Systematic Review of Prognosis Models in Predicting Tooth Loss in Periodontitis.

Journal of dental research Pub Date : 2024-06-01 Epub Date: 2024-05-10 DOI:10.1177/00220345241237448
D Y Chow, J R H Tay, G G Nascimento
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Abstract

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.

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预测牙周炎牙齿脱落的预后模型的系统性回顾。
本研究回顾并评估了牙周炎患者牙齿缺失预测模型的方法和报告质量,包括回归模型和机器学习模型的使用。研究筛选了涉及牙周炎患者牙齿缺失预测模型的研究。截至 2022 年 2 月 12 日,通过 PubMed、Embase 和 CENTRAL 在 MEDLINE 中进行了检索,并进行了引文追逐。这些研究探讨了牙周炎患者在任何时间点用于临床的牙齿缺失评估模型的开发或外部验证研究,所有预测范围均为英语。如果所开发的模型不是用于牙周炎患者、未在任何数据集上开发或验证、预测的结果不是牙齿脱落,或者是预后因素研究,则排除这些研究。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。共筛选出 4,661 条记录,并纳入了 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 统计量中位数为 0.671(范围为 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没有考虑模型的过度拟合(68%),所有研究都存在较高的偏倚风险。目前有许多预测牙周炎患者牙齿脱落的模型,但评估这些模型的研究存在较高的偏倚风险。模型的性能指标可能过于乐观,在临床使用中可能无法复制。虽然本综述无法向临床实践推荐任何模型,但它整理了现有模型及其在外部验证中的模型性能以及相关样本量,这将有助于为未来的外部验证研究确定有前途的模型。
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