV, LDH and RDW at the time of diagnostic bone marrow. A factor was considered positive if the age was > 65 or if its value exceeded the upper range of normal for our laboratory standards (MCV > 96 fl, LDH > 250 IU/L, RDW >14.5%). All patients for whom we had 4 recorded factors were then assigned a score that ranged from 0 to 4. We determined the distribution of scores within this population, and the sensitivity and specificity of this scoring method in predicting a histopathological diagnosis of MDS. Results Three-hundred and forty bone marrows met our inclusion criteria, and 289 (85%) had all four factors recorded at the time of diagnosis. The predictive ability of the MDS score is summarized in Table 1. The probability of diagnosing MDS increased from 8% with a score of 0 to 46% with a score of 3 or 4. A similar trend was seen when all 344 marrows were analysed. Our scoring system had high sensitivity (> 96%), when only one predictive factor was present and high specificity (96%) when 4 predictive factors were present at the time of diagnostic marrow. (Table 2) Conclusions In patients with unexplained cytopenias, or macrocytosis undergoing a diagnostic BM test, the pre-test probability of a MDS diagnosis increases as the number of predictive factors increases. The specificity of our MDS score also follows a similar trend. The sensitivity is high with only one positive factor indicating a lower score can be used as a screening tool. We have effectively validated a scoring system that may help clinicians decide on the utility of arranging diagnostic bone marrow examinations for patients with undiagnosed cytopenias and macrocytosis. Disclosures Buckstein: Celgene : Honoraria; Novartis: Honoraria.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".