Classification of painful or painless diabetic peripheral neuropathy and identification of the most powerful predictors using machine learning models in large cross-sectional cohorts.
Classification of painful or painless diabetic peripheral neuropathy and identification of the most powerful predictors using machine learning models in large cross-sectional cohorts.
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DOI：
10.1186/s12911-022-01890-x
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发表时间：
2022-05-29
影响因子：
3.5
通讯作者：
Bennett, David L. H.
中科院分区：
文献类型：
--
作者：
Baskozos, Georgios;Themistocleous, Andreas C.;Hebert, Harry L.;Pascal, Mathilde M., V;John, Jishi;Callaghan, Brian C.;Laycock, Helen;Granovsky, Yelena;Crombez, Geert;Yarnitsky, David;Rice, Andrew S. C.;Smith, Blair H.;Bennett, David L. H.
为了改善痛性糖尿病周围神经病变(DPN)及其相关并发症的治疗，需要对痛性DPN的病理生理和危险因素有更好的了解。使用协调队列(N = 1230)，我们建立了模型，使用生活质量(EQ5D)、生活方式(吸烟、饮酒)、人口统计学(年龄、性别)、个性和心理特征(焦虑、抑郁、个性特征)、生化(HbA1c)和临床变量(体重指数、住院时间和年轻时的创伤)作为预测因素，对疼痛和无痛DPN进行分类。对随机森林、自适应回归样条法和朴素贝叶斯机器学习模型进行训练，对疼痛/无痛DPN进行分类。在大型横断面队列(N = 935)中使用交叉验证来评估它们的性能，并在基于人群的大型队列(N = 295)中进行外部验证。使用特定于模型的指标对变量的重要性进行排名，并在全球层面汇总和评估预测因素的边际影响。使用马修斯相关系数(MCC)进行模型选择，并使用MCC、准确度/召回率曲线下面积(AUPRC)和准确度来量化验证集中的模型性能。随机森林(MCC = 0.28，AUPRC = 0.76)和自适应回归样条法(MCC = 0.29，AUPRC = 0.77)是性能最好的模型，并且在训练和验证数据集之间表现出最小的性能下降。EQ5D指数、10项人格维度、HbA1c、抑郁和焦虑t值、年龄和体重指数始终是区分疼痛和无痛DPN的最有力的预测指标。在大的横断面队列上训练的机器学习模型能够在独立的基于人群的数据集中准确地对疼痛或无痛DPN进行分类。痛苦的DPN与更多的抑郁、焦虑和某些人格特征有关。它还与自我报告的生活质量较差、年龄较小、血糖控制较差和身体质量指数(BMI)较高有关。在存在缺失值和噪声数据集的情况下，模型在现实条件下表现出了良好的性能。这些模型既可以用于临床环境，帮助患者根据疼痛DPN的风险进行分层，也可以根据用户的输入返回广泛的风险类别。模型的表现和校准表明，在这两种情况下，他们都可以通过改变BMI和HbA1c控制等可改变的因素来改善诊断和结果，并采取更早的预防或支持措施，如心理干预。网上版载有补充材料，可在10.1186/s12911-022-01890-x查阅。
To improve the treatment of painful Diabetic Peripheral Neuropathy (DPN) and associated co-morbidities, a better understanding of the pathophysiology and risk factors for painful DPN is required. Using harmonised cohorts (N = 1230) we have built models that classify painful versus painless DPN using quality of life (EQ5D), lifestyle (smoking, alcohol consumption), demographics (age, gender), personality and psychology traits (anxiety, depression, personality traits), biochemical (HbA1c) and clinical variables (BMI, hospital stay and trauma at young age) as predictors. The Random Forest, Adaptive Regression Splines and Naive Bayes machine learning models were trained for classifying painful/painless DPN. Their performance was estimated using cross-validation in large cross-sectional cohorts (N = 935) and externally validated in a large population-based cohort (N = 295). Variables were ranked for importance using model specific metrics and marginal effects of predictors were aggregated and assessed at the global level. Model selection was carried out using the Mathews Correlation Coefficient (MCC) and model performance was quantified in the validation set using MCC, the area under the precision/recall curve (AUPRC) and accuracy. Random Forest (MCC = 0.28, AUPRC = 0.76) and Adaptive Regression Splines (MCC = 0.29, AUPRC = 0.77) were the best performing models and showed the smallest reduction in performance between the training and validation dataset. EQ5D index, the 10-item personality dimensions, HbA1c, Depression and Anxiety t-scores, age and Body Mass Index were consistently amongst the most powerful predictors in classifying painful vs painless DPN. Machine learning models trained on large cross-sectional cohorts were able to accurately classify painful or painless DPN on an independent population-based dataset. Painful DPN is associated with more depression, anxiety and certain personality traits. It is also associated with poorer self-reported quality of life, younger age, poor glucose control and high Body Mass Index (BMI). The models showed good performance in realistic conditions in the presence of missing values and noisy datasets. These models can be used either in the clinical context to assist patient stratification based on the risk of painful DPN or return broad risk categories based on user input. Model’s performance and calibration suggest that in both cases they could potentially improve diagnosis and outcomes by changing modifiable factors like BMI and HbA1c control and institute earlier preventive or supportive measures like psychological interventions. The online version contains supplementary material available at 10.1186/s12911-022-01890-x.
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影响因子：
3.7
作者：
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通讯作者：
Hartemann A
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