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首页> 外文期刊>BMC Nephrology >Detecting chronic kidney disease in population-based administrative databases using an algorithm of hospital encounter and physician claim codes
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Detecting chronic kidney disease in population-based administrative databases using an algorithm of hospital encounter and physician claim codes

机译：使用医院遭遇和医生索赔代码在基于人群的管理数据库中检测慢性肾脏疾病

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Background Large, population-based administrative healthcare databases can be used to identify patients with chronic kidney disease (CKD) when serum creatinine laboratory results are unavailable. We examined the validity of algorithms that used combined hospital encounter and physician claims database codes for the detection of CKD in Ontario, Canada. Methods We accrued 123,499 patients over the age of 65 from 2007 to 2010. All patients had a baseline serum creatinine value to estimate glomerular filtration rate (eGFR). We developed an algorithm of physician claims and hospital encounter codes to search administrative databases for the presence of CKD. We determined the sensitivity, specificity, positive and negative predictive values of this algorithm to detect our primary threshold of CKD, an eGFR 2 (15.4% of patients). We also assessed serum creatinine and eGFR values in patients with and without CKD codes (algorithm positive and negative, respectively). Results Our algorithm required evidence of at least one of eleven CKD codes and 7.7% of patients were algorithm positive. The sensitivity was 32.7% [95% confidence interval: (95% CI): 32.0 to 33.3%]. Sensitivity was lower in women compared to men (25.7 vs. 43.7%; p 2 (26 to 51 mL/min per 1.73 m2) vs. 69 mL/min per 1.73 m2 (56 to 82 mL/min per 1.73 m2), respectively. Conclusions Patients with CKD as identified by our database algorithm had distinctly higher baseline serum creatinine values and lower eGFR values than those without such codes. However, because of limited sensitivity, the prevalence of CKD was underestimated.
机译：背景技术当无法获得血清肌酐实验室检查结果时，可以使用基于人群的大型行政医疗数据库来识别患有慢性肾脏疾病（CKD）的患者。我们检查了使用合并的医院遭遇和医生索赔数据库代码检测加拿大安大略省CKD的算法的有效性。方法从2007年至2010年，我们收集了123,499名65岁以上的患者。所有患者均具有基线血清肌酐值，以估计肾小球滤过率（eGFR）。我们开发了医师索赔和医院遭遇代码算法，以搜索管理数据库中是否存在CKD。我们确定了该算法检测CKD的主要阈值eGFR 2 （占患者的15.4％）的敏感性，特异性，阳性和阴性预测值。我们还评估了有和没有CKD代码（算法分别为阳性和阴性）的患者的血清肌酐和eGFR值。结果我们的算法需要至少11个CKD代码之一的证据，而7.7％的患者为算法阳性。灵敏度为32.7％[95％置信区间：（95％CI）：32.0至33.3％]。女性的敏感性低于男性（25.7对43.7％; p 2 （每1.73 m 2 26至51 mL / min）对每1.73 m <69 mL / min结论 sup> 2 （每1.73 m 2 分别为56至82 mL / min）结论根据我们的数据库算法确定的CKD患者基线血肌酐基线值明显较高，eGFR值较低与没有此类密码的人相比，由于敏感性有限，CKD的患病率被低估了。

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