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1.浙江中医药大学基础医学院,浙江省杭州市滨江区滨文路548号,310000
2.中国信息通信研究院人工智能(杭州)研究中心
Received:15 April 2023,
Revised:2023-09-05,
Published:17 December 2023
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李宣霖,王憭瑶,黄琳等.类风湿关节炎前状态病情转归动态预测模型的探索与构建[J].中医杂志,2023,64(24):2516-2521.
LI Xuanlin,WANG Liaoyao,HUANG Lin,et al.Exploration and Construction of Dynamic Prediction Model in the Pre-Rheumatoid Arthritis State[J].Journal of Traditional Chinese Medicine,2023,64(24):2516-2521.
李宣霖,王憭瑶,黄琳等.类风湿关节炎前状态病情转归动态预测模型的探索与构建[J].中医杂志,2023,64(24):2516-2521. DOI: 10.13288/j.11-2166/r.2023.24.007.
LI Xuanlin,WANG Liaoyao,HUANG Lin,et al.Exploration and Construction of Dynamic Prediction Model in the Pre-Rheumatoid Arthritis State[J].Journal of Traditional Chinese Medicine,2023,64(24):2516-2521. DOI: 10.13288/j.11-2166/r.2023.24.007.
类风湿关节炎前状态(Pre-RA)是类风湿关节炎无症状表现的状态,其病情演变具有复杂性、动态性与显著个体差异性。动态预测模型对动态精准识别个体的风险与及时采取科学的防控措施有重要指导价值。认为将动态预测模型应用于Pre-RA中医临床研究,有望预测Pre-RA向类风湿关节炎转化的临界状态,揭示Pre-RA病情演变规律,同时可反映中医临床研究的外部真实性,并可作为临床疗效评价方法的补充。据此设想基于Pre-RA中医临床试验数据,通过动态预测模型算法构建Pre-RA病情转归动态预测模型,并应用长期随访队列数据进行外部验证,为Pre-RA病情演变的科学预警与临床决策提供依据。
Pre-rheumatoid arthritis (Pre-RA) is the asymptomatic state of rheumatoid arthritis (RA), and its progression showed complexity, dynamics and significant individual differences. The dynamic prediction model is valuable in identifying individual risks and taking timely preventive and control measures. It is believed that applying the dynamic prediction model to traditional Chinese medicine (TCM) clinical studies of Pre-RA is expected to predict the critical state of Pre-RA to RA, reveal the evolution of Pre-RA, reflect the external authenticity of the TCM clinical studies, and complement the clinical efficacy evaluation method. Accordingly, it is envisioned to construct a dynamic prediction model for Pre-RA progression based on TCM clinical trial data through the dynamic prediction model algorithm, and to apply long-term follow-up cohort data for external validation, thereby providing a basis for scientific warning and clinical decision-making on the evolution of Pre-RA.
中华医学会风湿病学分会 . 2018中国类风湿关节炎诊疗指南 [J]. 中华内科杂志 , 2018 , 57 ( 4 ): 242 - 251 .
FINCKH A , GILBERT B , HODKINSON B , et al . Global epidemiology of rheumatoid arthritis [J]. Nat Rev Rheumatol , 2022 , 18 ( 10 ): 591 - 602 .
JIN S , LI M , FANG Y , et al . Chinese Registry of rheumatoid arthritis (CREDIT): Ⅱ. prevalence and risk factors of major comorbidities in Chinese patients with rheumatoid arthritis [J]. Arthritis Res Ther , 2017 , 19 ( 1 ): 251 .
GERLAG DM , RAZA K , VAN BAARSEN LG , et al . EULAR recommendations for terminology and research in individuals at risk of rheumatoid arthritis: report from the Study Group for Risk Factors for Rheumatoid Arthritis [J]. Ann Rheum Dis , 2012 , 71 ( 5 ): 638 - 641 .
曾小峰 , 朱松林 , 谭爱春 , 等 . 我国类风湿关节炎疾病负担和生存质量研究的系统评价 [J]. 中国循证医学杂志 , 2013 , 13 ( 3 ): 300 - 307 .
田新平 , 李梦涛 , 曾小峰 . 我国类风湿关节炎诊治现状与挑战:来自中国类风湿关节炎2019年年度报告 [J]. 中华内科杂志 , 2021 , 60 ( 7 ): 593 - 598 .
WINTHROP KL , ISAACS JD , MEASE PJ , et al . Unmet need in rheumatology: reports from the Advances in Targeted Therapies meeting, 2022 [J]. Ann Rheum Dis , 2023 , 82 ( 5 ): 594 - 598 .
