Literature DB >> 21134982

Risk stratification based on both disease status and extra-hematologic comorbidities in patients with myelodysplastic syndrome.

Matteo G Della Porta1, Luca Malcovati, Corinna Strupp, Ilaria Ambaglio, Andrea Kuendgen, Esther Zipperer, Erica Travaglino, Rosangela Invernizzi, Cristiana Pascutto, Mario Lazzarino, Ulrich Germing, Mario Cazzola.   

Abstract

The incidence of myelodysplastic syndromes increases with age and a high prevalence of co-morbid conditions has been reported in these patients. So far, risk assessment in myelodysplastic syndromes has been mainly based on disease status. We studied the prognostic impact of comorbidity on the natural history of myelodysplastic syndrome with the aim of developing novel tools for risk assessment. The study population included a learning cohort of 840 patients diagnosed with myelodysplastic syndrome in Pavia, Italy, and a validation cohort of 504 patients followed in Duesseldorf, Germany. Information on comorbidity was extracted from detailed review of the patients' medical charts and laboratory values at diagnosis and during the course of the disease. Univariable and multivariable survival analyses with both fixed and time-dependent covariates were performed using Cox's proportional hazards regression models. Comorbidity was present in 54% of patients in the learning cohort. Cardiac disease was the most frequent comorbidity and the main cause of non-leukemic death. In multivariable analysis, comorbidity had a significant impact on both non-leukemic death (P=0.01) and overall survival (P=0.02). Cardiac, liver, renal, pulmonary disease and solid tumors were found to independently affect the risk of non-leukemic death. A time-dependent myelodysplastic syndrome-specific comorbidity index (MDS-CI) was developed for predicting the effect of comorbidity on outcome. This identified three groups of patients which showed significantly different probabilities of non-leukemic death (P<0.001) and survival (P=0.005) also in the validation cohort. Landmark survival analyses at fixed time points from diagnosis showed that the MDS-CI can better define the life expectancy of patients with myelodysplastic syndrome stratified according to the WHO-classification based Prognostic Scoring System (WPSS).Comorbidities have a significant impact on the outcome of patients with myelodysplastic syndrome. Accounting for both disease status by means of the WPSS and comorbidity through the MDS-CI considerably improves risk stratification in myelodysplastic syndromes.

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Year:  2010        PMID: 21134982      PMCID: PMC3046276          DOI: 10.3324/haematol.2010.033506

Source DB:  PubMed          Journal:  Haematologica        ISSN: 0390-6078            Impact factor:   9.941


  35 in total

1.  Allogeneic stem cell transplantation for myelodysplastic syndromes with bone marrow fibrosis.

Authors:  Nicolaus Kröger; Tatjana Zabelina; Anja van Biezen; Ronald Brand; Dietger Niederwieser; Rodrigo Martino; Zi Yi Lim; Francesco Onida; Christoph Schmid; Laurent Garderet; Marie Robin; Michael van Gelder; Reinhard Marks; Argiris Symeonidis; Guido Kobbe; Theo de Witte
Journal:  Haematologica       Date:  2010-10-22       Impact factor: 9.941

2.  Underrepresentation of patients 65 years of age or older in cancer-treatment trials.

Authors:  L F Hutchins; J M Unger; J J Crowley; C A Coltman; K S Albain
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3.  A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.

Authors:  M E Charlson; P Pompei; K L Ales; C R MacKenzie
Journal:  J Chronic Dis       Date:  1987

4.  International scoring system for evaluating prognosis in myelodysplastic syndromes.

Authors:  P Greenberg; C Cox; M M LeBeau; P Fenaux; P Morel; G Sanz; M Sanz; T Vallespi; T Hamblin; D Oscier; K Ohyashiki; K Toyama; C Aul; G Mufti; J Bennett
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5.  Comprehensive geriatric assessment adds information to Eastern Cooperative Oncology Group performance status in elderly cancer patients: an Italian Group for Geriatric Oncology Study.

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Journal:  J Clin Oncol       Date:  2002-01-15       Impact factor: 44.544

Review 6.  The World Health Organization (WHO) classification of the myeloid neoplasms.

Authors:  James W Vardiman; Nancy Lee Harris; Richard D Brunning
Journal:  Blood       Date:  2002-10-01       Impact factor: 22.113

7.  Proposals for the classification of the myelodysplastic syndromes.

Authors:  J M Bennett; D Catovsky; M T Daniel; G Flandrin; D A Galton; H R Gralnick; C Sultan
Journal:  Br J Haematol       Date:  1982-06       Impact factor: 6.998

8.  No increase in age-specific incidence of myelodysplastic syndromes.

Authors:  Ulrich Germing; Corinna Strupp; Andrea Kündgen; David Bowen; Carlo Aul; Rainer Haas; Norbert Gattermann
Journal:  Haematologica       Date:  2004-08       Impact factor: 9.941

9.  Participation in cancer clinical trials: race-, sex-, and age-based disparities.

Authors:  Vivek H Murthy; Harlan M Krumholz; Cary P Gross
Journal:  JAMA       Date:  2004-06-09       Impact factor: 56.272

10.  A decision analysis of allogeneic bone marrow transplantation for the myelodysplastic syndromes: delayed transplantation for low-risk myelodysplasia is associated with improved outcome.

