Suppr超能文献

使用诊断相关分组(DRGs)数据评估一种识别新发乳腺癌病例的算法。

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

作者信息

Ganry O, Taleb A, Peng J, Raverdy N, Dubreuil A

机构信息

Medical Information, Epidemiology and Biostatistics, Hôpital Nord, Place Pauchet, Amiens University Hospital, 80 054 Amiens Cedex 1, France.

出版信息

Eur J Cancer Prev. 2003 Aug;12(4):295-9. doi: 10.1097/00008469-200308000-00009.

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.

摘要

医院数据库有可能成为获取癌症信息的低成本、及时且具有全国代表性的来源。本研究探讨了源自诊断相关分组(DRG)分类并命名为“医疗信息系统规划(PMSI)”的法国医院数据库,作为识别新发癌症病例的独立来源的效用。从1998年在法国索姆地区公立医院住院并接受治疗的19679名女性中,我们在PMSI数据库中确定了那些被诊断为乳腺癌的患者。这些女性与索姆地区癌症登记处中1998年被诊断为乳腺癌的女性进行匹配。使用一种算法来识别报告给PMSI的癌症相关诊断和程序。以癌症登记处作为金标准,计算PMSI数据库的敏感性、特异性和阳性预测值(PPV)。对于以乳腺癌作为主要诊断住院的女性,PMSI数据库的敏感性为85%,特异性为99.9%,PPV为97%。以乳腺癌作为次要诊断住院时,敏感性高9%,但PPV较低(78%)。总之,由于其高敏感性,特别是在考虑次要诊断时,以及非常高的特异性和PPV,PMSI数据库似乎为评估乳腺癌发病率提供了一个有趣的潜力。然而,在使用此类数据库之前,尤其是在没有癌症登记处的地区,这些初步结果需要在法国的其他研究中得到证实。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验