黄聿峰 , 林久灶 , 陈挺霖 . 早期类风湿关节炎短期复发风险的列线图预测模型开发与评估 [J]. 现代医学 , 2022 , 50 ( 5 ): 608 - 612 .
COLLINS GS , REITSMA JB , ALTMAN DG , et al . Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement [J]. BMJ , 2015 , 350 : g7594 . doi: 10.1136/bmj.g7594 http://dx.doi.org/10.1136/bmj.g7594 .
谷鸿秋 , 周支瑞 , 章仲恒 , 等 . 临床预测模型:基本概念、应用场景及研究思路 [J]. 中国循证心血管医学杂志 , 2018 , 10 ( 12 ): 1454 - 1456,1462 .
宋若齐 , 吴疏桐 , 王闯世 . 医学研究中常见动态预测模型方法介绍 [J]. 中国循证医学杂志 , 2022 , 22 ( 10 ): 1224 - 1232 .
DAVIS SE , LASKO TA , CHEN G , et al . Calibration drift in regression and machine learning models for acute kidney injury [J]. J Am Med Inform Assoc , 2017 , 24 ( 6 ): 1052 - 1061 .
谷鸿秋 , 王俊峰 , 章仲恒 , 等 . 临床预测模型:模型的建立 [J]. 中国循证心血管医学杂志 , 2019 , 11 ( 1 ): 14 - 16,23 .
RIZOPOULOS D , MOLENBERGHS G , LESAFFRE EMEH . Dynamic predictions with time-dependent covariates in survival analysis using joint modeling and landmarking [J]. Biom J , 2017 , 59 ( 6 ): 1261 - 1276 .
WELTEN M , DE KROON MLA , RENDERS CM , et al . Repeatedly measured predictors: a comparison of methods for prediction modeling [J]. Diagn Progn Res , 2018 , 2 : 5 . doi: 10.1186/s41512-018-0024-7 http://dx.doi.org/10.1186/s41512-018-0024-7 .
BULL LM , LUNT M , MARTIN GP , et al . Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods [J]. Diagn Progn Res , 2020 , 4 : 9 . doi: 10.1186/s41512-020-00078-z http://dx.doi.org/10.1186/s41512-020-00078-z .
李笑丛 , 王闯世 , 郝军 , 等 . 临床预测模型校准与更新方法介绍及R软件实现 [J]. 中国循证医学杂志 , 2023 , 23 ( 1 ): 112 - 119 .
翟映红 , 陈琪 , 韩贺东 , 等 . 联合模型介绍及在医学研究中的应用 [J]. 中华流行病学杂志 , 2019 , 40 ( 11 ): 1456 - 1457 .
ANDRINOPOULOU ER , HARHAY MO , RATCLIFFE SJ , et al . Reflection on modern methods: dynamic prediction using joint models of longitudinal and time-to-event data [J]. Int J Epidemiol , 2021 , 50 ( 5 ): 1731 - 1743 .
RIZOPOULOS D . Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data [J]. Biometrics , 2011 , 67 ( 3 ): 819 - 829 .
MAUGUEN A , RACHET B , MATHOULIN-PELISSIER S , et al . Dynamic prediction of risk of death using history of cancer recurrences in joint frailty models [J]. Stat Med , 2013 , 32 ( 30 ): 5366 - 5380 .
BLANCHE P , PROUST-LIMA C , LOUBERE L , et al . Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks [J]. Biometrics , 2015 , 71 ( 1 ): 102 - 113 .
PAPAGEORGIOU G , MOKHLES MM , TAKKENBERG JJM , et al . Individualized dynamic prediction of survival with the presence of intermediate events [J]. Stat Med , 2019 , 38 ( 30 ): 5623 - 5640 .
肖宇飞 , 冯佳宁 , 王晓璇 , 等 . 利用数据库数据采用联合模型动态预测312例肝硬化患者预后的观察分析 [J]. 山东大学学报(医学版) , 2020 , 58 ( 9 ): 71 - 76 .
SHEN F , LI L . Backward joint model and dynamic prediction of survival with multivariate longitudinal data [J]. Stat Med , 2021 , 40 ( 20 ): 4395 - 4409 .
HE J , MCGEE DL , NIU X . Application of the Bayesian dynamic survival model in medicine [J]. Stat Med , 2010 , 29 ( 3 ): 347 - 360 .
YANG M , LUO S , DESANTIS S . Bayesian quantile regression joint models: inference and dynamic predictions [J]. Stat Methods Med Res , 2019 , 28 ( 8 ): 2524 - 2537 .
