Wednesday, 27 November 2013

Big Brother: modeling MS using electronic health records

We are watching every step you take; using Big Brother to study the impact of MS. #MSBlog #MSResearch

"Big Brother, are you ready for him? The use of data collected for other reasons can be very useful for imputing the impact of MS on quality of life and physical functioning. This study shows how by trawling electronic health records (EHR) you can assess the severity of MS and it correlated reasonably well with measured outcome, i.e. the EDSS and  its derivative the MS severity score (MSSS) and the MRI metric of brain volume. EHR are just the beginning. What about your bank or credit card statements? It can tell us how much your are earning and spending; employment is a good indicator of health. We may be able to see if, when and how much you are spending on MS-related healthcare services. We can also see if your spending pattern changes over time; less spending on leisure activities, for example movies, restaurants, golf, holidays, etc. could indicate reduced quality of life and an advance in your MS. What about your smart phone? How you use it and where it goes by  tracking it will tell us something about your MS. The less mobile you are the more disabled."

"Are you ready for the Orwellian world of Big Brother? Could I convince you that this type of data will help health economists assess the real impact of MS on peoples lives and it can then be used to assess whether or not DMTs or MS services are making an impact on MS at a population level."


"To explore this issue further I would appreciate it if you could complete this short survey. Thank you."


Xia et al. Modeling disease severity in multiple sclerosis using electronic health records. PLoS One. 2013 Nov 11;8(11):e78927. doi: 10.1371/journal.pone.0078927.

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(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)). 

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.

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