Burnett-Hartman Andrea N, Kamineni Aruna, Corley Douglas A, Singal Amit G, Halm Ethan A, Rutter Carolyn M, Chubak Jessica, Lee Jeffrey K, Doubeni Chyke A, Inadomi John M, Doria-Rose V Paul, Zheng Yingye
Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, US.
Fred Hutchinson Cancer Research Center, Seattle, WA, US.
EGEMS (Wash DC). 2019 Aug 2;7(1):37. doi: 10.5334/egems.296.
Despite the importance of characterizing colonoscopy indication for quality monitoring and cancer screening program evaluation, there is no standard approach to documenting colonoscopy indication in medical records.
We applied two algorithms in three health care systems to assign colonoscopy indication to persons 50-89 years old who received a colonoscopy during 2010-2013. Both algorithms used standard procedure, diagnostic, and laboratory codes. One algorithm, the KPNC algorithm, used a hierarchical approach to classify exam indication into: diagnostic, surveillance, or screening; whereas the other, the SEARCH algorithm, used a logistic regression-based algorithm to provide the probability that colonoscopy was performed for screening. Gold standard assessment of indication was from medical records abstraction.
There were 1,796 colonoscopy exams included in analyses; age and racial/ethnic distributions of participants differed across health care systems. The KPNC algorithm's sensitivities and specificities for screening indication ranged from 0.78-0.82 and 0.78-0.91, respectively; sensitivities and specificities for diagnostic indication ranged from 0.78-0.89 and 0.74-0.82, respectively. The KPNC algorithm had poor sensitivities (ranging from 0.11-0.67) and high specificities for surveillance exams. The Area Under the Curve (AUC) of the SEARCH algorithm for screening indication ranged from 0.76-0.84 across health care systems. For screening indication, the KPNC algorithm obtained higher specificities than the SEARCH algorithm at the same sensitivity.
Despite standardized implementation of these indication algorithms across three health care systems, the capture of colonoscopy indication data was imperfect. Thus, we recommend that standard, systematic documentation of colonoscopy indication should be added to medical records to ensure efficient and accurate data capture.
尽管确定结肠镜检查指征对于质量监测和癌症筛查项目评估至关重要，但在医疗记录中记录结肠镜检查指征尚无标准方法。
我们在三个医疗系统中应用两种算法，为2010年至2013年期间接受结肠镜检查的50至89岁人群确定结肠镜检查指征。两种算法均使用标准程序、诊断和实验室代码。一种算法即KPNC算法，采用分层方法将检查指征分为：诊断性、监测性或筛查性；而另一种算法即SEARCH算法，使用基于逻辑回归的算法来提供结肠镜检查用于筛查的概率。指征的金标准评估来自病历摘要。
分析纳入了1796例结肠镜检查；不同医疗系统中参与者的年龄和种族/族裔分布有所不同。KPNC算法对筛查指征的敏感性和特异性分别为0.78 - 0.82和0.78 - 0.91；对诊断指征的敏感性和特异性分别为0.78 - 0.89和0.74 - 0.82。KPNC算法对监测性检查的敏感性较差（范围为0.11 - 0.67）且特异性较高。SEARCH算法用于筛查指征的曲线下面积（AUC）在各医疗系统中范围为0.76 - 0.84。对于筛查指征，在相同敏感性下，KPNC算法比SEARCH算法获得更高的特异性。
尽管这两种指征算法在三个医疗系统中进行了标准化实施，但结肠镜检查指征数据的获取并不完善。因此，我们建议应在医疗记录中增加结肠镜检查指征的标准、系统记录，以确保高效、准确地获取数据。
