Cargando…
Accuracy of administrative databases in detecting primary breast cancer diagnoses: a systematic review
OBJECTIVE: To define the accuracy of administrative datasets to identify primary diagnoses of breast cancer based on the International Classification of Diseases (ICD) 9th or 10th revision codes. DESIGN: Systematic review. Data sources: MEDLINE, EMBASE, Web of Science and the Cochrane Library (April...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6059263/ https://www.ncbi.nlm.nih.gov/pubmed/30037859 http://dx.doi.org/10.1136/bmjopen-2017-019264 |
_version_ | 1783341826281832448 |
---|---|
author | Abraha, Iosief Montedori, Alessandro Serraino, Diego Orso, Massimiliano Giovannini, Gianni Scotti, Valeria Granata, Annalisa Cozzolino, Francesco Fusco, Mario Bidoli, Ettore |
author_facet | Abraha, Iosief Montedori, Alessandro Serraino, Diego Orso, Massimiliano Giovannini, Gianni Scotti, Valeria Granata, Annalisa Cozzolino, Francesco Fusco, Mario Bidoli, Ettore |
author_sort | Abraha, Iosief |
collection | PubMed |
description | OBJECTIVE: To define the accuracy of administrative datasets to identify primary diagnoses of breast cancer based on the International Classification of Diseases (ICD) 9th or 10th revision codes. DESIGN: Systematic review. Data sources: MEDLINE, EMBASE, Web of Science and the Cochrane Library (April 2017). ELIGIBILITY CRITERIA: The inclusion criteria were: (a) the presence of a reference standard; (b) the presence of at least one accuracy test measure (eg, sensitivity) and (c) the use of an administrative database. DATA EXTRACTION: Eligible studies were selected and data extracted independently by two reviewers; quality was assessed using the Standards for Reporting of Diagnostic accuracy criteria. DATA ANALYSIS: Extracted data were synthesised using a narrative approach. RESULTS: From 2929 records screened 21 studies were included (data collection period between 1977 and 2011). Eighteen studies evaluated ICD-9 codes (11 of which assessed both invasive breast cancer (code 174.x) and carcinoma in situ (ICD-9 233.0)); three studies evaluated invasive breast cancer-related ICD-10 codes. All studies except one considered incident cases. The initial algorithm results were: sensitivity ≥80% in 11 of 17 studies (range 57%–99%); positive predictive value was ≥83% in 14 of 19 studies (range 15%–98%) and specificity ≥98% in 8 studies. The combination of the breast cancer diagnosis with surgical procedures, chemoradiation or radiation therapy, outpatient data or physician claim may enhance the accuracy of the algorithms in some but not all circumstances. Accuracy for breast cancer based on outpatient or physician’s data only or breast cancer diagnosis in secondary position diagnosis resulted low. CONCLUSION: Based on the retrieved evidence, administrative databases can be employed to identify primary breast cancer. The best algorithm suggested is ICD-9 or ICD-10 codes located in primary position. TRIAL REGISTRATION NUMBER: CRD42015026881. |
format | Online Article Text |
id | pubmed-6059263 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-60592632018-07-27 Accuracy of administrative databases in detecting primary breast cancer diagnoses: a systematic review Abraha, Iosief Montedori, Alessandro Serraino, Diego Orso, Massimiliano Giovannini, Gianni Scotti, Valeria Granata, Annalisa Cozzolino, Francesco Fusco, Mario Bidoli, Ettore BMJ Open Oncology OBJECTIVE: To define the accuracy of administrative datasets to identify primary diagnoses of breast cancer based on the International Classification of Diseases (ICD) 9th or 10th revision codes. DESIGN: Systematic review. Data sources: MEDLINE, EMBASE, Web of Science and the Cochrane Library (April 2017). ELIGIBILITY CRITERIA: The inclusion criteria were: (a) the presence of a reference standard; (b) the presence of at least one accuracy test measure (eg, sensitivity) and (c) the