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A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS
HS. Oster, S. Crouch, A. Smith, G. Yu, B. Abu Shrkihe, S. Baruch, A. Kolomansky, J. Ben-Ezra, S. Naor, P. Fenaux, A. Symeonidis, R. Stauder, J. Cermak, G. Sanz, E. Hellström-Lindberg, L. Malcovati, S. Langemeijer, U. Germing, MS. Holm, K. Madry,...
Jazyk angličtina Země Spojené státy americké
Typ dokumentu časopisecké články
NLK
Directory of Open Access Journals
od 2016
PubMed Central
od 2016
Europe PubMed Central
od 2016
ROAD: Directory of Open Access Scholarly Resources
od 2016
- MeSH
- algoritmy MeSH
- laboratoře MeSH
- lidé MeSH
- myelodysplastické syndromy * diagnóza MeSH
- nemoci kostní dřeně * MeSH
- vyšetřování kostní dřeně MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
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.
Department of Haematology Aberdeen Royal Infirmary Aberdeen United Kingdom
Department of Haematology Oncology and Internal Medicine Warsaw Medical University Warsaw Poland
Department of Hematology Aarhus University Hospital Aarhus Denmark
Department of Hematology Radboudumc Nijmegen The Netherlands
Department of Internal Medicine 5 Innsbruck Medical University Innsbruck Austria
Department of Medicine Tel Aviv Sourasky Medical Center Tel Aviv Israel
Department of Pathology Tel Aviv Sourasky Medical Center Tel Aviv Israel
Division of Hematology Department of Medicine Karolinska Institutet Stockholm Sweden
Hematology Department Hospital Universitario y Politécnico La Fe Valencia Spain
Sackler Faculty of Medicine Tel Aviv University Tel Aviv Israel
Service d'Hématologie Centre Hospitalier de Perpignan Perpignan France
Service d'Hématologie Centre Hospitalier Universitaire Brabois Vandoeuvre Nancy France
St James's Institute of Oncology The Leeds Teaching Hospitals NHS Trust Leeds United Kingdom
Citace poskytuje Crossref.org
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- $a Oster, Howard S $u Department of Medicine, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel $u Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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- $a A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS / $c HS. Oster, S. Crouch, A. Smith, G. Yu, B. Abu Shrkihe, S. Baruch, A. Kolomansky, J. Ben-Ezra, S. Naor, P. Fenaux, A. Symeonidis, R. Stauder, J. Cermak, G. Sanz, E. Hellström-Lindberg, L. Malcovati, S. Langemeijer, U. Germing, MS. Holm, K. Madry, A. Guerci-Bresler, D. Culligan, L. Sanhes, J. Mills, I. Kotsianidis, C. van Marrewijk, D. Bowen, T. de Witte, M. Mittelman
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- $a 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.
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