Clustering Multiple Sclerosis Medication Sequence Data with Mixture Markov Chain Analysis with covariates using Multiple Simplex Constrained Optimization Routine (MSiCOR)

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system that causes neurodegeneration. While disease-modifying therapies (DMTs) reduce inflammatory disease activity and delay worsening disability in MS, there are significantly varying treatment responses across people with MS (pwMS). pwMS often receive serial monotherapies of DMTs. Here, we propose a novel method to cluster pwMS according to the sequence of DMT prescriptions and associated clinical features (covariates). This is achieved via a mixture Markov chain analysis with covariates, where the sequence of prescribed DMTs for each patient is modeled as a Markov chain. Given the computational challenges to maximize the mixture likelihood on the constrained parameter space, we develop a pattern search-based global optimization technique which can optimize any objective function on a collection of simplexes and shown to outperform other related global optimization techniques. In simulation experiments, the proposed method is shown to outperform the Expectation-Maximization (EM) algorithm based method for clustering sequence data without covariates. Based on the analysis, we divided MS patients into 3 clusters: inferon-beta dominated, multi-DMTs, and natalizumab dominated. Further cluster-specific summaries of relevant covariates indicate patient differences among the clusters. This method may guide the DMT prescription sequence based on clinical features

Design of school bell automatic control system based on single-chip microcomputer

This article introduces the basic components of the school's automatic control system, and makes a detailed introduction and comparison of the functions, application scenarios, and advantages of each part. The hardware design of the automatic control system is based on the STC89C52 single-chip control circuit as the core, supplemented by sensor circuits, clock circuits, bell circuits and human-computer interaction circuits to complete various functions. The human-computer interaction circuits include keyboard input circuits and liquid crystal display circuits. The software design of this system mainly includes sensor detection, button setting, and bell output part. The sensor detection part is composed of a temperature detection subprogram, the key setting part is composed of an independent key subprogram and a liquid crystal display subprogram, and the bell output part is composed of a voice recording and playback subprogram. The program and clock subroutine constitute

From mandatory icebreaker guiding to a permission regime: changes to the new Russian legislation of the Northern Sea Route

This article focuses on two issues. The first concerns definitions of the Northern Sea Route (NSR) in old and new Russian legislation, and the second relates to Russian rules on icebreaker guiding. Based on a comprehensive comparative analysis of relevant Russian legal provisions enacted in 2013 and previous laws in this area, we offer the following conclusions. (1) Our legal analysis indicates that Russia’s view of the NSR as a historical national transportation route has not changed. However, the new law redefines the scope and coverage of the NSR, which now comprises the internal waters, territorial sea, adjacent zone, and the exclusive economic zone of the Russian Federation. In fact, the new law resolves previous ambiguity regarding extension of the NSR boundary to the high seas. (2) Based on an analysis of the new rules on icebreaker guiding, the article concludes that NSR is transitioning from a mandatory icebreaker guiding regime into a permit regime. This is particularly evident in its provision of a concrete, practical, and predictable clause on permissible or impermissible conditions relating to independent navigation. According to the new rules, it is possible for foreign ships to undertake independent navigation in the NSR. The Russian NSR policy, therefore, appears to have changed significantly, and has future potential for opening the NSR up to the international community

Complex relation of HLA-DRB1*1501, age at menarche, and age at multiple sclerosis onset

Objective: To examine the relationship between 2 markers of early multiple sclerosis (MS) onset, 1 genetic (HLA-DRB1*1501) 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-DRB1*1501 status, and age at MS onset were analyzed. Results: In both cohorts, participants with at least 1 HLA-DRB1*1501 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-DRB1*1501 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

