Using automated electronic medical record data extraction to model ALS survival and progression

Alex G Karanevich,Omar Jawdat,Luke J Weisbrod,Byron J Gajewski,Jianghua He,Jeffrey M Statland,Richard J Barohn

doi:10.1186/s12883-018-1208-z

Alex G Karanevich, Omar Jawdat + Show 5 more

Open Access

https://doi.org/10.1186/s12883-018-1208-z

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Abstract

BackgroundTo assess the feasibility of using automated capture of Electronic Medical Record (EMR) data to build predictive models for amyotrophic lateral sclerosis (ALS) outcomes.MethodsWe used an Informatics for Integrating Biology and the Bedside search discovery tool to identify and extract data from 354 ALS patients from the University of Kansas Medical Center EMR. The completeness and integrity of the data extraction were verified by manual chart review. A linear mixed model was used to model disease progression. Cox proportional hazards models were used to investigate the effects of BMI, gender, and age on survival.ResultsData extracted from the EMR was sufficient to create simple models of disease progression and survival. Several key variables of interest were unavailable without including a manual chart review. The average ALS Functional Rating Scale – Revised (ALSFRS-R) baseline score at first clinical visit was 34.08, and average decline was − 0.64 per month. Median survival was 27 months after first visit. Higher baseline ALSFRS-R score and BMI were associated with improved survival, higher baseline age was associated with decreased survival.ConclusionsThis study serves to show that EMR-captured data can be extracted and used to track outcomes in an ALS clinic setting, potentially important for post-marketing research of new drugs, or as historical controls for future studies. However, as automated EMR-based data extraction becomes more widely used there will be a need to standardize ALS data elements and clinical forms for data capture so data can be pooled across academic centers.

Highlights

To assess the feasibility of using automated capture of Electronic Medical Record (EMR) data to build predictive models for amyotrophic lateral sclerosis (ALS) outcomes
To determine if these variables could be automatically extracted from the EMR, we conducted a retrospective chart review of patients seen at the University of Kansas Medical Center (KUMC) ALS Clinic between summer 2013 and summer 2016
Accuracy of EMR data A general search based on ICD10 code identified 572 subjects; 354 patients had at least one ALSFRS-R recorded in the EMR (62.4%), 352 of which were deemed eligible for analysis (Fig. 1)

Summary

Introduction

To assess the feasibility of using automated capture of Electronic Medical Record (EMR) data to build predictive models for amyotrophic lateral sclerosis (ALS) outcomes. Amyotrophic Lateral Sclerosis (ALS) is a fatal neuro-degenerative disease. While over 50 clinical trials have been conducted over the last two decades, none have been successful save riluzole and edaravone [1], which at best offer modest improvements in survival or function [2]. While many studies may have failed because the drugs were ineffective, a recurring theme in ALS are trials which do not meet their primary outcome but yield indeterminate results [3]. Two major hurdles to conducting ALS trials are the rarity of ALS 100,000 people in the US [4]) and the disease’s heterogeneity [5], which is a barrier to properly powered studies

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