Hyperlipidemia and rheumatoid arthritis

Venetsanopoulou, Aliki; Drosos, Alexandros A.

doi:10.1016/b978-0-323-85857-1.00003-1

Cited by 2 publications

(2 citation statements)

References 169 publications

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“…This aligns with previous epidemiological studies which indicated that RA primarily affects women with a female-to-male ratio of approximately three to one, often presenting with higher disease activity and disability. 24 The elevated prevalence of RA among women is corroborated by various studies; for instance, Silva-Fernández et al found that women constituted 61.5% of RA patients in a population-based national survey in Spain. 25 Furthermore, a large epidemiological study across Middle Eastern regions reported an even higher proportion of female patients at 84.9%.…”

Section: Sun Et Al In Anmentioning

confidence: 84%

Predictive Factors of Methotrexate Monotherapy Success in Patients with Rheumatoid Arthritis in a National Referral Center: A Cohort Study

Hidayat,

Fauzia,

Parlindungan

et al. 2024

Preprint

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Background Methotrexate (MTX) remains the recommended first-line treatment for rheumatoid arthritis (RA); however, its response varies and is influenced by various factors. This study aimed to identify predictors of MTX monotherapy treatment success in an Indonesian cohort. Methods This retrospective cohort study included newly diagnosed RA patients receiving MTX monotherapy. Treatment success was defined as achieving remission or low disease activity according to Disease Activity Score-28 with erythrocyte sedimentation rate (DAS28-ESR) after 12 months of MTX therapy. The association between demographic, clinical, and laboratory factors and achieving therapy targets was evaluated using multivariate logistic regression analysis. Results Among 254 subjects, 59.4% achieved treatment success with MTX monotherapy, with remission attained in 33% and low disease activity in 26.4%. Most subjects were female (95.7%) with a mean age of 48 ± 11 years. Multivariate analysis revealed that lower disease activity (OR 1.97; 95% CI [1.04–3.72]), normal ESR (OR 2.58; 95% CI [1.05–6.34]), normoweight (OR 2.55, 95% CI [1.45–4.49]), and tender joint count ≤ 5 (OR 2.45, 95% CI [1.31–4.58]) were significant predictors of treatment success. Conclusion The rate of MTX monotherapy success in our study was 59.4%. Lower disease activity, normal ESR, normoweight, and fewer tender joints at baseline were significant predictors of treatment success.

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Section: Sun Et Al In Anmentioning

confidence: 84%

Predictive Factors of Methotrexate Monotherapy Success in Patients with Rheumatoid Arthritis in a National Referral Center: A Cohort Study

Hidayat,

Fauzia,

Parlindungan

et al. 2024

Preprint

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“…In the non-AI-based methods for CVD/stroke the risk was determined by computing the digital total of all the normalized risk values for the image-based biomarkers and then compartmentalized into different risk classes such as no-risk, low-risk, low-moderate risk, high-moderate risk, low-of-high risk, and high-of-high risk. This was computed using the AtheroEdge™ 2.0 system (AtheroPoint, Roseville, CA, USA) [ 36 , 220 , 221 , 258 , 259 , 260 , 261 , 262 , 263 , 264 , 265 ]. Image-based biomarkers, such as TPA, have shown to have a strong link with eGFR [ 266 ], and thus AI-based solution have adapted the usage of TPA in the modeling process.…”

Section: Deep Learning For Cvd/stroke Risk Assessment In Pd Patients ...mentioning

confidence: 99%

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Suri¹,

Maindarkar

Sauseng

et al. 2022

Diagnostics

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Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19.Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

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Hyperlipidemia and rheumatoid arthritis

Cited by 2 publications

References 169 publications

Predictive Factors of Methotrexate Monotherapy Success in Patients with Rheumatoid Arthritis in a National Referral Center: A Cohort Study

Predictive Factors of Methotrexate Monotherapy Success in Patients with Rheumatoid Arthritis in a National Referral Center: A Cohort Study

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

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