Improving Case Definition of Crohnʼs Disease and Ulcerative Colitis in Electronic Medical Records Using Natural Language Processing

available in PMC 2014 June 01Background: Previous studies identifying patients with inflammatory bowel disease using administrative codes have yielded inconsistent results. Our objective was to develop a robust electronic medical record–based model for classification of inflammatory bowel disease leveraging the combination of codified data and information from clinical text notes using natural language processing. Methods: Using the electronic medical records of 2 large academic centers, we created data marts for Crohn’s disease (CD) and ulcerative colitis (UC) comprising patients with ≥1 International Classification of Diseases, 9th edition, code for each disease. We used codified (i.e., International Classification of Diseases, 9th edition codes, electronic prescriptions) and narrative data from clinical notes to develop our classification model. Model development and validation was performed in a training set of 600 randomly selected patients for each disease with medical record review as the gold standard. Logistic regression with the adaptive LASSO penalty was used to select informative variables. Results: We confirmed 399 CD cases (67%) in the CD training set and 378 UC cases (63%) in the UC training set. For both, a combined model including narrative and codified data had better accuracy (area under the curve for CD 0.95; UC 0.94) than models using only disease International Classification of Diseases, 9th edition codes (area under the curve 0.89 for CD; 0.86 for UC). Addition of natural language processing narrative terms to our final model resulted in classification of 6% to 12% more subjects with the same accuracy. Conclusions: Inclusion of narrative concepts identified using natural language processing improves the accuracy of electronic medical records case definition for CD and UC while simultaneously identifying more subjects compared with models using codified data alone.National Institutes of Health (U.S.) (NIH U54-LM008748)American Gastroenterological AssociationNational Institutes of Health (U.S.) (NIH K08 AR060257)Beth Isreal Deaconess Medical Center (Katherine Swan Ginsburg Fund)National Institutes of Health (U.S.) (NIH R01-AR056768)Burroughs Wellcome Fund (Career Award for Medical Scientists)National Institutes of Health (U.S.) (NIH U01-GM092691)National Institutes of Health (U.S.) (NIH R01-AR059648

Normalization of Plasma 25-Hydroxy Vitamin D Is Associated with Reduced Risk of Surgery in Crohn’s Disease

available in PMC 2014 August 01AB Background: Vitamin D may have an immunologic role in Crohn's disease (CD) and ulcerative colitis (UC). Retrospective studies suggested a weak association between vitamin D status and disease activity but have significant limitations. Methods: Using a multi-institution inflammatory bowel disease cohort, we identified all patients with CD and UC who had at least one measured plasma 25-hydroxy vitamin D (25(OH)D). Plasma 25(OH)D was considered sufficient at levels >=30 ng/mL. Logistic regression models adjusting for potential confounders were used to identify impact of measured plasma 25(OH)D on subsequent risk of inflammatory bowel disease-related surgery or hospitalization. In a subset of patients where multiple measures of 25(OH)D were available, we examined impact of normalization of vitamin D status on study outcomes. Results: Our study included 3217 patients (55% CD; mean age, 49 yr). The median lowest plasma 25(OH)D was 26 ng/mL (interquartile range, 17-35 ng/mL). In CD, on multivariable analysis, plasma 25(OH)D =30 ng/mL. Similar estimates were also seen for UC. Furthermore, patients with CD who had initial levels <30 ng/mL but subsequently normalized their 25(OH)D had a reduced likelihood of surgery (odds ratio, 0.56; 95% confidence interval, 0.32-0.98) compared with those who remained deficient. Conclusion: Low plasma 25(OH)D is associated with increased risk of surgery and hospitalizations in both CD and UC, and normalization of 25(OH)D status is associated with a reduction in the risk of CD-related surgery. (C) Crohn's & Colitis Foundation of America, Inc

Similar Risk of Depression and Anxiety Following Surgery or Hospitalization for Crohn's Disease and Ulcerative Colitis

OBJECTIVES: Psychiatric comorbidity is common in Crohn's disease (CD) and ulcerative colitis (UC). Inflammatory bowel disease (IBD)-related surgery or hospitalizations represent major events in the natural history of the disease. The objective of this study is to examine whether there is a difference in the risk of psychiatric comorbidity following surgery in CD and UC. METHODS: We used a multi-institution cohort of IBD patients without a diagnosis code for anxiety or depression preceding their IBD-related surgery or hospitalization. Demographic-, disease-, and treatment-related variables were retrieved. Multivariate logistic regression analysis was performed to individually identify risk factors for depression and anxiety. RESULTS: Our study included a total of 707 CD and 530 UC patients who underwent bowel resection surgery and did not have depression before surgery. The risk of depression 5 years after surgery was 16% and 11% in CD and UC patients, respectively. We found no difference in the risk of depression following surgery in the CD and UC patients (adjusted odds ratio, 1.11; 95% confidence interval, 0.84–1.47). Female gender, comorbidity, immunosuppressant use, perianal disease, stoma surgery, and early surgery within 3 years of care predicted depression after CD surgery; only the female gender and comorbidity predicted depression in UC patients. Only 12% of the CD cohort had ≥4 risk factors for depression, but among them nearly 44% subsequently received a diagnosis code for depression. CONCLUSIONS: IBD-related surgery or hospitalization is associated with a significant risk for depression and anxiety, with a similar magnitude of risk in both diseases.National Institutes of Health (U.S.) (U54-LM008748

