Modeling Disease Severity in Multiple Sclerosis Using Electronic Health Records

Background/objective: In 2019, the 2010 U.S. multiple sclerosis (MS) prevalence was robustly estimated (265.1–309.2/100,000) based on large administrative health-claims datasets. Using 56.6 million electronic health records (EHRs), we sought to generate complementary age, sex, and race standardized estimates. Methods/results: Using de-identified EHRs and 2018 U.S. Census data, we estimated an age- and sex-standardized MS prevalence of 219.5/100,000 which increased to 274.5/100,000 when accounting for White and Black race alone. Women aged 50 to 69 years had the highest prevalence (>600/100,000). Among White and Black Americans, the age- and sex-standardized prevalence was 283.7 and 226.1 per 100,000, respectively. Conclusion: Using 56.6 million EHRs and standardizing for age, sex, and race (White and Black Americans only), we estimated at least 810,504 Americans were living with MS in 2018.

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Prevalence of multiple sclerosis within a healthcare delivery system in Northern California: a retrospective, electronic, health records-based study from 2010 to 2016

10.26226/morressier.5b75785c5aff7400151f3b3b ◽

2018 ◽

Author(s):

Robert Romanelli

Keyword(s):

Multiple Sclerosis ◽

Electronic Health Records ◽

Delivery System ◽

Healthcare Delivery ◽

Northern California ◽

Healthcare Delivery System ◽

Health Records ◽

Electronic Health

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Validation of an algorithm using inpatient electronic health records to determine the presence and severity of cirrhosis in patients with hepatocellular carcinoma in England: an observational study

BMJ Open ◽

10.1136/bmjopen-2018-028571 ◽

2019 ◽

Vol 9 (7) ◽

pp. e028571 ◽

Cited By ~ 1

Author(s):

Robert J Driver ◽

Vinay Balachandrakumar ◽

Anya Burton ◽

Jessica Shearer ◽

Amy Downing ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Disease ◽

Observational Study ◽

Electronic Health Records ◽

Disease Severity ◽

Health Records ◽

Derivation Cohort ◽

Electronic Health ◽

Clinical Records ◽

New Diagnosis

ObjectivesOutcomes in hepatocellular carcinoma (HCC) are determined by both cancer characteristics and liver disease severity. This study aims to validate the use of inpatient electronic health records to determine liver disease severity from treatment and procedure codes.DesignRetrospective observational study.SettingTwo National Health Service (NHS) cancer centres in England.Participants339 patients with a new diagnosis of HCC between 2007 and 2016.Main outcomeUsing inpatient electronic health records, we have developed an optimised algorithm to identify cirrhosis and determine liver disease severity in a population with HCC. The diagnostic accuracy of the algorithm was optimised using clinical records from one NHS Trust and it was externally validated using anonymised data from another centre.ResultsThe optimised algorithm has a positive predictive value (PPV) of 99% for identifying cirrhosis in the derivation cohort, with a sensitivity of 86% (95% CI 82% to 90%) and a specificity of 98% (95% CI 96% to 100%). The sensitivity for detecting advanced stage cirrhosis is 80% (95% CI 75% to 87%) and specificity is 98% (95% CI 96% to 100%), with a PPV of 89%.ConclusionsOur optimised algorithm, based on inpatient electronic health records, reliably identifies and stages cirrhosis in patients with HCC. This highlights the potential of routine health data in population studies to stratify patients with HCC according to liver disease severity.

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