Ferretti Stefano, Guzzinati Stefano, Zambon Paola, Manneschi Gianfranco, Crocetti Emanuele, Falcini Fabio, Giorgetti Stefania, Cirilli Claudia, Pirani Monica, Mangone Lucia, Di Felice Enza, Del Lisi Vincenzo, Sgargi Paolo, Buzzoni Carlotta, Russo Antonio, Paci Eugenio
Registro tumori della Provincia di Ferrara, Dipartimento di medicina sperimentale e diagnostica, Sezione di anatomia, istologia e citologia patologica, via Fossato di Mortara 64B, Ferrara.
Epidemiol Prev. 2009 Jul-Oct;33(4-5):147-53.
the study evaluates the accuracy of an algorithm based on hospital discharge data (HDD) in order to estimate breast cancer incidence in three italian regions (Emilia-Romagna, Toscana and Veneto) covered by cancer registries (CR). The evolution of computer-based information systems in health organization suggests automatic processing of HDD as a possible alternative to the time-consuming methods of CR. The study intends to verify whether HDD quickly provides reliable cancer incidence estimates for diagnosis and therapy evaluations.
an algorithm based on discharge diagnosis and surgical therapy of hospitalized breast cancer patients was developed in order to provide breast cancer incidence. Results were compared with the corresponding incidence data of cancer registries. The accuracy of the automatic method was also verified by a direct record-linkage between HDD output and registries' files. The overall survival of cases lost to "HDD method" was analyzed.
in the period covered by the study (3,125,425 person/year) CR enrolled 6,079 incident cases, compared to 6,000 cases recorded through the HDD flow. Incidence rates of the two methods (CR 194.5; HDD 192.0 x 100.000) showed no statistical differences. However, matched cases by the two methods were only 5,038. The sensitivity of the HDD algorithm was 82.9% and its predictive positive value (PPV) was 84.0%. False positive cases were 9.9%. On the other hand, 12.3% CR incident cases were not identified by the algorithm: these were mainly made up of older women, not eligible for surgical therapy. Their three-years survival was 62.0% vs 88.8% of the whole incidence group.
HDD flow performance was similar to observations reported in the literature. The agreement between HDD and CR incidence rates is a result of a cross effect of both sensitivity and specificity limitations of the HDD algorithm. This can seriously impair the reliability of the latter method with regard to the evaluation of diagnostic and therapeutic strategies in cohort studies (i.e. the most effective approach to health setting in oncology).s.
本研究评估一种基于医院出院数据(HDD)的算法的准确性,以估计意大利三个有癌症登记处(CR)覆盖的地区(艾米利亚 - 罗马涅、托斯卡纳和威尼托)的乳腺癌发病率。卫生组织中基于计算机的信息系统的发展表明,对HDD进行自动处理可能是耗时的CR方法的一种替代方案。该研究旨在验证HDD是否能快速提供可靠的癌症发病率估计值,用于诊断和治疗评估。
开发了一种基于住院乳腺癌患者出院诊断和手术治疗的算法,以提供乳腺癌发病率。将结果与癌症登记处的相应发病率数据进行比较。还通过HDD输出与登记处文件之间的直接记录链接,验证了自动方法的准确性。分析了“HDD方法”遗漏病例的总生存率。
在研究涵盖的时间段(3,125,425人/年)内,CR登记了6,079例新发病例,而通过HDD流程记录的病例为6,000例。两种方法的发病率(CR为194.5;HDD为192.0×100,000)无统计学差异。然而,两种方法匹配的病例仅为5,038例。HDD算法的敏感性为82.9%,其预测阳性值(PPV)为84.0%。假阳性病例为9.9%。另一方面,该算法未识别出12.3%的CR新发病例:这些病例主要由不符合手术治疗条件的老年女性组成。她们的三年生存率为62.0%,而整个发病组的三年生存率为88.8%。
HDD流程的性能与文献报道的观察结果相似。HDD与CR发病率之间的一致性是HDD算法敏感性和特异性局限性交叉效应的结果。这可能会严重损害后一种方法在队列研究中评估诊断和治疗策略方面的可靠性(即肿瘤学中最有效的健康设置方法)。