Analysing the relationship between polysomnographic measures of sleep with measures of physical and cognitive fatigue in people with multiple sclerosis.

OBJECTIVES: We aimed to determine whether there was a relationship between objective measures of sleep measured by polysomnography and measures of physical and cognitive fatigue in patients with Multiple Sclerosis (MS). METHODS: People with MS of age 18-50 years of any subtype attending the OPD satisfying the revised 2010 McDonald criteria were recruited. Modified Fatigue Impact Scale (MFIS) and the Fatigue Severity Scale (FSS) were used to assess physical fatigue. Cognitive fatigue was measured with modified versions of the Stroop test, modified Symbol Digit Modalities Test, Serial Addition Test, and with latency and amplitude of the P300 evoked potential. Percentage of N1, N2, N3 and REM sleep stages, Sleep onset latency, Sleep efficiency, Wake after sleep onset, Respiratory event index, Periodic limb movement index were the measures recorded with polysomnography. RESULTS: Among 113 patients, 43 (38.05%) complained of disturbed sleep and 88 (77.88%) complained of increased fatigability and tiredness. Mean MFIS score of the sample was 42.34 ±â€¯9.09. Mean FSS score was 19.12 ±â€¯9.42. Polysomnographic measures of sleep showed a significant correlation with objective measures of cognitive fatigue and did not show any significant correlation with measures of physical fatigue. CONCLUSIONS: Sleep impairment is a very common problem in people with MS justifying routine polysomnographic evaluation. We have found evidence that though sleep impairment is not related to physical fatigue, it is strongly related to cognitive fatigue.

Predicting falls in multiple sclerosis: Do electrophysiological measures have a better predictive accuracy compared to clinical measures?

BACKGROUND: The risk of falls in people with Multiple Sclerosis (MS) is much greater than that of the general population due to impaired coordination, gait, sensation, muscle tone, strength, and cognition. These MS related falls hamper the day to day living of these individuals and are one of the prime factors aggravating the disease related morbidity. The fear of falling itself may make these individuals more dependent and hinder their professional and leisurely activities. Hence, the significance of identifying individuals who are at risk of falling and instituting preventive counter-measures cannot be overemphasized. Various simple clinical tests and questionnaires have been recommended for this purpose, but are far from ideal. OBJECTIVE: The objective was to find accurate measures to predict a future fall in MS patients. We also aimed to enquire about the prevalence of falls in MS population and its clinical profile which included detailed history about the past falls, Expanded disability status scale (EDSS) scores, Timed 25 foot walk (T25FW) scores, Activities specific balance confidence (ABC) scores, Falls efficacy scale international (FESI) scores, Multiple Sclerosis Walking Scale 12 (MSWS12) questionnaire. DESIGN/METHODS: This was a prospective cohort study conducted at the Institute of Neurology, Chennai from January 2015 to August 2017. MS patients of any subtype attending Neurology OPD satisfying revised 2010 McDonald criteria were recruited. 134 subjects with MS consented to participate in this study and 113 of them who met the criteria were included. Baseline history was obtained about the number of falls in the previous year. EDSS, T25FW, ABC, FESI, and MSWS12 scores were obtained at the baseline. VEMP and SEP tests were done and the baseline P13/N23 cVEMP latencies, N10 oVEMP latency, and P37 lower limb SEP latency were obtained. These subjects were followed up for one year and were enquired if they had fallen during that period and the number of falls was recorded. Logistic regression models were used to compute the area under receiver operating characteristic curve (AUC) for each variable tested. Pearson correlation coefficients were calculated for each variable with the number of future falls. RESULTS: Among the 113 patients, 72% (n = 81) had one or more falls during follow-up. Among all variables tested P13 cervical VEMP latency had the highest predictive accuracy (AUC = 0.820) followed by N10 ocular VEMP latency (AUC = 0.794) and P37 SEP latency (AUC = 0.732). P13 latency also had the highest correlation coefficient (R = 0.689, R2 = 0.475) with the number of future falls. CONCLUSION: P13, N10 and P37 latencies were the most accurate in predicting a future fall when compared to clinical measures.

A study of cognitive fatigue in Multiple Sclerosis with novel clinical and electrophysiological parameters utilizing the event related potential P300.

BACKGROUND: Although cognitive fatigue plays a significant part in Multiple Sclerosis (MS) related impairment, knowledge regarding it is largely lacking. Until now, not many tools are available to a clinician to detect cognitive fatigue. The subjective tools of fatigue have never been reliable.tabl OBJECTIVES: To assess the prevalence and clinical/ demographic profile of cognitive fatigue in MS using novel clinical and electrophysiological measures and to find their accuracy. We also aimed to test the three leading hypotheses - the temporal fatigue, cognitive load and cognitive domain hypotheses of cognitive fatigue in MS. METHODS: 50 consecutive MS patients attending the Neurology OPD in Madras Medical College, Chennai from May 2015 to February 2016 satisfying the 2010 revised McDonald criteria for MS with an equal number of matched controls were recruited. Modified versions (a shorter version, and longer and more demanding versions) of the Stroop test, symbol digit modalities test, and serial addition tests were used in addition to modified tests of P300 latency and amplitude each specifically tailored to reveal cognitive fatigue. RESULTS: Out of the seven measures of cognitive fatigue used, 46% (n=23) of MS patients had impairment in two or more of the scores compared to that of 8% (n=4) in the healthy control group. The Expanded disability status scale (EDSS) scores were significantly higher for MS patients with cognitive fatigue compared to those without. All the clinical and electrophysiological measures used in this study had a relatively high sensitivity and specificity. In addition, all the clinical measures correlated with the electrophysiological measures of cognitive fatigue in this study. Our data also supported all three hypotheses implying that cognitive fatigue in MS may be a multifaceted entity. CONCLUSION: Cognitive fatigue is widely prevalent in MS and can be detected with specific tools. The tools developed and described in this study may be used as an effective means of detecting cognitive fatigue in MS patients and thus allowing patients to realise their limitations. Institution of appropriate remedial measures like advising such patients to break up a more cognitively demanding task into smaller subtasks may help to improve their quality of life.