Methods to Develop an Electronic Medical Record Phenotype Algorithm to Compare the Risk of Coronary Artery Disease across 3 Chronic Disease Cohorts

Background Typically, algorithms to classify phenotypes using electronic medical record (EMR) data were developed to perform well in a specific patient population. There is increasing interest in analyses which can allow study of a specific outcome across different diseases. Such a study in the EMR would require an algorithm that can be applied across different patient populations. Our objectives were: (1) to develop an algorithm that would enable the study of coronary artery disease (CAD) across diverse patient populations; (2) to study the impact of adding narrative data extracted using natural language processing (NLP) in the algorithm. Additionally, we demonstrate how to implement CAD algorithm to compare risk across 3 chronic diseases in a preliminary study. Methods and Results We studied 3 established EMR based patient cohorts: diabetes mellitus (DM, n = 65,099), inflammatory bowel disease (IBD, n = 10,974), and rheumatoid arthritis (RA, n = 4,453) from two large academic centers. We developed a CAD algorithm using NLP in addition to structured data (e.g. ICD9 codes) in the RA cohort and validated it in the DM and IBD cohorts. The CAD algorithm using NLP in addition to structured data achieved specificity >95% with a positive predictive value (PPV) 90% in the training (RA) and validation sets (IBD and DM). The addition of NLP data improved the sensitivity for all cohorts, classifying an additional 17% of CAD subjects in IBD and 10% in DM while maintaining PPV of 90%. The algorithm classified 16,488 DM (26.1%), 457 IBD (4.2%), and 245 RA (5.0%) with CAD. In a cross-sectional analysis, CAD risk was 63% lower in RA and 68% lower in IBD compared to DM (p<0.0001) after adjusting for traditional cardiovascular risk factors. Conclusions We developed and validated a CAD algorithm that performed well across diverse patient populations. The addition of NLP into the CAD algorithm improved the sensitivity of the algorithm, particularly in cohorts where the prevalence of CAD was low. Preliminary data suggest that CAD risk was significantly lower in RA and IBD compared to DM.National Institutes of Health (U.S.). Informatics for Integrating Biology and the Bedside Project (U54LM008748

Modeling Disease Severity in Multiple Sclerosis Using Electronic Health Records

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 R[superscript 2] = 0.38±0.05, and that between EHR-derived and true BPF has a mean R[superscript 2] = 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[superscript −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.National Institute of General Medical Sciences (U.S.) (NIH U54-LM008748

Common variation near IRF6 is associated with IFN-β-induced liver injury in multiple sclerosis

Multiple sclerosis (MS) is a disease of the central nervous system treated with disease-modifying therapies, including the biologic, interferon-β (IFN-β). Up to 60% of IFN-β-exposed MS patients develop abnormal biochemical liver test results1,2, and 1 in 50 experiences drug-induced liver injury3. Since genomic variation contributes to other forms of drug-induced liver injury4,5, we aimed to identify biomarkers of IFN-β-induced liver injury using a two-stage genome-wide association study. The rs2205986 variant, previously linked to differential expression of IRF6, surpassed genome-wide significance in the combined two-stage analysis (P = 2.3 × 10-8, odds ratio = 8.3, 95% confidence interval = 3.6-19.2). Analysis of an independent cohort of IFN-β-treated MS patients identified via electronic medical records showed that rs2205986 was also associated with increased peak levels of aspartate aminotransferase (P = 7.6 × 10-5) and alkaline phosphatase (P = 4.9 × 10-4). We show that these findings may be applicable to predicting IFN-β-induced liver injury, offering insight into its safer use