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(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)).


CONCLUSIONS
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.
Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America ; Harvard Medical School, Boston, Massachusetts, United States of America ; Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America.
Zongqi
Z
Xia
Elizabeth
E
Secor
Lori B
LB
Chibnik
Riley M
RM
Bove
Suchun
S
Cheng
Tanuja
T
Chitnis
Andrew
A
Cagan
Vivian S
VS
Gainer
Pei J
PJ
Chen
Katherine P
KP
Liao
Stanley Y
SY
Shaw
Ashwin N
AN
Ananthakrishnan
Peter
P
Szolovits
Howard L
HL
Weiner
Elizabeth W
EW
Karlson
Shawn N
SN
Murphy
Guergana K
GK
Savova
Tianxi
T
Cai
Susanne E
SE
Churchill
Robert M
RM
Plenge
Isaac S
IS
Kohane
Philip L
PL
De Jager
D000465:Algorithms; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D009103:Multiple Sclerosis / Q000473:pathology* / Q000503:physiopathology*; D012720:Severity of Illness Index