目的:如有必要,医疗专业人员应建议患者去看牙医。由于缺乏时间和知识,通常不进行牙周炎筛查。为了缓解这个问题,总的筛选模型(自己的牙齿/牙龈健康,牙龈治疗,牙齿松动,漱口水使用,和年龄)/严重牙周炎(牙龈治疗,牙齿松动,牙齿外观,漱口水使用,年龄,和性别)在阿姆斯特丹牙科学术中心(ACTA)[1]开发了医疗环境。本研究的目的是在门诊医疗环境中外部验证该工具。
方法:患者被要求在门诊医疗环境中作为验证队列。进行了自我报告的口腔健康问卷,收集了人口统计数据,并进行牙周检查。算法区分表示为接收器工作特征曲线下的面积(AUROCC)。灵敏度,特异性,并计算阳性和阴性预测值。制作校准图。
结果:对于预测全牙周炎,AUROCC为0.59,敏感性为49%,特异性为68%.PPV为57%,NPV为55%。为了预测严重的牙周炎,AUROCC为0.73,敏感性为71%,特异性为63%.PPV为39%,NPV87%。
结论:在目前的医学研究人群中,该算法对严重牙周炎的性能已足够。建议在非牙科学校人群中进一步对牙周炎算法进行外部验证。
结论:因为全科医生有义务筛查患者的牙周炎,我们的总体目标是,他们可以在医疗环境中使用预测模型而无需口腔检查。
OBJECTIVE: Medical professionals should advise their patients to visit a dentist if necessary. Due to the lack of time and knowledge, screening for periodontitis is often not done. To alleviate this problem, a screening model for total (own teeth/gum health, gum treatment, loose teeth, mouthwash use, and age)/severe periodontitis (gum treatment, loose teeth, tooth appearance, mouthwash use, age, and sex) in a medical care setting was developed in the Academic Center of Dentistry Amsterdam (ACTA) [1]. The purpose of the present study was to externally validate this tool in an outpatient medical setting.
METHODS: Patients were requited in an outpatient medical setting as the validation cohort. The self-reported oral health questionnaire was conducted, demographic data were collected, and periodontal examination was performed. Algorithm discrimination was expressed as the area under the receiver operating characteristic curve (AUROCC). Sensitivity, specificity, and positive and negative predictive values were calculated. Calibration plots were made.
RESULTS: For predicting total periodontitis, the AUROCC was 0.59 with a sensitivity of 49% and specificity of 68%. The PPV was 57% and the NPV scored 55%. For predicting severe periodontitis, the AUROCC was 0.73 with a sensitivity of 71% and specificity of 63%. The PPV was 39% and the NPV 87%.
CONCLUSIONS: The performance of the algorithm for severe periodontitis is found to be sufficient in the current medical study population. Further external validation of periodontitis algorithms in non-dental school populations is recommended.
CONCLUSIONS: Because general physicians are obligated to screen patients for periodontitis, it is our general goal that they can use a prediction model in medical settings without an oral examination.