重新校准非实验室风险模型,以估算香港初级保健中的糖尿病前期/糖尿病风险。

IF 3 Q1 PRIMARY HEALTH CARE
Will H G Cheng, Weinan Dong, Emily T Y Tse, Linda Chan, Carlos K H Wong, Weng Y Chin, Laura E Bedford, Wai Kit Ko, David V K Chao, Kathryn C B Tan, Cindy L K Lam
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引用次数: 0

摘要

导言/目的:一个基于香港华裔人口的非实验室糖尿病前期/糖尿病(Pre-DM/DM)风险预测模型在初级保健(PC)人群中显示出良好的外部区分度,但估计的风险水平明显低于观察到的发病率,表明校准效果不佳。本研究探讨了重新校准/更新方法能否提高该模型在 PC 中估计个体风险的准确性:我们对模型的预测因素和血液检测结果进行了二次分析,研究对象是 2021 年 4 月至 2022 年 1 月在香港 PC 诊所招募的 919 名既往未确诊为 DM 的中国成年人。数据集被随机分成两半,即训练集和测试集。根据七步方法对模型进行了重新校准/更新,包括模型重新校准、修订和扩展方法。主要结果是重新校准/更新后模型的校准情况,用校准图表示。此外,还通过接收者操作特征曲线下面积(AUC-ROC)对模型的辨别能力进行了评估:结果:在不改变预测因子系数的情况下,重新校准模型的回归常数提高了模型的准确性(校准图截距:-0.01,斜率:0.69)。更广泛的方法无法进一步提高模型的准确性。所有重新校准/更新的模型的 AUC-ROC 与原始模型相似:结论:简单的重新校准方法可以使香港中文大学的测前/测后模型适用于不同测前概率的 PC 群体。重新校准后的模型可作为第一步筛查工具,也可作为监测DM/DM前风险随时间或干预后变化的措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recalibration of a Non-Laboratory-Based Risk Model to Estimate Pre-Diabetes/Diabetes Mellitus Risk in Primary Care in Hong Kong.

Introduction/objectives: A non-laboratory-based pre-diabetes/diabetes mellitus (pre-DM/DM) risk prediction model developed from the Hong Kong Chinese population showed good external discrimination in a primary care (PC) population, but the estimated risk level was significantly lower than the observed incidence, indicating poor calibration. This study explored whether recalibrating/updating methods could improve the model's accuracy in estimating individuals' risks in PC.

Methods: We performed a secondary analysis on the model's predictors and blood test results of 919 Chinese adults with no prior DM diagnosis recruited from PC clinics from April 2021 to January 2022 in HK. The dataset was randomly split in half into a training set and a test set. The model was recalibrated/updated based on a seven-step methodology, including model recalibrating, revising and extending methods. The primary outcome was the calibration of the recalibrated/updated models, indicated by calibration plots. The models' discrimination, indicated by the area under the receiver operating characteristic curves (AUC-ROC), was also evaluated.

Results: Recalibrating the model's regression constant, with no change to the predictors' coefficients, improved the model's accuracy (calibration plot intercept: -0.01, slope: 0.69). More extensive methods could not improve any further. All recalibrated/updated models had similar AUC-ROCs to the original model.

Conclusion: The simple recalibration method can adapt the HK Chinese pre-DM/DM model to PC populations with different pre-test probabilities. The recalibrated model can be used as a first-step screening tool and as a measure to monitor changes in pre-DM/DM risks over time or after interventions.

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来源期刊
CiteScore
4.80
自引率
2.80%
发文量
183
审稿时长
15 weeks
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