Person:

Bove, Riley

Objectives: To assess the potential of an online platform, PatientsLikeMe.com (PLM), for research in multiple sclerosis (MS). An investigation of the role of body mass index (BMI) on MS disease course was conducted to illustrate the utility of the platform. Methods: First, we compared the demographic characteristics of subjects from PLM and from a regional MS center. Second, we validated PLM’s patient-reported outcome measure (MS Rating Scale, MSRS) against standard physician-rated tools. Finally, we analyzed the relation of BMI to the MSRS measure. Results: Compared with 4,039 MS Center patients, the 10,255 PLM members were younger, more educated, and less often male and white. Disease course was more often relapsing remitting, with younger symptom onset and shorter disease duration. Differences were significant because of large sample sizes but small in absolute terms. MSRS scores for 121 MS Center patients revealed acceptable agreement between patient-derived and physician-derived composite scores (weighted kappa = 0.46). The Walking domain showed the highest weighted kappa (0.73) and correlation (rs = 0.86) between patient and physician scores. Additionally, there were good correlations between the patient-reported MSRS composite and walking scores and physician-derived measures: Expanded Disability Status Scale (composite rs = 0.61, walking rs = 0.74), Timed 25 Foot Walk (composite rs = 0.70, walking rs = 0.69), and Ambulation Index (composite rs = 0.81, walking rs = 0.84). Finally, using PLM data, we found a modest correlation between BMI and cross-sectional MSRS (rho = 0.17) and no association between BMI and disease course. Conclusions: The PLM population is comparable to a clinic population, and its patient-reported MSRS is correlated with existing clinical instruments. Thus, this online platform may provide a venue for MS investigations with unique strengths (frequent data collection, large sample sizes). To illustrate its applicability, we assessed the role of BMI in MS disease course but did not find a clinically meaningful role for BMI in this setting.

Objective: To optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings. Methods: In a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume). Results: The EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R2 = 0.38±0.05, and that between EHR-derived and true BPF has a mean R2 = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10−12). Conclusion: Incorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.

The objective of this study was to test a new version of the Magnetic Resonance Disease Severity Scale (MRDSS2), incorporating cerebral gray matter (GM) and spinal cord involvement from 3 T MRI, in modeling the relationship between MRI and physical disability or cognitive status in multiple sclerosis (MS). Fifty-five MS patients and 30 normal controls underwent high-resolution 3 T MRI. The patients had an Expanded Disability Status Scale score of 1.6±1.7 (mean±SD). The cerebral normalized GM fraction (GMF), the T2 lesion volume (T2LV), and the ratio of T1 hypointense LV to T2LV (T1/T2) were derived from brain images. Upper cervical spinal cord area (UCCA) was obtained from spinal cord images. A within-subject d-score (difference of MS from normal control) for each MRI component was calculated, equally weighted, and summed to form MRDSS2. With regard to the relationship between physical disability and MRDSS2 or its individual components, MRI–Expanded Disability Status Scale correlations were significant for MRDSS2 (r=0.33, P=0.013) and UCCA (r=−0.33, P=0.015), but not for GMF (P=0.198), T2LV (P=0.707), and T1/T2 (P=0.240). The inclusion of UCCA appeared to drive this MRI–disability relationship in MRDSS2. With regard to cognition, MRDSS2 showed a larger effect size (P=0.035) than its individual components [GMF (P=0.081), T2LV (P=0. 179), T1/T2 (P=0.043), and UCCA (P=0.818)] in comparing cognitively impaired with cognitively preserved patients (defined by the Minimal Assessment of Cognitive Function in MS). Both cerebral lesions (T1/T2) and atrophy (GMF) appeared to drive this relationship. We describe a new version of the MRDSS, which has been expanded to include cerebral GM and spinal cord involvement. MRDSS2 has concurrent validity with clinical status.