Authors:  Corey S Cutler; Stephanie J Lee; Peter Greenberg; H Joachim Deeg; Waleska S Pérez; Claudio Anasetti; Brian J Bolwell; Mitchell S Cairo; Robert Peter Gale; John P Klein; Hillard M Lazarus; Jane L Liesveld; Philip L McCarthy; Gustavo A Milone; J Douglas Rizzo; Kirk R Schultz; Michael E Trigg; Armand Keating; Daniel J Weisdorf; Joseph H Antin; Mary M Horowitz
Journal:  Blood       Date:  2004-03-23       Impact factor: 22.113

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  63 in total

1.  Clinical evaluation of extra-hematologic comorbidity in myelodysplastic syndromes: ready-to-wear versus made-to-measure tool.

Authors:  Matteo G Della Porta; Ilaria Ambaglio; Marta Ubezio; Erica Travaglino; Cristiana Pascutto; Luca Malcovati
Journal:  Haematologica       Date:  2012-01-22       Impact factor: 9.941

Review 2.  Why methylation is not a marker predictive of response to hypomethylating agents.

Authors:  Maria Teresa Voso; Valeria Santini; Emiliano Fabiani; Luana Fianchi; Marianna Criscuolo; Giulia Falconi; Francesco Guidi; Stefan Hohaus; Giuseppe Leone
Journal:  Haematologica       Date:  2014-04       Impact factor: 9.941

3.  Validation of WHO classification-based Prognostic Scoring System (WPSS) for myelodysplastic syndromes and comparison with the revised International Prognostic Scoring System (IPSS-R). A study of the International Working Group for Prognosis in Myelodysplasia (IWG-PM).

Authors:  M G Della Porta; H Tuechler; L Malcovati; J Schanz; G Sanz; G Garcia-Manero; F Solé; J M Bennett; D Bowen; P Fenaux; F Dreyfus; H Kantarjian; A Kuendgen; A Levis; J Cermak; C Fonatsch; M M Le Beau; M L Slovak; O Krieger; M Luebbert; J Maciejewski; S M M Magalhaes; Y Miyazaki; M Pfeilstöcker; M A Sekeres; W R Sperr; R Stauder; S Tauro; P Valent; T Vallespi; A A van de Loosdrecht; U Germing; D Haase; P L Greenberg; M Cazzola
Journal:  Leukemia       Date:  2015-02-27       Impact factor: 11.528

4.  Deriving comorbidities from medical records using natural language processing.

Authors:  Hojjat Salmasian; Daniel E Freedberg; Carol Friedman
Journal:  J Am Med Inform Assoc       Date:  2013-10-31       Impact factor: 4.497

5.  Impact of tobacco usage on disease outcome in myelodysplastic syndromes.

Authors:  Asmita Mishra; Dana E Rollison; Thomas H Brandon; Najla H Al Ali; Maria Corrales-Yepez; Eric Padron; Pearlie K Epling-Burnette; Jeffrey E Lancet; Alan F List; Rami S Komrokji
Journal:  Leuk Res       Date:  2015-04-07       Impact factor: 3.156

6.  Risk assessment in myelodysplastic syndromes and myelodysplastic/myeloproliferative neoplasms.

Authors:  Mario Cazzola
Journal:  Haematologica       Date:  2011-03       Impact factor: 9.941

7.  Myelodysplastic syndromes with bone marrow fibrosis.

Authors:  Matteo Giovanni Della Porta; Luca Malcovati
Journal:  Haematologica       Date:  2011-02       Impact factor: 9.941

8.  The critical role of comorbidities and polypharmacy in lower risk myelodysplastic patients: is there any difference between countries?

Authors:  Priscila da Silva Mendonça; Ronald Pinheiro Feitosa; Silvia Maria Meira Magalhães
Journal:  Med Oncol       Date:  2018-09-11       Impact factor: 3.064

9.  Factors predicting early mortality after new diagnosis of myelodysplastic syndrome: A population-based study.

Authors:  Annie M Jacobsen; Jenny N Poynter; Michaela R Richardson; Phuong L Nguyen; Betsy Hirsch; Adina Cioc; Michelle A Roesler; Erica D Warlick
Journal:  Eur J Haematol       Date:  2019-05-16       Impact factor: 2.997

Review 10.  Myelodysplastic Syndromes and Acute Myeloid Leukemia in the Elderly.

Authors:  Heidi D Klepin
Journal:  Clin Geriatr Med       Date:  2016-02       Impact factor: 3.076

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