Health Services Research Unit, Institute for Health Sciences in Aragon, Zaragoza, Spain.
BMC Health Serv Res. 2010 Jan 8;10:9. doi: 10.1186/1472-6963-10-9.
The use of hospital discharge administrative data (HDAD) has been recommended for automating, improving, even substituting, population-based cancer registries. The frequency of false positive and false negative cases recommends local validation.
The aim of this study was to detect newly diagnosed, false positive and false negative cases of cancer from hospital discharge claims, using four Spanish population-based cancer registries as the gold standard. Prostate cancer was used as a case study.
A total of 2286 incident cases of prostate cancer registered in 2000 were used for validation. In the most sensitive algorithm (that using five diagnostic codes), estimates for Sensitivity ranged from 14.5% (CI95% 10.3-19.6) to 45.7% (CI95% 41.4-50.1). In the most predictive algorithm (that using five diagnostic and five surgical codes) Positive Predictive Value estimates ranged from 55.9% (CI95% 42.4-68.8) to 74.3% (CI95% 67.0-80.6). The most frequent reason for false positive cases was the number of prevalent cases inadequately considered as newly diagnosed cancers, ranging from 61.1% to 82.3% of false positive cases. The most frequent reason for false negative cases was related to the number of cases not attended in hospital settings. In this case, figures ranged from 34.4% to 69.7% of false negative cases, in the most predictive algorithm.
HDAD might be a helpful tool for cancer registries to reach their goals. The findings suggest that, for automating cancer registries, algorithms combining diagnoses and procedures are the best option. However, for cancer surveillance purposes, in those cancers like prostate cancer in which care is not only hospital-based, combining inpatient and outpatient information will be required.
使用医院出院行政数据(HDAD)已被推荐用于自动化、改进甚至替代基于人群的癌症登记处。假阳性和假阴性病例的频率建议进行局部验证。
本研究旨在利用四个基于人群的西班牙癌症登记处作为金标准,从医院出院记录中检测新诊断的癌症、假阳性和假阴性病例。前列腺癌被用作案例研究。
共使用 2000 年登记的 2286 例前列腺癌新发病例进行验证。在最敏感的算法(使用五个诊断代码)中,灵敏度估计值范围为 14.5%(95%CI95% 10.3-19.6)至 45.7%(95%CI95% 41.4-50.1)。在最具预测性的算法(使用五个诊断和五个手术代码)中,阳性预测值的估计值范围为 55.9%(95%CI95% 42.4-68.8)至 74.3%(95%CI95% 67.0-80.6)。假阳性病例最常见的原因是未充分考虑为新诊断癌症的既往病例数量,从 61.1%到 82.3%的假阳性病例属于这种情况。假阴性病例最常见的原因与未在医院环境中就诊的病例数量有关。在这种情况下,最具预测性的算法中,假阴性病例的比例范围为 34.4%至 69.7%。
HDAD 可能是癌症登记处实现其目标的有用工具。研究结果表明,对于自动化癌症登记处,结合诊断和程序的算法是最佳选择。然而,对于癌症监测目的,在像前列腺癌这样的癌症中,护理不仅基于医院,还需要结合住院和门诊信息。