BAUER C , ZHANG K , LI W , et al . Small area forecasting of opioid-related mortality: Bayesian spatiotemporal dynamic modeling approach [J]. JMIR Public Health Surveill , 2023 , 9 : e41450 . doi: 10.2196/41450 http://dx.doi.org/10.2196/41450 .
THORSEN-MEYER HC , NIELSEN AB , NIELSEN AP , et al . Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records [J]. Lancet Digit Health , 2020 , 2 ( 4 ): e179 - e191 .
ZENG Z , TANG X , LIU Y , et al . Interpretable recurrent neural network models for dynamic prediction of the extubation failure risk in patients with invasive mechanical ventilation in the intensive care unit [J]. BioData Min , 2022 , 15 ( 1 ): 21 .
FAHIMI F , GUO Y , TONG SC , et al . A vital signs telemonitoring programme Improves the Dynamic Prediction of Readmission Risk in Patients with Heart Failure [J]. AMIA Annu Symp Proc , 2020 : 432 - 441 . PMID: 33936416 .
RAKIEH C , NAM JL , HUNT L , et al . Predicting the development of clinical arthritis in anti-CCP positive individuals with non-specific musculoskeletal symptoms: a prospective observational cohort study [J]. Ann Rheum Dis , 2015 , 74 ( 9 ): 1659 - 1666 .
包洁 , 黄琳 , 谢志军 , 等 . 从伏邪理论论治类风湿关节炎前状态 [J]. 中华中医药杂志 , 2022 , 37 ( 8 ): 4539 - 4542 .
周东海 , 王俏 , 冯炯 , 等 . 脾虚风湿痹阻为类风湿关节炎前状态主要病机 [J]. 中医杂志 , 2020 , 61 ( 7 ): 590 - 593 .
王凯丽 , 许小芬 , 杨辉 , 等 . 类风湿关节炎前状态的转归及中医药防治思路 [J]. 中医杂志 , 2021 , 62 ( 14 ): 1266 - 1268 .
TANG Y , LI H , HUANG L , et al . Yunpi Qufeng Chushi Formula for pre-rheumatoid arthritis: study protocol for a multiple-center, double-blind, placebo-controlled randomized controlled trial [J]. Front Pharmacol , 2022 , 13 : 793394 . doi: 10.3389/fphar.2022.793394 http://dx.doi.org/10.3389/fphar.2022.793394 .
费宇彤 , 柴倩云 , 夏如玉 , 等 . 真实世界临床研究证据分级的思考与初步探索 [J]. 中国循证医学杂志 , 2022 , 22 ( 10 ): 1206 - 1211 .
中华中医药学会 . 中医药重大科学问题和工程技术难题 [J]. 中医杂志 , 2019 , 60 ( 12 ): 991 - 1000 .
张英 , 陈红 , 于长禾 . 中医辨证论治临床疗效评价阶梯递进式模型的构建 [J]. 中医杂志 , 2020 , 61 ( 19 ): 1696 - 1701 .
邱瑞瑾 , 赵晨 , 钟长鸣 , 等 . 中医临床疗效个体化评价应用混合方法研究设计思路 [J]. 中国循证医学杂志 , 2020 , 20 ( 8 ): 973 - 978 .
符宇 , 邵明义 , 燕树勋 , 等 . 真实世界研究与中医临床疗效评价 [J]. 中医杂志 , 2019 , 60 ( 7 ): 546 - 550 .
关曼柯 , 魏旭煦 , 赵晨 , 等 . 循证决策模式下中医临床个体化疗效评价研究的定位与未来发展路径 [J]. 中国循证医学杂志 , 2020 , 20 ( 10 ): 1124 - 1129 .
姚帅君 , 闫敬来 , 杜彩凤 , 等 . 基于集成学习构建围绝经期综合征中医智能诊断模型 [J]. 中医杂志 , 2023 , 64 ( 6 ): 572 - 580 .
王琦琪 , 戴家佳 , 崔熊卫 . 基于集成学习模型的糖尿病患病风险预测研究 [J]. 软件导刊 , 2022 , 21 ( 4 ): 62 - 66 .
MOONS KG , ALTMAN DG , REITSMA JB , et al . Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration [J]. Ann Intern Med , 2015 , 162 ( 1 ): W1 - W73 .
SIONTIS GC , TZOULAKI I , CASTALDI PJ , et al . External validation of new risk prediction models is infrequent and reveals worse prognostic discrimination [J]. J Clin Epidemiol , 2015 , 68 ( 1 ): 25 - 34 .
王俊峰 , 章仲恒 , 周支瑞 , 等 . 临床预测模型: 模型的验证 [J]. 中国循证心血管医学杂志 , 2019 , 11 ( 2 ): 141 - 144 .
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