Toggle Main Menu Toggle Search

Open Access padlockePrints

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

Lookup NU author(s): Dr Ge YuORCiD

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


Abstract

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). Gradient-boosted 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.


Publication metadata

Author(s): Oster HS, Crouch S, Smith A, Yu G, Shrkihe BA, Baruch S, Kolomansky A, BenEzra J, Naor S, Fenaux P, Symeonidis A, Stauder R, Cermak J, Sanz G, Hellstrom-Lindberg E, Malcovati L, Langemeijer S, Germing U, Holm M, Madry K, Guerci-Bresler A, Culligan D, Sanhes L, Mills J, Kotsianidis I, Marrewijk C, Bowen D, Witte TD, Mittelman M

Publication type: Article

Publication status: Published

Journal: Blood advances

Year: 2021

Volume: 5

Issue: 16

Pages: 3066-3075

Online publication date: 24/08/2021

Acceptance date: 24/08/2021

ISSN (electronic): 2473-9529

Publisher: American Society of Hematology

URL: https://doi.org/10.1182/bloodadvances.2020004055

DOI: 10.1182/bloodadvances.2020004055


Altmetrics

Altmetrics provided by Altmetric


Share