Cargando…

The Danish Myelodysplastic Syndromes Database: Patient Characteristics and Validity of Data Records

BACKGROUND: The Danish Myelodysplastic Syndromes Database (DMDSD) comprises nearly all patients diagnosed with myelodysplastic syndromes (MDS) in Denmark since 2010. The DMDSD has not yet been used for epidemiological research and the quality of registered variables remains to be investigated. OBJEC...

Descripción completa

Detalles Bibliográficos
Autores principales: Lauritsen, Tine Bichel, Nørgaard, Jan Maxwell, Grønbæk, Kirsten, Vallentin, Anders Pommer, Ahmad, Syed Azhar, Hannig, Louise Hur, Severinsen, Marianne Tang, Adelborg, Kasper, Østgård, Lene Sofie Granfeldt
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8213953/
https://www.ncbi.nlm.nih.gov/pubmed/34163252
http://dx.doi.org/10.2147/CLEP.S306857
_version_ 1783709962603593728
author Lauritsen, Tine Bichel
Nørgaard, Jan Maxwell
Grønbæk, Kirsten
Vallentin, Anders Pommer
Ahmad, Syed Azhar
Hannig, Louise Hur
Severinsen, Marianne Tang
Adelborg, Kasper
Østgård, Lene Sofie Granfeldt
author_facet Lauritsen, Tine Bichel
Nørgaard, Jan Maxwell
Grønbæk, Kirsten
Vallentin, Anders Pommer
Ahmad, Syed Azhar
Hannig, Louise Hur
Severinsen, Marianne Tang
Adelborg, Kasper
Østgård, Lene Sofie Granfeldt
author_sort Lauritsen, Tine Bichel
collection PubMed
description BACKGROUND: The Danish Myelodysplastic Syndromes Database (DMDSD) comprises nearly all patients diagnosed with myelodysplastic syndromes (MDS) in Denmark since 2010. The DMDSD has not yet been used for epidemiological research and the quality of registered variables remains to be investigated. OBJECTIVE: To describe characteristics of the patients registered in the DMDSD and to calculate predictive values and the proportion of missing values of registered data records. METHODS: We performed a nationwide cross-sectional validation study of recorded disease and treatment data on MDS patients during 2010–2019. Patient characteristics and the proportion of missing values were tabulated. A random sample of 12% was drawn to calculate predictive values with 95% confidence intervals (CIs) of 48 variables using information from medical records as a reference standard. RESULTS: Overall, 2284 patients were identified (median age: 76 years, men 62%). Of these, 10% had therapy-related MDS, and 6% had an antecedent hematological disease. Hemoglobin level was less than 6.2 mmol/L for 59% of patients. Within the first two years of treatment, 59% received transfusions, 35% received erythropoiesis-stimulating agents, and 15% were treated with a hypomethylating agent. For the majority of variables (around 80%), there were no missing data. A total of 260 medical records were available for validation. The positive predictive value of the MDS diagnosis was 92% (95% CI: 88–95). Predictive values ranged from 64% to 100% and exceeded 90% for 36 out of 48 variables. Stratification by year of diagnosis suggested that the positive predictive value of the MDS diagnosis improved from 88% before 2015 to 95% after. CONCLUSION: In this study, there was a high accuracy of recorded data and a low proportion of missing data. Thus, the DMDSD serves as a valuable data source for future epidemiological studies on MDS.
format Online
Article
Text
id pubmed-8213953
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Dove
record_format MEDLINE/PubMed
spelling pubmed-82139532021-06-22 The Danish Myelodysplastic Syndromes Database: Patient Characteristics and Validity of Data Records Lauritsen, Tine Bichel Nørgaard, Jan Maxwell Grønbæk, Kirsten Vallentin, Anders Pommer Ahmad, Syed Azhar Hannig, Louise Hur Severinsen, Marianne Tang Adelborg, Kasper Østgård, Lene Sofie Granfeldt Clin Epidemiol Original Research BACKGROUND: The Danish Myelodysplastic Syndromes Database (DMDSD) comprises nearly all patients diagnosed with myelodysplastic syndromes (MDS) in Denmark since 2010. The DMDSD has not yet been used for epidemiological research and the quality of registered variables remains to be investigated. OBJECTIVE: To describe characteristics of the patients registered in the DMDSD and to calculate predictive values and the proportion of missing values of registered data records. METHODS: We performed a nationwide cross-sectional validation study of recorded disease and treatment data on MDS patients during 2010–2019. Patient characteristics and the proportion of missing values were tabulated. A random sample of 