Literature DB >> 12883382

Evaluation of an algorithm to identify incident breast cancer cases using DRGs data.

O Ganry1, A Taleb, J Peng, N Raverdy, A Dubreuil.   

Abstract

Hospital databases have the potential to be inexpensive, timely and nationally representative sources of information about cancer. This study examines the utility of the French hospital database adapted from the Diagnosis Related Group (DRG) classification and named 'Programme de médicalisation des systèmes d'information (PMSI)', as an independent source to identify incident cancer cases. From the 19 679 women hospitalized and treated in 1998 in the public hospitals of the Somme area in France, we identified those diagnosed with breast cancer in the PMSI database. These women were matched with women in the cancer registry of the Somme area who had been diagnosed with breast cancer in 1998. An algorithm was used to identify cancer-related diagnoses and procedures reported to PMSI. The sensitivity, specificity and positive predictive value (PPV) of the PMSI database were calculated using the cancer registry as a gold standard. The PMSI database had 85% sensitivity, 99.9% specificity and 97% PPV for women hospitalized with breast cancer as a principal diagnosis. The sensitivity was higher by 9% for hospitalization with breast cancer as a secondary diagnosis but had a lower PPV (78%). In conclusion, the PMSI database seems to offer an interesting potential to assess breast cancer incidence, because of its high sensitivity, in particular when secondary diagnosis was considered, and its very high specificity and PPV. However, these preliminary results need to be confirmed by other studies in France before such databases are used, particularly in areas without cancer registries.

Entities:  

Mesh:

Year:  2003        PMID: 12883382     DOI: 10.1097/00008469-200308000-00009

Source DB:  PubMed          Journal:  Eur J Cancer Prev        ISSN: 0959-8278            Impact factor:   2.497


  6 in total

1.  Leveraging Linkage of Cohort Studies With Administrative Claims Data to Identify Individuals With Cancer.

Authors:  Mackenzie R Bronson; Nirav S Kapadia; Andrea M Austin; Qianfei Wang; Diane Feskanich; Julie P W Bynum; Francine Grodstein; Anna N A Tosteson
Journal:  Med Care       Date:  2018-12       Impact factor: 2.983

2.  Detection of incident breast and colorectal cancer cases from an administrative healthcare database in Catalonia, Spain.

Authors:  J M Escribà; M Banqué; F Macià; J Gálvez; L Esteban; L Pareja; R Clèries; X Sanz; X Castells; J M Borrás; J Ribes
Journal:  Clin Transl Oncol       Date:  2019-10-04       Impact factor: 3.405

3.  Is it possible to estimate the incidence of breast cancer from medico-administrative databases?

Authors:  L Remontet; N Mitton; C M Couris; J Iwaz; F Gomez; F Olive; S Polazzi; A M Schott; B Trombert; N Bossard; M Colonna
Journal:  Eur J Epidemiol       Date:  2008-08-21       Impact factor: 8.082

4.  Estimation of national colorectal-cancer incidence using claims databases.

Authors:  C Quantin; E Benzenine; M Hägi; B Auverlot; M Abrahamowicz; J Cottenet; E Fournier; C Binquet; D Compain; E Monnet; A M Bouvier; A Danzon
Journal:  J Cancer Epidemiol       Date:  2012-06-26

5.  Accuracy of administrative databases in detecting primary breast cancer diagnoses: a systematic review.

Authors:  Iosief Abraha; Alessandro Montedori; Diego Serraino; Massimiliano Orso; Gianni Giovannini; Valeria Scotti; Annalisa Granata; Francesco Cozzolino; Mario Fusco; Ettore Bidoli
Journal:  BMJ Open       Date:  2018-07-23       Impact factor: 2.692

6.  Validation of Administrative Big Database for Colorectal Cancer Searched by International Classification of Disease 10th Codes in Korean: A Retrospective Big-cohort Study.

Authors:  Young-Jae Hwang; Nayoung Kim; Chang Yong Yun; Hyuk Yoon; Cheol Min Shin; Young Soo Park; Il Tae Son; Heung-Kwon Oh; Duck-Woo Kim; Sung-Bum Kang; Hye Seung Lee; Seon Mee Park; Dong Ho Lee
Journal:  J Cancer Prev       Date:  2018-12-30
  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.