A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS

Howard S. Oster, Simon Crouch, Alexandra Smith, Ge Yu, Bander Abu Shrkihe, Shoham Baruch, Albert Kolomansky, Jonathan Ben-Ezra, Shachar Naor, Pierre Fenaux, Argiris Symeonidis, Reinhard Stauder, Jaroslav Cermak, Guillermo Sanz, Eva Hellstrom-Lindberg, Luca Malcovati, Saskia Langemeijer, Ulrich Germing, Mette Skov Holm, Krzysztof MadryAgnes Guerci-Bresler, Dominic Culligan, Laurence Sanhes, Juliet Mills, Ioannis Kotsianidis, Corine Van Marrewijk, David Bowen, Theo De Witte, Moshe Mittelman*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


We present a noninvasive Web-based app to help exclude or diagnose myelodysplastic syndrome (MDS), a bone marrow (BM) disorder with cytopenias and leukemic risk, diagnosed by BM examination. A sample of 502 MDS patients from the European MDS (EUMDS) registry (n . 2600) was combined with 502 controls (all BM proven). Gradientboosted models (GBMs) were used to predict/exclude MDS using demographic, clinical, and laboratory variables. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the models, and performance was validated using 100 times fivefold cross-validation. Model stability was assessed by repeating its fit using different randomly chosen groups of 502 EUMDS cases. AUC was 0.96 (95% confidence interval, 0.95-0.97). MDS is predicted/excluded accurately in 86% of patients with unexplained anemia. A GBM score (range, 0-1) of less than 0.68 (GBM , 0.68) resulted in a negative predictive value of 0.94, that is, MDS was excluded. GBM $ 0.82 provided a positive predictive value of 0.88, that is, MDS. The diagnosis of the remaining patients (0.68 # GBM , 0.82) is indeterminate. The discriminating variables: Age, sex, hemoglobin, white blood cells, platelets, mean corpuscular volume, neutrophils, monocytes, glucose, and creatinine. A Web-based app was developed; physicians could use it to exclude or predict MDS noninvasively in most patients without a BM examination. Future work will add peripheral blood cytogenetics/genetics, EUMDS-based prospective validation, and prognostication.

Original languageEnglish
Pages (from-to)3066-3075
Number of pages10
JournalBlood advances
Issue number16
StatePublished - 24 Aug 2021


FundersFunder number
Amgen Limited
Celgene International
Novartis Pharmacy B.V. Oncology Europe
Janssen Pharmaceuticals
Takeda Pharmaceuticals International


    Dive into the research topics of 'A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS'. Together they form a unique fingerprint.

    Cite this