use of an administrative database. DATA EXTRACTION: Eligible studies were selected and data extracted independently by two reviewers; quality was assessed using the Standards for Reporting of Diagnostic accuracy criteria. DATA ANALYSIS: Extracted data were synthesised using a narrative approach. RESULTS: From 2929 records screened 21 studies were included (data collection period between 1977 and 2011). Eighteen studies evaluated ICD-9 codes (11 of which assessed both invasive breast cancer (code 174.x) and carcinoma in situ (ICD-9 233.0)); three studies evaluated invasive breast cancer-related ICD-10 codes. All studies except one considered incident cases. The initial algorithm results were: sensitivity ≥80% in 11 of 17 studies (range 57%–99%); positive predictive value was ≥83% in 14 of 19 studies (range 15%–98%) and specificity ≥98% in 8 studies. The combination of the breast cancer diagnosis with surgical procedures, chemoradiation or radiation therapy, outpatient data or physician claim may enhance the accuracy of the algorithms in some but not all circumstances. Accuracy for breast cancer based on outpatient or physician’s data only or breast cancer diagnosis in secondary position diagnosis resulted low. CONCLUSION: Based on the retrieved evidence, administrative databases can be employed to identify primary breast cancer. The best algorithm suggested is ICD-9 or ICD-10 codes located in primary position. TRIAL REGISTRATION NUMBER: CRD42015026881. BMJ Publishing Group 2018-07-23 /pmc/articles/PMC6059263/ /pubmed/30037859 http://dx.doi.org/10.1136/bmjopen-2017-019264 Text en © Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Oncology Abraha, Iosief Montedori, Alessandro Serraino, Diego Orso, Massimiliano Giovannini, Gianni Scotti, Valeria Granata, Annalisa Cozzolino, Francesco Fusco, Mario Bidoli, Ettore Accuracy of administrative databases in detecting primary breast cancer diagnoses: a systematic review |
title | Accuracy of administrative databases in detecting primary breast cancer diagnoses: a systematic review |
title_full | Accuracy of administrative databases in detecting primary breast cancer diagnoses: a systematic review |
title_fullStr | Accuracy of administrative databases in detecting primary breast cancer diagnoses: a systematic review |
title_full_unstemmed | Accuracy of administrative databases in detecting primary breast cancer diagnoses: a systematic review |
title_short | Accuracy of administrative databases in detecting primary breast cancer diagnoses: a systematic review |
title_sort | accuracy of administrative databases in detecting primary breast cancer diagnoses: a systematic review |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6059263/ https://www.ncbi.nlm.nih.gov/pubmed/30037859 http://dx.doi.org/10.1136/bmjopen-2017-019264 |
work_keys_str_mv | AT abrahaiosief accuracyofadministrativedatabasesindetectingprimarybreastcancerdiagnosesasystematicreview AT montedorialessandro accuracyofadministrativedatabasesindetectingprimarybreastcancerdiagnosesasystematicreview AT serrainodiego accuracyofadministrativedatabasesindetectingprimarybreastcancerdiagnosesasystematicreview AT orsomassimiliano accuracyofadministrativedatabasesindetectingprimarybreastcancerdiagnosesasystematicreview AT giovanninigianni accuracyofadministrativedatabasesindetectingprimarybreastcancerdiagnosesasystematicreview AT scottivaleria accuracyofadministrativedatabasesindetectingprimarybreastcancerdiagnosesasystematicreview AT granataannalisa accuracyofadministrativedatabasesindetectingprimarybreastcancerdiagnosesasystematicreview AT cozzolinofrancesco accuracyofadministrativedatabasesindetectingprimarybreastcancerdiagnosesasystematicreview AT fuscomario accuracyofadministrativedatabasesindetectingprimarybreastcancerdiagnosesasystematicreview AT bidoliettore accuracyofadministrativedatabasesindetectingprimarybreastcancerdiagnosesasystematicreview |