AimPeriodontal disease (PD) screening models based on a self‐reported questionnaire were previously established and externally validated. The aim of the present study is to explore whether the screening models could be modified to improve prediction performance; this methodology is called ‘updating’.MethodsUpdating the models for ‘total’ and ‘severe’ PD was performed using two datasets. One dataset from a previous study (n = 155) was used to explore the updating, and a second (n = 187, built for the current study) was used to validate whether updating improved performance. Updating was based on different statistical approaches, including model recalibration and revision. Discrimination and calibration were assessed after updating.ResultsFor ‘total’ PD, the update based on model revision improved its performance. However, still low AUCs were found: 0.64 (0.56–0.73) and 0.61 (0.53–0.69) with corresponding O:E ratios 1.00 (0.80–1.23) and 0.92 (0.75–1.13) in the update and validation cohorts, respectively. For ‘severe’ PD, performance of the original model without update performed still the best; AUCs were 0.72 (0.61–0.83) and 0.75 (0.66–0.84) in the update and validation cohorts, respectively, with corresponding O:E ratios 0.60 (0.38–0.84) and 0.62 (0.42–0.87).ConclusionsThe updating methodology did not further improve the performance of the original ‘severe’ PD screening model; it performed satisfactorily in the medical care setting. Despite updating attempts, the screening model for ‘total’ PD remained sub‐optimal. Screening for ‘severe’ PD can now be implemented in the medical care setting.

中文翻译：

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尝试根据医疗保健环境中自我报告的口腔健康问卷修改牙周筛查模型


基于自我报告问卷的牙周病 （PD） 筛查模型是先前建立的，并进行了外部验证。本研究的目的是探讨是否可以修改筛选模型以提高预测性能;这种方法称为 “更新”。方法使用两个数据集更新 'total' 和 'severe' PD 的模型。来自先前研究的一个数据集 （n = 155） 用于探索更新，第二个数据集 （n = 187，为当前研究构建） 用于验证更新是否提高了性能。更新基于不同的统计方法，包括模型重新校准和修订。更新后评估鉴别和校准。结果对于 'total' PD，基于模型修订的更新提高了其性能。然而，发现 AUC 仍然很低：0.64 （0.56-0.73） 和 0.61 （0.53-0.69），相应的 O：E 比率分别为 1.00 （0.80-1.23） 和 0.92 （0.75-1.13） 在更新和验证队列中。对于“严重”PD，没有更新的原始模型的性能仍然表现最好;更新和验证队列中的 AUC 分别为 0.72 （0.61-0.83） 和 0.75 （0.66-0.84），相应的 O：E 比为 0.60 （0.38-0.84） 和 0.62 （0.42-0.87）。结论更新的方法并未进一步提高原始“重度”PD 筛查模型的性能;它在医疗保健环境中的表现令人满意。尽管进行了更新尝试，但“总”PD 的筛查模型仍然不理想。现在可以在医疗保健环境中实施“严重”PD 筛查。