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首页> 外文期刊>International journal of medical informatics >The absence of longitudinal data limits the accuracy of high-throughput clinical phenotyping for identifying type 2 diabetes mellitus subjects
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The absence of longitudinal data limits the accuracy of high-throughput clinical phenotyping for identifying type 2 diabetes mellitus subjects

机译：缺乏纵向数据限制了高通量临床表型鉴定2型糖尿病受试者的准确性

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Purpose: To evaluate the impact of insufficient longitudinal data on the accuracy of a high-throughput clinical phenotyping (HTCP) algorithm for identifying (1) patients with type 2 diabetes mellitus (T2DM) and (2) patients with no diabetes. Methods: Retrospective study conducted at Mayo Clinic in Rochester, Minnesota. Eligible subjects were Olmsted County residents with >1 Mayo Clinic encounter in each of three time periods: (1) 2007, (2) from 1997 through 2006, and (3) before 1997 (N = 54,283). Diabetes relevant electronic medical record (EMR) data about diagnoses, laboratories, and medications were used. We employed the HTCP algorithm to categorize individuals as T2DM cases and non-diabetes controls. Considering the full 11 years (1997-2007) as the gold standard, we compared gold-standard categorizations with those using data for 10 subsequent intervals, ranging from 1998-2007 (10-year data) to 2007 (1-year data). Positive predictive values (PPVs) and false-negative rates (FNRs) were calculated. McNemar tests were used to determine whether categorizations using shorter time periods differed from the gold standard. Statistical significance was defined as P< 0.05. Results: We identified 2770 T2DM cases and 21,005 controls when the algorithm was applied using 11-year data. Using 2007 data alone, PPVs and FNRs, respectively, were 70% and 25% for case identification and 59% and 67% for control identification. All time frames differed significantly from the gold standard, except for the 10-year period. Conclusions: The accuracy of the algorithm reduced remarkably as data were limited to shorter observation periods. This impact should be considered carefully when designing/executing HTCP algorithms.
机译：目的：评价纵向数据不足对高通量临床表型（HTCP）算法的准确性的影响，该算法可识别（1）2型糖尿病（T2DM）患者和（2）无糖尿病患者。方法：在明尼苏达州罗切斯特市梅奥诊所进行的回顾性研究。符合条件的受试者是在以下三个时间段中每个时间都有> 1 Mayo诊所的Olmsted县居民：（1）2007，（2）从1997年到2006年以及（3）1997年之前（N = 54,283）。使用与糖尿病有关的有关诊断，实验室和药物的电子病历（EMR）数据。我们采用HTCP算法将个人归类为T2DM病例和非糖尿病对照。将整整11年（1997-2007年）作为黄金标准，我们将黄金标准分类与使用10个后续区间（从1998-2007年（10年数据）到2007年（1年数据））的数据进行了比较。计算阳性预测值（PPV）和假阴性率（FNR）。 McNemar测试用于确定使用较短时间段的分类是否不同于黄金标准。统计学显着性定义为P <0.05。结果：使用11年的数据应用该算法时，我们确定了2770例T2DM病例和21,005例对照。仅使用2007年的数据，病例识别的PPV和FNR分别为70％和25％，对照识别的分别为59％和67％。除10年期限外，所有时间范围均与金本位制有显着差异。结论：该算法的准确性显着降低，因为数据仅限于较短的观察期。在设计/执行HTCP算法时，应仔细考虑这种影响。

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