Prognostic factors of disability in relapsing remitting multiple sclerosis

Briggs, Farren; Thompson, Nicolas R.; Conway, Devon

doi:10.1016/j.msard.2019.01.045

Cited by 56 publications

(60 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Family history of MS seems to be a predictor of a shorter time to conversion to SPMS [7]. Environmental and modifiable factors, such as smoke and high body mass index, contribute to impairments in walking speed, overall disability and depression [8]. Vitamin D deficiency has been associated with worse outcome as well [9].…”

Section: Introductionmentioning

confidence: 99%

Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis

et al. 2020

View full text Add to dashboard Cite

Multiple Sclerosis (MS) progresses at an unpredictable rate, but predictions on the disease course in each patient would be extremely useful to tailor therapy to the individual needs. We explore different machine learning (ML) approaches to predict whether a patient will shift from the initial Relapsing-Remitting (RR) to the Secondary Progressive (SP) form of the disease, using only "real world" data available in clinical routine. The clinical records of 1624 outpatients (207 in the SP phase) attending the MS service of Sant'Andrea hospital, Rome, Italy, were used. Predictions at 180, 360 or 720 days from the last visit were obtained considering either the data of the last available visit (Visit-Oriented setting), comparing four classical ML methods (Random Forest, Support Vector Machine, K-Nearest Neighbours and AdaBoost) or the whole clinical history of each patient (History-Oriented setting), using a Recurrent Neural Network model, specifically designed for historical data. Missing values were handled by removing either all clinical records presenting at least one missing parameter (Feature-saving approach) or the 3 clinical parameters which contained missing values (Record-saving approach). The performances of the classifiers were rated using common indicators, such as Recall (or Sensitivity) and Precision (or Positive predictive value). In the visit-oriented setting, the Record-saving approach yielded Recall values from 70% to 100%, but low Precision (5% to 10%), which however increased to 50% when considering only predictions for which the model returned a probability above a given "confidence threshold". For the History-oriented setting, both indicators increased as prediction time lengthened, reaching values of 67% (Recall) and 42% (Precision) at 720 days. We show how "real world" data can be effectively used to forecast the evolution of MS, leading to high Recall values and propose innovative approaches to improve Precision towards clinically useful values.

show abstract

Section: Introductionmentioning

confidence: 99%

Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Recent studies have demonstrated that lower SES is associated with higher risk of disability progression in the UK and Canada (Harding et al, 2019) and a single-center, US cohort. (Briggs et al, 2019) A prior study in MS (Marrie et al, 2006) showed that accounting for SES might explain some of differing outcomes between Caucasians and African Americans. Most prior studies have focused upon physical disability and associated outcomes.…”

Section: Introductionmentioning

confidence: 99%

Socioeconomic status and race are correlated with affective symptoms in multiple sclerosis

Wang

Tian

Fitzgerald

et al. 2020

Multiple Sclerosis and Related Disorders

View full text Add to dashboard Cite

Objective-Investigate the relationship between socioeconomic status (SES) and race with selfreported fatigue, depression, and anxiety levels in multiple sclerosis (MS). Methods-Cross-sectional review of the MS Partners Advancing Technology and Health Solutions (MS PATHS) database for adults with MS in the United States. We evaluated race and socioeconomic status (available markers: insurance, employment status, or level of education) as predictors of fatigue, depression, and anxiety sub-scores of the Neuro-QoL (Quality of life in neurological disorders), with particular interest between Caucasians/ whites (CA) and African Americans/blacks (AA). Multivariate linear regression models included as covariates age, sex, disability status, smoking status, body mass index, and disease-modifying therapy. Results-7,430 individuals were included; compared to CA, AA tended to be younger, more female-predominant , and had a higher level of disability. AA had completed slightly less education, had a higher level of Medicaid coverage or uninsured status, and had higher rates of unemployed or disabled status. In the univariate model, markers oflower SES, by whichever definition we used, correlated with worse affective symptoms. In the multivariate model stratified by race, CA showed similar trends. In contrast, in AA, only lower SES by employment status was correlated with worse affective symptoms. In both CA and AA, moderate and severe level of disability correlated with worse affective symptoms.

show abstract

“…Our study findings are in accordance with prior work demonstrating that MS patients with an elevated BMI exhibit more rapid disease progression, including accelerated brain atrophy and worse outcomes in ambulation and global disability. [6][7][8] Several previous studies have also reported associations between BMI and measures of disease severity, including cognitive performance and global disability; however, the cross-sectional design of these studies limits inferences about causality. 3,5 Our study's cross-sectional findings at baseline demonstrated reduced mean GCIPL thickness and marginally reduced pRNFL thickness among the normal weight cohort with respect to the overweight and obese cohorts; however, these findings appear to have been primarily driven by disproportionate tissue injury resulting from prior ON events.…”

Section: Discussionmentioning

confidence: 99%

“…1 In MS, elevated BMI has been associated with disability, cognitive dysfunction, and disease progression. [3][4][5][6][7] However, the role of BMI in MS prognostication remains unclear. Recently, elevated BMI in MS has been shown to be associated with accelerated rates of brain atrophy, thought to primarily reflect neuro-axonal loss, the principal substrate underlying disability in MS. 8 Confidence in these findings would be strengthened by confirmation in an independent cohort.…”

Section: Introductionmentioning

confidence: 99%

Association of body mass index with longitudinal rates of retinal atrophy in multiple sclerosis

et al. 2020

View full text Add to dashboard Cite

Background: Studies evaluating associations between body mass index (BMI) and optical coherence tomography (OCT) measures in multiple sclerosis (MS) are lacking. Objective: To assess whether elevated BMI is associated with accelerated retinal atrophy. Methods: In this observational study, 513 MS patients were followed with serial spectral-domain OCT for a median of 4.4 years. Participants were categorized as normal weight (BMI: 18.5–24.9 kg/m2), overweight (BMI: 25–29.9 kg/m2), and obese (BMI: ⩾30 kg/m2). Participants with diabetes mellitus or uncontrolled hypertension and eyes with optic neuritis (ON) ⩽6 months prior to baseline OCT or during follow-up were excluded. Statistical analyses were performed with mixed-effects linear regression. Results: Obese patients ( n = 146) exhibited accelerated rates of ganglion cell + inner plexiform layer (GCIPL) atrophy relative to normal weight patients ( n = 214; –0.57%/year (95% confidence interval (CI): –0.65% to –0.48%) versus –0.42%/year (95% CI: –0.49% to –0.35%); p = 0.012). GCIPL atrophy rate did not differ between overweight ( n = 153) and normal weight patients (–0.47%/year vs –0.42%/year; p = 0.41). Each 1 kg/m2 higher BMI was associated with accelerated GCIPL (–0.011%/year; 95% CI: –0.019% to –0.004%; p = 0.003) atrophy. Multivariable analyses accounting for age, sex, race, MS subtype, and ON history did not alter the above findings. Conclusions: Elevated BMI, in the absence of overt metabolic comorbidities, may be associated with accelerated GCIPL atrophy. Obesity, a modifiable risk factor, may be associated with accelerated neurodegeneration in MS.

show abstract

Prognostic factors of disability in relapsing remitting multiple sclerosis

Cited by 56 publications

References 46 publications

Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis

Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis

Socioeconomic status and race are correlated with affective symptoms in multiple sclerosis

Association of body mass index with longitudinal rates of retinal atrophy in multiple sclerosis

Contact Info

Product

Resources

About