Background: The disease course in multiple sclerosis (MS) is influenced by many factors, including age, sex, and sex hormones. Little is known about sex-specific changes in disease course around age 50, which may represent a key biological transition period for reproductive aging. Methods: Male and female subjects with no prior chemotherapy exposure were selected from a prospective MS cohort to form groups representing the years before (38–46 years, N=351) and after (54–62 years, N=200)age 50. Primary analysis assessed for interaction between effects of sex and age on clinical (Expanded Disability Status Scale, EDSS; relapse rate) and radiologic (T2 lesion volume, T2LV; brain parenchymal fraction, BPF) outcomes. Secondarily, we explored patient-reported outcomes (PROs). Results: As expected, there were age- and sex- related changes with male and older cohorts showing worse disease severity (EDSS), brain atrophy (BPF), and more progressive course. There was no interaction between age and sex on cross-sectional adjusted clinical (EDSS, relapse rate) or radiologic (BPF, T2LV) measures, or on 2-year trajectories of decline. There was a significant interaction between age and sex for a physical functioning PRO (SF-36): the older female cohort reported lower physical functioning than men (p=0.002). There were no differences in depression (Center for Epidemiological Study – Depression, CES-D) or fatigue (Modified Fatigue Impact Scale, MFIS) scores. Conclusions: There was no interaction between age and sex suggestive of an effect of reproductive aging on clinical or radiologic progression. Prospective analyses across the menopausal transition are needed.

Objective: To examine the relationship between 2 markers of early multiple sclerosis (MS) onset, 1 genetic (HLA-DRB11501) and 1 experiential (early menarche), in 2 cohorts. Methods: We included 540 white women with MS or clinically isolated syndrome (N = 156 with genetic data available) and 1,390 white women without MS but with a first-degree relative with MS (Genes and Environment in Multiple Sclerosis [GEMS]). Age at menarche, HLA-DRB11501 status, and age at MS onset were analyzed. Results: In both cohorts, participants with at least 1 HLA-DRB11501 allele had a later age at menarche than did participants with no risk alleles (MS: mean difference = 0.49, 95% confidence interval [CI] = [0.03–0.95], p = 0.036; GEMS: mean difference = 0.159, 95% CI = [0.012–0.305], p = 0.034). This association remained after we adjusted for body mass index at age 18 (available in GEMS) and for other MS risk alleles, as well as a single nucleotide polymorphism near the HLA-A region previously associated with age of menarche (available in MS cohort). Confirming previously reported associations, in our MS cohort, every year decrease in age at menarche was associated with a 0.65-year earlier MS onset (95% CI = [0.07–1.22], p = 0.027, N = 540). Earlier MS onset was also found in individuals with at least 1 HLA-DRB11501 risk allele (mean difference = −3.40 years, 95% CI = [−6.42 to −0.37], p = 0.028, N = 156). Conclusions: In 2 cohorts, a genetic marker for earlier MS onset (HLA-DRB1*1501) was inversely related to earlier menarche, an experiential marker for earlier symptom onset. This finding warrants broader investigations into the association between the HLA region and hormonal regulation in determining the onset of autoimmune disease.

Objective: In this cohort of individuals with and without multiple sclerosis (MS), we illustrate some of the novel approaches that smartphones provide to monitor patients with chronic neurologic disorders in their natural setting. Methods: Thirty-eight participant pairs (MS and cohabitant) aged 18–55 years participated in the study. Each participant received an Android HTC Sensation 4G smartphone containing a custom application suite of 19 tests capturing participant performance and patient-reported outcomes (PROs). Over 1 year, participants were prompted daily to complete one assigned test. Results: A total of 22 patients with MS and 17 cohabitants completed the entire study. Among patients with MS, low scores on PROs relating to mental and visual function were associated with dropout (p < 0.05). We illustrate several novel features of a smartphone platform. First, fluctuations in MS outcomes (e.g., fatigue) were assessed against an individual's ambient environment by linking responses to meteorological data. Second, both response accuracy and speed for the Ishihara color vision test were captured, highlighting the benefits of both active and passive data collection. Third, a new trait, a person-specific learning curve in neuropsychological testing, was identified using spline analysis. Finally, averaging repeated measures over the study yielded the most robust correlation matrix of the different outcome measures. Conclusions: We report the feasibility of, and barriers to, deploying a smartphone platform to gather useful passive and active performance data at high frequency in an unstructured manner in the field. A smartphone platform may therefore enable large-scale naturalistic studies of patients with MS or other neurologic diseases.