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

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 contr...

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Vydáno v:	Blood advances Ročník 5; číslo 16; s. 3066 - 3075
Hlavní autoři:	Oster, Howard S., Crouch, Simon, Smith, Alexandra, Yu, Ge, Abu Shrkihe, Bander, Baruch, Shoham, Kolomansky, Albert, Ben-Ezra, Jonathan, Naor, Shachar, Fenaux, Pierre, Symeonidis, Argiris, Stauder, Reinhard, Cermak, Jaroslav, Sanz, Guillermo, Hellström-Lindberg, Eva, Malcovati, Luca, Langemeijer, Saskia, Germing, Ulrich, Holm, Mette Skov, Madry, Krzysztof, Guerci-Bresler, Agnes, Culligan, Dominic, Sanhes, Laurence, Mills, Juliet, Kotsianidis, Ioannis, van Marrewijk, Corine, Bowen, David, de Witte, Theo, Mittelman, Moshe
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Washington, DC Elsevier Inc 24.08.2021 American Society of Hematology
Témata:	Myeloid Neoplasia
ISSN:	2473-9529, 2473-9537, 2473-9537
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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. •A BM examination is the gold standard for the diagnosis of MDS, but it is invasive and subjective.•A predictive algorithm/app using data of 10 readily available parameters from 1004 subjects was developed to help diagnose/rule out MDS. [Display omitted]
AbstractList	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. •A BM examination is the gold standard for the diagnosis of MDS, but it is invasive and subjective.•A predictive algorithm/app using data of 10 readily available parameters from 1004 subjects was developed to help diagnose/rule out MDS. [Display omitted] 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. A BM examination is the gold standard for the diagnosis of MDS, but it is invasive and subjective.A predictive algorithm/app using data of 10 readily available parameters from 1004 subjects was developed to help diagnose/rule out MDS. 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. 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.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.
Author	Stauder, Reinhard Baruch, Shoham Guerci-Bresler, Agnes de Witte, Theo Hellström-Lindberg, Eva Crouch, Simon Yu, Ge Fenaux, Pierre Kolomansky, Albert Holm, Mette Skov Oster, Howard S. Malcovati, Luca Bowen, David Naor, Shachar Mills, Juliet Symeonidis, Argiris Sanz, Guillermo Mittelman, Moshe Ben-Ezra, Jonathan van Marrewijk, Corine Langemeijer, Saskia Culligan, Dominic Smith, Alexandra Sanhes, Laurence Cermak, Jaroslav Kotsianidis, Ioannis Madry, Krzysztof Abu Shrkihe, Bander Germing, Ulrich
AuthorAffiliation	17 Service d’Hématologie, Centre Hospitalier Universitaire (CHU) Brabois Vandoeuvre, Nancy, France 5 Department of Pathology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel 2 Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel 21 Department of Hematology, Democritus University of Thrace Medical School, University Hospital of Alexandroupolis, Alexandroupolis, Greece 20 Department of Haematology, Worcestershire Acute Hospitals National Health Service (NHS) Trust and University of Birmingham, Birmingham, United Kingdom 1 Department of Medicine, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel 11 Division of Hematology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden 16 Department of Haematology, Oncology and Internal Medicine, Warsaw Medical University, Warsaw, Poland 23 Department of Tumor Immunology, Nijmegen Center for Molecular Life Sciences, Radboudumc, Nijmegen, The Netherlands 7 Division Hematology, Department of Internal Medicine, University of Patras
AuthorAffiliation_xml	– name: 4 Department of Cell and Developmental Biology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel – name: 3 Epidemiology and Cancer Statistics Group, Department of Health Sciences, University of York, York, United Kingdom – name: 18 Department of Haematology, Aberdeen Royal Infirmary, Aberdeen, United Kingdom – name: 10 Hematology Department, Hospital Universitario y Politécnico La Fe, Valencia, Spain – name: 17 Service d’Hématologie, Centre Hospitalier Universitaire (CHU) Brabois Vandoeuvre, Nancy, France – name: 9 Department of Clinical Hematology, Institute of Hematology and Blood Transfusion, Prague, Czech Republic – name: 6 Service d’Hématologie Séniors, Hôpital Saint-Louis, Assistance Publique des Hôpitaux de Paris (AP-HP) and Université Paris 7, Paris, France – name: 20 Department of Haematology, Worcestershire Acute Hospitals National Health Service (NHS) Trust and University of Birmingham, Birmingham, United Kingdom – name: 2 Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel – name: 21 Department of Hematology, Democritus University of Thrace Medical School, University Hospital of Alexandroupolis, Alexandroupolis, Greece – name: 1 Department of Medicine, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel – name: 13 Department of Hematology, Radboudumc, Nijmegen, The Netherlands – name: 8 Department of Internal Medicine V (Hematology and Oncology), Innsbruck Medical University, Innsbruck, Austria – name: 16 Department of Haematology, Oncology and Internal Medicine, Warsaw Medical University, Warsaw, Poland – name: 7 Division Hematology, Department of Internal Medicine, University of Patras Medical School, Patras, Greece – name: 11 Division of Hematology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden – name: 14 Department of Hematology, Oncology and Clinical Immunology, Universitätsklinik Düsseldorf, Düsseldorf, Germany – name: 19 Service d’Hématologie, Centre Hospitalier de Perpignan, Perpignan, France – name: 22 St. James's Institute of Oncology, The Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom; and – name: 15 Department of Hematology, Aarhus University Hospital, Aarhus, Denmark – name: 12 Department of Molecular Medicine and Hematology Oncology, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Policlinico San Matteo, University of Pavia, Pavia, Italy – name: 23 Department of Tumor Immunology, Nijmegen Center for Molecular Life Sciences, Radboudumc, Nijmegen, The Netherlands – name: 5 Department of Pathology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
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PublicationDate	2021-08-24
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PublicationTitle	Blood advances
PublicationYear	2021
Publisher	Elsevier Inc American Society of Hematology
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10.1182/blood-2020-139412
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Snippet	We present a noninvasive Web-based app to help exclude or diagnose myelodysplastic syndrome (MDS), a bone marrow (BM) disorder with cytopenias and leukemic... A BM examination is the gold standard for the diagnosis of MDS, but it is invasive and subjective.A predictive algorithm/app using data of 10 readily available...
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SubjectTerms	Myeloid Neoplasia
Title	A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS
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