Trial reportPloS one2013
Modeling disease severity in multiple sclerosis using electronic health records.
Trial report in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
47 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2022Pooled it
- Natural language processing in clinical neuroscience and psychiatry: A review.Frontiers in psychiatry · 2022Pooled it
- Extracting information from the text of electronic medical records to improve case detection: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2016Pooled it
- Knowledge graph-guided multiple sclerosis identification and therapeutic trend analysis: Real-world evidence from two large healthcare systems.PLOS digital health · 2026Article
- Semisupervised transfer learning for evaluation of model classification performance.Biometrics · 2024Article
- Direct Clinical Applications of Natural Language Processing in Common Neurological Disorders: Scoping Review.JMIR neurotechnology · 2024Review
- Clustering Sequence Data with Mixture Markov Chains with Covariates Using Multiple Simplex Constrained Optimization Routine (MSiCOR).Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- Scalable Feature Engineering from Electronic Free Text Notes to Supplement Confounding Adjustment of Claims-Based Pharmacoepidemiologic Studies.Clinical pharmacology and therapeutics · 2023Article
- A semi-supervised adaptive Markov Gaussian embedding process (SAMGEP) for prediction of phenotype event times using the electronic health record.Scientific reports · 2022Article
- Efficient Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Sampling.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2022Article
- Assessment of Natural Language Processing Methods for Ascertaining the Expanded Disability Status Scale Score From the Electronic Health Records of Patients With Multiple Sclerosis: Algorithm Development and Validation Study.JMIR medical informatics · 2022Article
- It's time to change our documentation philosophy: writing better neurology notes without the burnout.Frontiers in digital health · 2022Article
- Temporal trends of multiple sclerosis disease activity: Electronic health records indicators.Multiple sclerosis and related disorders · 2022Article
- Comparison of Dimethyl Fumarate vs Fingolimod and Rituximab vs Natalizumab for Treatment of Multiple Sclerosis.JAMA network open · 2021Article
- Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules.Journal of gastroenterology and hepatology · 2021Article
- Leveraging electronic health records data to predict multiple sclerosis disease activity.Annals of clinical and translational neurology · 2021Article
- High-throughput phenotyping with temporal sequences.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Phenome-wide examination of comorbidity burden and multiple sclerosis disease severity.Neurology(R) neuroimmunology & neuroinflammation · 2020Article
- sureLDA: A multidisease automated phenotyping method for the electronic health record.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP).Nature protocols · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors.
Funding
Abstract
objectiveTo 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.
methodsIn 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).
resultsThe 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(2) = 0.38±0.05, and that between EHR-derived and true BPF has a mean R(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(-12)).
conclusionIncorporation 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.