Do we Need to Perform Bone Marrow Examination in all Subjects Suspected of MDS? Evaluation and Validation of Non‐Invasive (Web‐Based) Diagnostic Algorithm

ABSTRACT Background Bone marrow examination (BME) is the gold standard of diagnosing myelodysplastic syndromes (MDS). Problems: it is invasive, painful, causing possible bleeding, inaccurate (aspirate hemodilution), and subjective (inter‐observer interpretation discordance). We developed non‐invasiv...

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Vydané v:European journal of haematology Ročník 114; číslo 4; s. 672 - 678
Hlavní autori: Oster, Howard S., Polakow, Ariel M., Gat, Roi, Goldschmidt, Noa, Ben‐Ezra, Jonathan, Mittelman, Moshe
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Wiley Subscription Services, Inc 01.04.2025
Predmet:
Aged
Aged, 80 and over
Algorithms
Bone marrow
Bone Marrow - pathology
Bone Marrow Examination - methods
Creatinine
diagnostic model
Discordance
Female
gradient boosted model
Humans
Internet
Male
Middle Aged
Monocytes
Morphometry
Myelodysplastic syndrome
myelodysplastic syndromes
Myelodysplastic Syndromes - blood
Myelodysplastic Syndromes - diagnosis
Myelodysplastic Syndromes - etiology
Peripheral blood
Registries
validation
diagnostic model
gradient boosted model
validation
myelodysplastic syndromes
ISSN:0902-4441, 1600-0609, 1600-0609
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Shrnutí:ABSTRACT Background Bone marrow examination (BME) is the gold standard of diagnosing myelodysplastic syndromes (MDS). Problems: it is invasive, painful, causing possible bleeding, inaccurate (aspirate hemodilution), and subjective (inter‐observer interpretation discordance). We developed non‐invasive diagnostic tools: A logistic regression formula [LeukRes 2018], then a web algorithm using 10 variables (age, gender, Hb, MCV, WBC, ANC, monocytes, PLT, glucose, creatinine) to diagnose/exclude MDS [BldAdv 2021]. Here, we perform external validation of the model. Methods From the TASMC BM registry (2019–22) we identified and compared the model performance between MDS patients and controls (> 50 year with unexplained anemia, not MDS), all BME diagnosed, and not used in model building. Results The model was accurate and predicted MDS in 63% of 103 patients, and excluded (correctly) in 83% of 101 controls. It miss‐classified in 11%/7% respectively, and was indeterminate in 26%/10% respectively. The positive predictive value (PPV), NPV, sensitivity, and specificity (excluding the indeterminate group) were 90%, 88%, 86%, and 92%, respectively. Subgroup (Lower/higher risk, LR/HR) analysis results were similar. Conclusions The MDS diagnostic model was validated and can be used, mainly for MDS exclusion, especially in suspected LR‐MDS, avoiding BME in some patients. In the future incorporating peripheral blood genetics and morphometry can further improve the model.
Bibliografia:The authors received no specific funding for this work.
Funding
Howard S. Oster and Ariel M. Polakow contributed equally to this study.
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ISSN:0902-4441
1600-0609
1600-0609
DOI:10.1111/ejh.14379