Accurate classification of secondary progression in multiple sclerosis using a decision tree

BACKGROUND: The absence of reliable imaging or biological markers of phenotype transition in multiple sclerosis (MS) makes assignment of current phenotype status difficult. OBJECTIVE: The authors sought to determine whether clinical information can be used to accurately assign current disease phenotypes. METHODS: Data from the clinical visits of 14,387 MS patients in Sweden were collected. Classifying algorithms based on several demographic and clinical factors were examined. Results obtained from the best classifier when predicting neurologist recorded disease classification were replicated in an independent cohort from British Columbia and were compared to a previously published algorithm and clinical judgment of three neurologists. RESULTS: A decision tree (the classifier) containing only most recently available expanded disability scale status score and age obtained 89.3% (95% confidence intervals (CIs): 88.8-89.8) classification accuracy, defined as concordance with the latest reported status. Validation in the independent cohort resulted in 82.0% (95% CI: 81.0-83.1) accuracy. A previously published classification algorithm with slight modifications achieved 77.8% (95% CI: 77.1-78.4) accuracy. With complete patient history of 100 patients, three neurologists obtained 84.3% accuracy compared with 85% for the classifier using the same data. CONCLUSION: The classifier can be used to standardize definitions of disease phenotype across different cohorts. Clinically, this model could assist neurologists by providing additional information

Cognitive function predicts work disability among multiple sclerosis patients

Background: In multiple sclerosis various aspects of cognitive function can be detrimentally affected. More than that, patients´ employment and social functioning is likely to be impacted. Objective: To determine whether work disability among multiple sclerosis patients could be predicted by the symbol digit modalities test. Methods: A register-based cohort study was conducted. Individual data on work disability, operationalised as annual net days of sickness absence and/or disability pension were retrieved at baseline, when the symbol digit modalities test was performed, after one-year and 3-year follow-up for 903 multiple sclerosis patients. The incidence rate ratios for work disability were calculated with general estimating equations using a negative binomial distribution and were adjusted for gender, age, educational level, family composition, type of living area and physical disability. Results: After one year of follow-up, the patients in the lowest symbol digit modalities test quartile were estimated to have a 73% higher rate of work disability when compared to the patients in the highest symbol digit modalities test quartile (incidence rate ratio 1.73, 95% confidence interval 1.42‒2.10). This estimate after 3-year follow-up was similar (incidence rate ratio 1.68, 95% confidence interval 1.40‒2.02). Conclusion: Cognitive function is to a high extent associated with multiple sclerosis patients' future work disability, even after adjusting for other factors

Income in Multiple Sclerosis Patients with Different Disease Phenotypes

<div><p>Background</p><p>Multiple sclerosis (MS) is a disease with profound heterogeneity in clinical course.</p><p>Objective</p><p>To analyze sources and levels of income among MS patients in relation to disease phenotype with a special focus on identifying differences/similarities between primary progressive MS (PPMS) and secondary progressive MS (SPMS).</p><p>Methods</p><p>A total of 6890 MS patients aged 21−64 years and living in Sweden in 2010 were identified for this cross-sectional study. Descriptive statistics, logistic, truncated linear, and zero-inflated negative binomial regression models were used to estimate differences in income between SPMS, PPMS and relapsing-remitting MS (RRMS) patients.</p><p>Results</p><p>RRMS patients earned almost twice as much as PPMS and SPMS patients (on average SEK 204,500, SEK 114,500, and SEK 79,800 in 2010, respectively). The difference in earnings between PPMS and SPMS was not statistically significant when analyzed with multivariable regression. The estimated odds ratio for PPMS patients to have income from earnings was not significantly different from SPMS patients (95% CI 0.98 to 1.59). PPMS and RRMS patients were less likely to receive benefits when compared to SPMS patients (by 6% and 27% lower, respectively).</p><p>Conclusion</p><p>Our findings argue for similarities between PPMS and SPMS and highlight the socioeconomic importance of preventing RRMS patients convert to SPMS.</p></div

The average of annual earnings of the patients by MS phenotype and age.

<p>The average of annual earnings of the patients by MS phenotype and age.</p

Analysis approach.

<p>Analysis approach.</p

Descriptive data of the study population of MS patients by phenotype.

<p>Descriptive data of the study population of MS patients by phenotype.</p

Differences in income in 2010 among MS patients by phenotype.

<p>Differences in income in 2010 among MS patients by phenotype.</p

Average annual income of the patients by MS phenotype.

<p>Average annual income of the patients by MS phenotype.</p