Division of Nephrology, Department of Medicine, Western University, London, Ontario, Canada.
PLoS One. 2013 Aug 14;8(8):e70825. doi: 10.1371/journal.pone.0070825. eCollection 2013.
We evaluated the validity of physician billing claims to identify deceased organ donors in large provincial healthcare databases.
We conducted a population-based retrospective validation study of all deceased donors in Ontario, Canada from 2006 to 2011 (n = 988). We included all registered deaths during the same period (n = 458,074). Our main outcome measures included sensitivity, specificity, positive predictive value, and negative predictive value of various algorithms consisting of physician billing claims to identify deceased organ donors and organ-specific donors compared to a reference standard of medical chart abstraction.
The best performing algorithm consisted of any one of 10 different physician billing claims. This algorithm had a sensitivity of 75.4% (95% CI: 72.6% to 78.0%) and a positive predictive value of 77.4% (95% CI: 74.7% to 80.0%) for the identification of deceased organ donors. As expected, specificity and negative predictive value were near 100%. The number of organ donors identified by the algorithm each year was similar to the expected value, and this included the pre-validation period (1991 to 2005). Algorithms to identify organ-specific donors performed poorly (e.g. sensitivity ranged from 0% for small intestine to 67% for heart; positive predictive values ranged from 0% for small intestine to 37% for heart).
Primary data abstraction to identify deceased organ donors should be used whenever possible, particularly for the detection of organ-specific donations. The limitations of physician billing claims should be considered whenever they are used.
我们评估了医师计费索赔识别大型省级医疗保健数据库中已故器官捐献者的有效性。
我们对加拿大安大略省 2006 年至 2011 年期间所有已故供体(n=988)进行了一项基于人群的回顾性验证研究。我们纳入了同期所有登记的死亡病例(n=458,074)。我们的主要观察指标包括各种算法(包含医师计费索赔)识别已故器官捐献者和器官特异性捐献者的敏感性、特异性、阳性预测值和阴性预测值,并与病历摘要的参考标准进行比较。
表现最佳的算法包含 10 种不同医师计费索赔中的任意一种。该算法识别已故器官捐献者的敏感性为 75.4%(95%CI:72.6%至 78.0%),阳性预测值为 77.4%(95%CI:74.7%至 80.0%)。正如预期的那样,特异性和阴性预测值接近 100%。每年通过算法识别的器官捐献者数量与预期值相似,这包括验证前期间(1991 年至 2005 年)。识别器官特异性捐献者的算法表现不佳(例如,小肠的敏感性范围为 0%至心脏的 67%;心脏的阳性预测值范围为 0%至 37%)。
应尽可能使用原始数据摘要来识别已故器官捐献者,特别是用于检测器官特异性捐献。在使用医师计费索赔时,应考虑其局限性。