Cardiovascular risk assessment using ASCVD risk score in fibromyalgia – A single centre, retrospective study using “traditional” case-control methodology and “novel” machine learning.

The objective of the current study is to compare the cardiovascular risk using AtheroSclerotic CardioVascular Disease (ASCVD) risk score in fibromyalgia patients versus normal controls as a traditional case-control study as well as apply supervised ML method for the development and training of a classification model to help predict the ASCVD risk category in fibromyalgia patients. 139 patients with fibromyalgia who visited the rheumatology out-patient department of the Quaternary centre in South India from 1st September 2016 to 31st January 2019; and who satisfied the inclusion criteria were included in the study. Fibromyalgia Impact Questionnaire Revised (FIQR) and Brief Pain Inventory (BPI) along with routine clinical and biochemical data of were included for analysis. 10 year and Lifetime mortality risk factors were analyzed using case control and supervised machine learning algorithms.本研究旨在开展两项核心工作：其一，作为传统病例对照研究，对比纤维肌痛患者与正常对照者的动脉粥样硬化性心血管疾病（AtheroSclerotic CardioVascular Disease, ASCVD）风险评分，以评估两组人群的心血管疾病风险差异；其二，应用监督式机器学习方法开发并训练分类模型，辅助预测纤维肌痛患者的ASCVD风险等级。本研究纳入2016年9月1日至2019年1月31日期间，于印度南部四级医疗中心风湿科门诊就诊且符合纳入标准的139例纤维肌痛患者。分析所用数据包括纤维肌痛影响问卷修订版（Fibromyalgia Impact Questionnaire Revised, FIQR）、简明疼痛量表（Brief Pain Inventory, BPI），以及受试者的常规临床与生化检验资料。本研究采用病例对照设计与监督式机器学习算法，对10年及终身死亡风险相关危险因素展开分析。