Acharya A, Cheng B, Koralkar R, Olson B, Lamster I B, Kunzel C, Lalla E
Marshfiled Clinic Research Institute, Marshfield, WI, USA.
Mailman School of Public Health, Columbia University, New York, NY, USA.
JDR Clin Trans Res. 2018 Apr;3(2):188-194. doi: 10.1177/2380084418759496. Epub 2018 Feb 26.
Undiagnosed diabetes and prediabetes present a serious public health challenge. We previously reported that data available in the dental setting can serve as a tool for early dysglycemia identification in a primarily Hispanic, urban population. In the present study, we sought to determine how the identification approach can be recalibrated to detect diabetes or prediabetes in a White, rural cohort and whether an integrated dental-medical electronic health record (iEHR) offers further value to the process. We analyzed iEHR data from the Marshfield Clinic, a health system providing care in rural Wisconsin, for dental patients who were ≥21 y of age, reported that they had never been told they had diabetes, had an initial periodontal examination of at least 2 quadrants, and had a glycemic assessment within 3 mo of that examination. We then assessed the performance of multiple predictive models for prediabetes/diabetes. The study outcome, glycemic status, was gleaned from the medical module of the iEHR based on American Diabetes Association blood test cutoffs. The sample size was 4,560 individuals. Multivariate logistic regression revealed that the best performance was achieved by a model that took advantage of the iEHR. Predictors included age, sex, race, ethnicity, number of missing teeth, percentage of teeth with at least 1 pocket ≥5 mm from the dental EHR, and overweight/obesity, hypertension, hyperlipidemia, and smoking status from the medical EHR. The model achieved an area under the receiver operating characteristic curve of 0.71 (95% confidence interval, 0.69-0.72), yielding a sensitivity of 0.70 and a specificity of 0.62. Across a range of populations, informed by certain patient characteristics, dental care team members can play a role in helping to identify dental patients with undiagnosed diabetes or prediabetes. The accuracy of the prediction increases when dental findings are combined with information from the medical EHR. Prediabetes and diabetes often go undiagnosed for many years. Early identification and care can lead to improved glycemic outcomes and prevent wide-ranging morbidity, including adverse oral health consequences, in affected individuals. Information available in the dental office can be used by clinicians to identify those who remain undiagnosed or are at risk; the accuracy of this prediction increases when combined with information from the medical electronic health record.
未确诊的糖尿病和糖尿病前期构成了严峻的公共卫生挑战。我们之前报告过，牙科环境中可用的数据可作为一种工具，用于在主要为西班牙裔的城市人群中早期识别血糖异常。在本研究中，我们试图确定如何重新校准识别方法，以在白人农村队列中检测糖尿病或糖尿病前期，以及整合的牙科 - 医疗电子健康记录（iEHR）是否能为该过程提供更多价值。我们分析了威斯康星州农村地区提供医疗服务的马什菲尔德诊所的iEHR数据，这些数据来自年龄≥21岁、报告从未被告知患有糖尿病、至少进行了2个象限的初始牙周检查且在该检查后3个月内进行了血糖评估的牙科患者。然后，我们评估了多种糖尿病前期/糖尿病预测模型的性能。研究结果，即血糖状态，是根据美国糖尿病协会的血液检测临界值从iEHR的医疗模块中获取的。样本量为4560人。多变量逻辑回归显示，利用iEHR的模型表现最佳。预测因素包括年龄、性别、种族、民族、缺失牙数量、牙科电子健康记录中至少有1个牙周袋≥5毫米的牙齿百分比，以及医疗电子健康记录中的超重/肥胖、高血压、高脂血症和吸烟状况。该模型的受试者工作特征曲线下面积为0.71（95%置信区间，0.69 - 0.72），灵敏度为0.70，特异性为0.62。在一系列人群中，根据某些患者特征，牙科护理团队成员可以在帮助识别未确诊糖尿病或糖尿病前期的牙科患者方面发挥作用。当牙科检查结果与医疗电子健康记录中的信息相结合时，预测的准确性会提高。糖尿病前期和糖尿病通常多年未被诊断出来。早期识别和治疗可改善血糖结果，并预防受影响个体的广泛发病，包括不良口腔健康后果。牙科诊所中可用的信息可被临床医生用于识别那些未被诊断或处于风险中的人；当与医疗电子健康记录中的信息相结合时，这种预测的准确性会提高。
