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Published on: December 9, 2015
Clinical and demographic factors affecting disease severity in patients with multiple sclerosis
Somayeh Baghizadeh1, Mohammad Ali Sahraian, Nahid Beladimoghadam
1Neurologist, Bouali Hospital, Qazvin University of Medical Sciences, Qazvin, Iran.
Background:
The clinical course of multiple sclerosis (MS) evolves over many years. Its prognosis is highly variable among affected individuals, i.e. while some suffer from early severe disabilities, others remain ambulatory and functional for many years. We used Multiple Sclerosis Severity Score (MSSS) and the new classification for MS severity Herbert et al. introduced in 2006 according to MSSS, to investigate some clinical and demographic factors as potential indicators of disease severity in in MS.
Methods:
During a six-month period, patients with definite MS according to the revised McDonald's criteria who referred to three neurology and MS clinics in Tehran (Iran) were included in the study. All patients were interviewed and examined by a neurology resident who had been trained for employing the Expanded Disability Status Scale (EDSS). For each patient, MSSS was determined by using EDSS and disease duration.
Results:
Overall, 338 (266 female and 72 male) patients were enrolled. Among demographic features, gender, younger age at onset, positive family history, and parental consanguinity were not associated with disease severity. Education was weakly associated with disease severity. Among clinical factors, presenting symptoms such as poly-symptomatic attacks, walking difficulty, and upper and lower extremity dysfunction were associated with more disability while presentation with optic neuritis had better prognosis. Complete recovery after the first attack, longer interval between the first and second attacks, lower number of symptoms at presentation, shorter duration of attacks, and relapsing-remitting course were associated with less disability and better prognosis. These results were noticed in ordinal logistic regression. However when multiple logistic regression was performed, the strongest determinant of disease severity was disease course with odds ratio (OR) = 49.12 for secondary progressive course and OR = 53.25 for primary progressive (± relapse) course. Walking difficulty as the presenting symptom had a borderline association with disease severity (OR = 2.31; P = 0.055). Increased number of onset symptoms was associated (but not significantly) with more severe disease.
Conclusion:
Early prediction of disease severity by demographic and clinical features is currently impossible. We need to determine stronger predictors, possibly a combination of demographic, clinical, biomarkers, and imaging findings.
Insights
Predicting multiple sclerosis (MS) severity early is challenging. While some clinical factors indicate prognosis, the disease course is the strongest predictor of disability in MS patients.
Area of Science:
- Neurology
- Clinical Research
Background:
- Multiple sclerosis (MS) exhibits highly variable clinical courses and prognoses.
- Predicting long-term disability is crucial for patient management.
Purpose of the Study:
- To investigate clinical and demographic factors as indicators of multiple sclerosis severity.
- Utilize the Multiple Sclerosis Severity Score (MSSS) and a 2006 MS severity classification.
Main Methods:
- Patients with definite MS were assessed over six months in Tehran, Iran.
- Expanded Disability Status Scale (EDSS) and disease duration were used to calculate MSSS.
- Demographic and clinical features were analyzed for association with disease severity.
Main Results:
- Demographic factors like gender and age at onset were not significantly associated with MS severity.
- Presenting symptoms such as walking difficulty and poly-symptomatic attacks correlated with increased disability.
- The strongest determinant of MS severity was the disease course, particularly progressive forms.
Conclusions:
- Early prediction of MS severity using current demographic and clinical features remains unreliable.
- Future research should explore combined predictors including biomarkers and imaging findings.
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