This study reviews and appraises the methodological and reporting quality of prediction models for tooth loss in periodontitis patients, including the use of regression and machine learning models. Studies involving prediction modeling for tooth loss in periodontitis patients were screened. A search was performed in MEDLINE via PubMed, Embase, and CENTRAL up to 12 February 2022, with citation chasing. Studies exploring model development or external validation studies for models assessing tooth loss in periodontitis patients for clinical use at any time point, with all prediction horizons in English, were considered. Studies were excluded if models were not developed for use in periodontitis patients, were not developed or validated on any data set, predicted outcomes other than tooth loss, or were prognostic factor studies. The CHARMS checklist was used for data extraction, TRIPOD to assess reporting quality, and PROBAST to assess the risk of bias. In total, 4,661 records were screened, and 45 studies were included. Only 26 studies reported any kind of performance measure. The median C-statistic reported was 0.671 (range, 0.57–0.97). All studies were at a high risk of bias due to inappropriate handling of missing data (96%), inappropriate evaluation of model performance (92%), and lack of accounting for model overfitting in evaluating model performance (68%). Many models predicting tooth loss in periodontitis are available, but studies evaluating these models are at a high risk of bias. Model performance measures are likely to be overly optimistic and might not be replicated in clinical use. While this review is unable to recommend any model for clinical practice, it has collated the existing models and their model performance at external validation and their associated sample sizes, which would be helpful to identify promising models for future external validation studies.

中文翻译：

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预测牙周炎牙齿脱落的预后模型的系统评价

本研究回顾和评估了牙周炎患者牙齿脱落预测模型的方法和报告质量，包括回归和机器学习模型的使用。筛选了涉及牙周炎患者牙齿脱落预测模型的研究。通过 PubMed、Embase 和 CENTRAL 在 MEDLINE 中进行了截至 2022 年 2 月 12 日的检索，并进行了引文追踪。考虑了探索模型开发或外部验证研究的模型，以评估牙周炎患者牙齿缺失的模型，以供临床使用，在任何时间点，所有预测范围均为英语。如果模型不是为牙周炎患者开发的，没有在任何数据集上开发或验证，预测除牙齿脱落以外的结果，或者是预后因素研究，则研究被排除。 CHARMS 检查表用于数据提取，TRIPOD 用于评估报告质量，PROBAST 用于评估偏倚风险。总共筛选了 4,661 条记录，纳入了 45 项研究。只有 26 项研究报告了任何类型的绩效衡量标准。报告的 C 统计中位数为 0.671（范围，0.57-0.97）。由于对缺失数据的处理不当（96％）、对模型性能的评估不当（92％）以及在评估模型性能时缺乏对模型过度拟合的考虑（68％），所有研究都存在很高的偏倚风险。有许多模型可以预测牙周炎导致的牙齿脱落，但评估这些模型的研究存在很高的偏倚风险。模型性能测量可能过于乐观，并且可能无法在临床使用中复制。虽然本次综述无法推荐任何用于临床实践的模型，但它整理了现有模型及其在外部验证中的模型性能及其相关的样本量，这将有助于为未来的外部验证研究确定有前景的模型。