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 M...

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Autores principales: Sandeep Surendran, C. B. Mithun, Merlin Moni, Arun Tiwari, Manu Pradeep
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Publicado: BMC 2021
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spelling oai:doaj.org-article:8d70bc6d7f724198b4b0261fb99d05d92021-11-28T12:23:42ZCardiovascular risk assessment using ASCVD risk score in fibromyalgia: a single-centre, retrospective study using “traditional” case control methodology and “novel” machine learning10.1186/s42358-021-00229-w2523-3106https://doaj.org/article/8d70bc6d7f724198b4b0261fb99d05d92021-11-01T00:00:00Zhttps://doi.org/10.1186/s42358-021-00229-whttps://doaj.org/toc/2523-3106Abstract 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.Sandeep SurendranC. B. MithunMerlin MoniArun TiwariManu PradeepBMCarticleFibromyalgiaHeart disease risk factorsMachine learningCardiovascular diseasesDiseases of the musculoskeletal systemRC925-935Immunologic diseases. AllergyRC581-607ENAdvances in Rheumatology, Vol 61, Iss 1, Pp 1-9 (2021)
institution DOAJ
collection DOAJ
language EN
topic Fibromyalgia
Heart disease risk factors
Machine learning
Cardiovascular diseases
Diseases of the musculoskeletal system
RC925-935
Immunologic diseases. Allergy
RC581-607
spellingShingle Fibromyalgia
Heart disease risk factors
Machine learning
Cardiovascular diseases
Diseases of the musculoskeletal system
RC925-935
Immunologic diseases. Allergy
RC581-607
Sandeep Surendran
C. B. Mithun
Merlin Moni
Arun Tiwari
Manu Pradeep
Cardiovascular risk assessment using ASCVD risk score in fibromyalgia: a single-centre, retrospective study using “traditional” case control methodology and “novel” machine learning
description 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.
format article
author Sandeep Surendran
C. B. Mithun
Merlin Moni
Arun Tiwari
Manu Pradeep
author_facet Sandeep Surendran
C. B. Mithun
Merlin Moni
Arun Tiwari
Manu Pradeep
author_sort Sandeep Surendran
title Cardiovascular risk assessment using ASCVD risk score in fibromyalgia: a single-centre, retrospective study using “traditional” case control methodology and “novel” machine learning
title_short Cardiovascular risk assessment using ASCVD risk score in fibromyalgia: a single-centre, retrospective study using “traditional” case control methodology and “novel” machine learning
title_full Cardiovascular risk assessment using ASCVD risk score in fibromyalgia: a single-centre, retrospective study using “traditional” case control methodology and “novel” machine learning
title_fullStr Cardiovascular risk assessment using ASCVD risk score in fibromyalgia: a single-centre, retrospective study using “traditional” case control methodology and “novel” machine learning
title_full_unstemmed Cardiovascular risk assessment using ASCVD risk score in fibromyalgia: a single-centre, retrospective study using “traditional” case control methodology and “novel” machine learning
title_sort cardiovascular risk assessment using ascvd risk score in fibromyalgia: a single-centre, retrospective study using “traditional” case control methodology and “novel” machine learning
publisher BMC
publishDate 2021
url https://doaj.org/article/8d70bc6d7f724198b4b0261fb99d05d9
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