Grams Morgan E, Waikar Sushrut S, MacMahon Blaithin, Whelton Seamus, Ballew Shoshana H, Coresh Josef
Division of Nephrology and, §Division of General Internal Medicine, Department of Medicine, Johns Hopkins University, Baltimore, Maryland;, †Renal Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, ‡Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
Clin J Am Soc Nephrol. 2014 Apr;9(4):682-9. doi: 10.2215/CJN.07650713. Epub 2014 Jan 23.
Billing codes are frequently used to identify AKI events in epidemiologic research. The goals of this study were to validate billing code-identified AKI against the current AKI consensus definition and to ascertain whether sensitivity and specificity vary by patient characteristic or over time.
DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: The study population included 10,056 Atherosclerosis Risk in Communities study participants hospitalized between 1996 and 2008. Billing code-identified AKI was compared with the 2012 Kidney Disease Improving Global Outcomes (KDIGO) creatinine-based criteria (AKIcr) and an approximation of the 2012 KDIGO creatinine- and urine output-based criteria (AKIcr_uop) in a subset with available outpatient data. Sensitivity and specificity of billing code-identified AKI were evaluated over time and according to patient age, race, sex, diabetes status, and CKD status in 546 charts selected for review, with estimates adjusted for sampling technique.
A total of 34,179 hospitalizations were identified; 1353 had a billing code for AKI. The sensitivity of billing code-identified AKI was 17.2% (95% confidence interval [95% CI], 13.2% to 21.2%) compared with AKIcr (n=1970 hospitalizations) and 11.7% (95% CI, 8.8% to 14.5%) compared with AKIcr_uop (n=1839 hospitalizations). Specificity was >98% in both cases. Sensitivity was significantly higher in the more recent time period (2002-2008) and among participants aged 65 years and older. Billing code-identified AKI captured a more severe spectrum of disease than did AKIcr and AKIcr_uop, with a larger proportion of patients with stage 3 AKI (34.9%, 19.7%, and 11.5%, respectively) and higher in-hospital mortality (41.2%, 18.7%, and 12.8%, respectively).
The use of billing codes to identify AKI has low sensitivity compared with the current KDIGO consensus definition, especially when the urine output criterion is included, and results in the identification of a more severe phenotype. Epidemiologic studies using billing codes may benefit from a high specificity, but the variation in sensitivity may result in bias, particularly when trends over time are the outcome of interest.
在流行病学研究中，计费代码常用于识别急性肾损伤（AKI）事件。本研究的目的是根据当前的AKI共识定义验证通过计费代码识别的AKI，并确定敏感性和特异性是否因患者特征或时间而有所不同。
设计、地点、参与者及测量方法：研究人群包括1996年至2008年间住院的10,056名社区动脉粥样硬化风险研究参与者。在有门诊数据的子集中，将通过计费代码识别的AKI与2012年改善全球肾脏病预后组织（KDIGO）基于肌酐的标准（AKIcr）以及2012年KDIGO基于肌酐和尿量的标准的近似值（AKIcr_uop）进行比较。在为审查而选择的546份病历中，根据患者年龄、种族、性别、糖尿病状态和慢性肾脏病（CKD）状态，对通过计费代码识别的AKI的敏感性和特异性进行了随时间的评估，并对抽样技术进行了估计调整。
共识别出34,179次住院；1353次有AKI的计费代码。与AKIcr（1970次住院）相比，通过计费代码识别的AKI的敏感性为17.2%（95%置信区间[95%CI]，13.2%至21.2%），与AKIcr_uop（1839次住院）相比为11.7%（95%CI，8.8%至14.5%）。两种情况下特异性均>98%。在最近时间段（2002 - 2008年）以及65岁及以上参与者中，敏感性显著更高。与AKIcr和AKIcr_uop相比，通过计费代码识别的AKI涵盖了更严重的疾病谱，3期AKI患者比例更高（分别为34.9%、19.7%和11.5%），住院死亡率也更高（分别为41.2%、18.7%和12.8%）。
与当前的KDIGO共识定义相比，使用计费代码识别AKI的敏感性较低，尤其是当纳入尿量标准时，并且会导致识别出更严重的表型。使用计费代码的流行病学研究可能受益于高特异性，但敏感性的差异可能导致偏差，特别是当随时间的趋势是感兴趣的结果时。
