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

Abstract

Background In autoimmune inflammatory rheumatological diseases, routine cardiovascular risk assessment is becoming more important. As an increased cardiovascular disease (CVD) risk is recognized in patients with fibromyalgia (FM), a combination of traditional CVD risk assessment tool with Machine Learning (ML) predictive model could help to identify non-traditional CVD risk factors. Methods This study was a retrospective case–control study conducted at a quaternary care center in India. Female patients diagnosed with FM as per 2016 modified American College of Rheumatology 2010/2011 diagnostic criteria were enrolled; healthy age and gender-matched controls were obtained from Non-communicable disease Initiatives and Research at AMrita (NIRAM) study database. Firstly, FM cases and healthy controls were age-stratified into three categories of 18–39 years, 40–59 years, and ≥ 60 years. A 10 year and lifetime CVD risk was calculated in both cases and controls using the ASCVD calculator. Pearson chi-square test and Fisher's exact were used to compare the ASCVD risk scores of FM patients and controls across the age categories. Secondly, ML predictive models of CVD risk in FM patients were developed. A random forest algorithm was used to develop the predictive models with ASCVD 10 years and lifetime risk as target measures. Model predictive accuracy of the ML models was assessed by accuracy, f1-score, and Area Under 'receiver operating Curve' (AUC). From the final predictive models, we assessed risk factors that had the highest weightage for CVD risk in FM. Results A total of 139 FM cases and 1820 controls were enrolled in the study. FM patients in the age group 40–59 years had increased lifetime CVD risk compared to the control group (OR = 1.56, p = 0.043). However, CVD risk was not associated with FM disease severity and disease duration as per the conventional statistical analysis. ML model for 10-year ASCVD risk had an accuracy of 95% with an f1-score of 0.67 and AUC of 0.825. ML model for the lifetime ASCVD risk had an accuracy of 72% with an f1-score of 0.79 and AUC of 0.713. In addition to the traditional risk factors for CVD, FM disease severity parameters were important contributors in the ML predictive models. Conclusion FM patients of the 40–59 years age group had increased lifetime CVD risk in our study. Although FM disease severity was not associated with high CVD risk as per the conventional statistical analysis of the data, it was among the highest contributor to ML predictive model for CVD risk in FM patients. This also highlights that ML can potentially help to bridge the gap of non-linear risk factor identification.

Subclinical atherosclerosis in fibromyalgia syndrome

“…However, CVD risk was not associated with FMS disease severity and duration. Similarly, our study showed no correlation between disease duration and IMT 41 …”

Fibromyalgia: one year in review 2022

“…In line with this, a recent study has found that FM patients have a greater glycaemic response to glucose load after one hour and two hours, a larger glucose area under the curve than healthy controls ( 26), and higher mean glycated HbA1c levels (27). Given this prevalence of obesity and glucose intolerance, an Indian study calculated the 10-year and lifetime risk of developing CVD using the atherosclerotic cardiovascular disease (AS-CVD) calculator and found that FM patients aged 40-59 years had increased lifetime CVD risk than controls (OR = 1.56), regardless of FM severity or duration (28). It is therefore important to monitor the metabolic syndrome in FM patients, not least because careful weight control is a forerunner of an improvement in pain levels.…”