12% was drawn to calculate predictive values with 95% confidence intervals (CIs) of 48 variables using information from medical records as a reference standard. RESULTS: Overall, 2284 patients were identified (median age: 76 years, men 62%). Of these, 10% had therapy-related MDS, and 6% had an antecedent hematological disease. Hemoglobin level was less than 6.2 mmol/L for 59% of patients. Within the first two years of treatment, 59% received transfusions, 35% received erythropoiesis-stimulating agents, and 15% were treated with a hypomethylating agent. For the majority of variables (around 80%), there were no missing data. A total of 260 medical records were available for validation. The positive predictive value of the MDS diagnosis was 92% (95% CI: 88–95). Predictive values ranged from 64% to 100% and exceeded 90% for 36 out of 48 variables. Stratification by year of diagnosis suggested that the positive predictive value of the MDS diagnosis improved from 88% before 2015 to 95% after. CONCLUSION: In this study, there was a high accuracy of recorded data and a low proportion of missing data. Thus, the DMDSD serves as a valuable data source for future epidemiological studies on MDS. Dove 2021-06-14 /pmc/articles/PMC8213953/ /pubmed/34163252 http://dx.doi.org/10.2147/CLEP.S306857 Text en © 2021 Lauritsen et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Lauritsen, Tine Bichel
Nørgaard, Jan Maxwell
Grønbæk, Kirsten
Vallentin, Anders Pommer
Ahmad, Syed Azhar
Hannig, Louise Hur
Severinsen, Marianne Tang
Adelborg, Kasper
Østgård, Lene Sofie Granfeldt
The Danish Myelodysplastic Syndromes Database: Patient Characteristics and Validity of Data Records
title The Danish Myelodysplastic Syndromes Database: Patient Characteristics and Validity of Data Records
title_full The Danish Myelodysplastic Syndromes Database: Patient Characteristics and Validity of Data Records
title_fullStr The Danish Myelodysplastic Syndromes Database: Patient Characteristics and Validity of Data Records
title_full_unstemmed The Danish Myelodysplastic Syndromes Database: Patient Characteristics and Validity of Data Records
title_short The Danish Myelodysplastic Syndromes Database: Patient Characteristics and Validity of Data Records
title_sort danish myelodysplastic syndromes database: patient characteristics and validity of data records
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8213953/
https://www.ncbi.nlm.nih.gov/pubmed/34163252
http://dx.doi.org/10.2147/CLEP.S306857
work_keys_str_mv AT lauritsentinebichel thedanishmyelodysplasticsyndromesdatabasepatientcharacteristicsandvalidityofdatarecords
AT nørgaardjanmaxwell thedanishmyelodysplasticsyndromesdatabasepatientcharacteristicsandvalidityofdatarecords
AT grønbækkirsten thedanishmyelodysplasticsyndromesdatabasepatientcharacteristicsandvalidityofdatarecords
AT vallentinanderspommer thedanishmyelodysplasticsyndromesdatabasepatientcharacteristicsandvalidityofdatarecords
AT ahmadsyedazhar thedanishmyelodysplasticsyndromesdatabasepatientcharacteristicsandvalidityofdatarecords
AT hanniglouisehur thedanishmyelodysplasticsyndromesdatabasepatientcharacteristicsandvalidityofdatarecords
AT severinsenmariannetang thedanishmyelodysplasticsyndromesdatabasepatientcharacteristicsandvalidityofdatarecords
AT adelborgkasper thedanishmyelodysplasticsyndromesdatabasepatientcharacteristicsandvalidityofdatarecords
AT østgardlenesofiegranfeldt thedanishmyelodysplasticsyndromesdatabasepatientcharacteristicsandvalidityofdatarecords
AT lauritsentinebichel danishmyelodysplasticsyndromesdatabasepatientcharacteristicsandvalidityofdatarecords
AT nørgaardjanmaxwell danishmyelodysplasticsyndromesdatabasepatientcharacteristicsandvalidityofdatarecords
AT grønbækkirsten danishmyelodysplasticsyndromesdatabasepatientcharacteristicsandvalidityofdatarecords
AT vallentinanderspommer danishmyelodysplasticsyndromesdatabasepatientcharacteristicsandvalidityofdatarecords
AT ahmadsyedazhar danishmyelodysplasticsyndromesdatabasepatientcharacteristicsandvalidityofdatarecords
AT hanniglouisehur danishmyelodysplasticsyndromesdatabasepatientcharacteristicsandvalidityofdatarecords
AT severinsenmariannetang danishmyelodysplasticsyndromesdatabasepatientcharacteristicsandvalidityofdatarecords
AT adelborgkasper danishmyelodysplasticsyndromesdatabasepatientcharacteristicsandvalidityofdatarecords
AT østgardlenesofiegranfeldt danishmyelodysplasticsyndromesdatabasepatientcharacteristicsandvalidityofdatarecords