Feingold-Link Lani, Edwards Todd L, Jones Sarah, Hartmann Katherine E, Velez Edwards Digna R
1 Department of Medicine, Vanderbilt University , Nashville, TN.
J Womens Health (Larchmt). 2014 Dec;23(12):1027-32. doi: 10.1089/jwh.2014.4978.
Uterine leiomyomata (fibroids) affect up to 77% of women by menopause and account for $9.4 billion in yearly healthcare costs. Most studies rely on self-reported diagnosis, which may result in misclassification of controls since as many as 50% of cases are asymptomatic and thus undiagnosed. Our objective was to evaluate the performance and accuracy of a fibroid phenotyping algorithm constructed from electronic medical record (EMR) data, limiting to subjects with pelvic imaging.
Our study population includes women from a clinical population at Vanderbilt University Medical Center (2008-2012). Analyses were restricted to women 18 years and older with at least one fibroid diagnosis confirmed by imaging for cases or at least two separate pelvic imaging procedures without a diagnosis for controls. We randomly reviewed 218 records to evaluate the accuracy of our algorithm and assess the indications for pelvic imaging. Participant characteristics and indications for imaging were compared between cases and controls in unadjusted and adjusted logistic regression analyses.
Our algorithm had a positive predictive value of 96% and negative predictive value of 98%. Increasing age (odds ratio=1.05, 95% confidence interval 1.03-1.08) and Black race (odds ratio=2.15, 95% confidence interval 1.18-3.94) were identified as risk factors for fibroids. The most common indications for imaging in both cases and controls were pain, bleeding, and reproductive factors, and the most common imaging modality was a pelvic ultrasound.
These data suggest that using biorepositories linked to EMR data is a feasible way to identify populations of imaged women that facilitate investigations of fibroid risk factors.
子宫平滑肌瘤（纤维瘤）在绝经前影响高达77%的女性，每年的医疗费用达94亿美元。大多数研究依赖自我报告的诊断，这可能导致对照组的错误分类，因为多达50%的病例无症状，因此未被诊断出来。我们的目标是评估从电子病历（EMR）数据构建的纤维瘤表型分析算法的性能和准确性，研究对象限于有盆腔影像学检查的患者。
我们的研究人群包括范德堡大学医学中心临床人群中的女性（2008 - 2012年）。分析仅限于18岁及以上的女性，病例组需经影像学确诊至少有一个纤维瘤，对照组需至少有两次独立的盆腔影像学检查且未确诊。我们随机查阅了218份记录，以评估算法的准确性并评估盆腔影像学检查的指征。在未调整和调整后的逻辑回归分析中，比较了病例组和对照组的参与者特征及影像学检查指征。
我们的算法阳性预测值为96%，阴性预测值为98%。年龄增加（比值比 = 1.05，95%置信区间1.03 - 1.08）和黑人种族（比值比 = 2.15，95%置信区间1.18 - 3.94）被确定为纤维瘤的危险因素。病例组和对照组最常见的影像学检查指征是疼痛、出血和生殖因素，最常见的影像学检查方式是盆腔超声。
这些数据表明，使用与EMR数据相关联的生物样本库是识别有盆腔影像学检查的女性群体的可行方法，有助于对纤维瘤危险因素进行研究。
