一种基于极端梯度提升算法的临床诊断模型，用于区分1型糖尿病。
A clinical diagnostic model based on an eXtreme Gradient Boosting algorithm to distinguish type 1 diabetes.
作者信息
Tang Xiaohan, Tang Rui, Sun Xingzhi, Yan Xiang, Huang Gan, Zhou Houde, Xie Guotong, Li Xia, Zhou Zhiguang
机构信息
Department of Metabolism and Endocrinology, the Second Xiangya Hospital, Central South University, Changsha, China.
Key Laboratory of Diabetes Immunology, Central South University, Ministry of Education, Changsha, China.
出版信息
Ann Transl Med. 2021 Mar;9(5):409. doi: 10.21037/atm-20-7115.
BACKGROUND
Accurate classification of type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in the early phase is crucial for individual precision treatment. This study aimed to develop a classification model having fewer and easier to access clinical variables to distinguish T1DM in newly diagnosed diabetes in adults.
METHODS
Clinical and laboratory data were collected from 15,206 adults with newly diagnosed diabetes in this cross-sectional study. This cohort represented 20 provinces and 4 municipalities in China. Types of diabetes were determined based on postprandial C-peptide (PCP) level and glutamic acid decarboxylase autoantibody (GADA) titer. We developed multivariable clinical diagnostic models using the eXtreme Gradient Boosting (XGBoost) algorithm. Classification variables included in the final model were based on their scores of importance. Model performance was evaluated by area under the receiver operating characteristic curve (ROC AUC), sensitivity, and specificity. The performance of models with different variable combinations was compared. Calibration intercept and slope were evaluated for the final model.
RESULTS
Among the newly diagnosed diabetes cohort, 1,465 (9.63%) persons had T1DM and 13,741 (90.37%) had T2DM. Body mass index (BMI) contributed the most to the model, followed by age of onset and hemoglobin A1c (HbA1c). Compared with models with other clinical variable combinations, a final model that integrated age of onset, BMI and HbA1c had relatively higher performance. The ROC AUC, sensitivity, and specificity for this model were 0.83 (95% CI, 0.80 to 0.85), 0.77, and 0.76, respectively. The calibration intercept and slope were 0.02 (95% CI, -0.03 to 0.06) and 0.90 (95% CI, 0.79 to 1.02), respectively, which suggested a good calibration performance.
CONCLUSIONS
Our classification model that integrated age of onset, BMI, and HbA1c could distinguish T1DM from T2DM, which provides a useful tool in assisting physicians in subtyping and precising treatment in diabetes.
背景
1型糖尿病（T1DM）和2型糖尿病（T2DM）的早期准确分类对于个体化精准治疗至关重要。本研究旨在开发一种分类模型，该模型具有更少且更容易获取的临床变量，以区分成人新诊断糖尿病中的T1DM。
方法
在这项横断面研究中，收集了15206例新诊断糖尿病成人的临床和实验室数据。该队列代表了中国20个省和4个直辖市。根据餐后C肽（PCP）水平和谷氨酸脱羧酶自身抗体（GADA）滴度确定糖尿病类型。我们使用极端梯度提升（XGBoost）算法开发了多变量临床诊断模型。最终模型中包含的分类变量基于其重要性得分。通过受试者工作特征曲线下面积（ROC AUC）、敏感性和特异性评估模型性能。比较了不同变量组合模型的性能。对最终模型评估校准截距和斜率。
结果
在新诊断糖尿病队列中，1465例（9.63%）患有T1DM，13741例（90.37%）患有T2DM。体重指数（BMI）对模型的贡献最大，其次是发病年龄和糖化血红蛋白（HbA1c）。与其他临床变量组合的模型相比，整合发病年龄、BMI和HbA1c的最终模型具有相对较高的性能。该模型的ROC AUC、敏感性和特异性分别为0.83（95%CI，0.80至0.85）、0.77和0.76。校准截距和斜率分别为0.02（95%CI，-0.03至0.06）和0.90（95%CI，0.79至1.02），表明校准性能良好。
结论
我们整合发病年龄、BMI和HbA1c的分类模型可以区分T1DM和T2DM，这为协助医生进行糖尿病亚型分类和精准治疗提供了一个有用的工具。
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