Objective:

To develop a predictive model to identify incident amyotrophic lateral sclerosis (ALS) cases using Medicare data.

Background:

ALS is a rapidly progressive and nearly universally fatal neurodegenerative disease. Delayed diagnosis results in unnecessary morbidity and early mortality in many patients. We sought to develop a claims-based predictive model using a large, population-based Medicare dataset. 

Design/Methods:

We used age-eligible Medicare beneficiaries to identify all incident ALS patients in 2017 and 2018 (N=8,050) and randomly selected controls (N=32,200) using a 1:4 ratio frequency matched on year and month of diagnosis. We used Medicare data from 2015-2018 to create predictor variables for demographics (age, sex, race/ethnicity), a measure for use of care, and all ICD-10-CM diagnosis codes assigned by beneficiaries from October 2015 up to the beneficiary diagnosis or control reference date. We randomly divided the data into 80% training and 20% validation and then used the training data to run a penalized regression model (elastic net) to identify demographic and diagnosis predictors of ALS-case status. We selected the two hyper-parameters (alpha and lambda) using five-fold cross validation. The predictive accuracy of the model was determined using area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs).

Results:

There were 4,462 ICD-10-CM diagnosis codes observed among our cases and controls. The AUC of a model with only demographic characteristics and use of care was 0.705 (95% CI: 0.694-0.717). Our elastic net model with demographics and diagnosis codes resulted in an AUC in the validation set of 0.903 (95% CI: 0.893-0.913) using the best alpha of 0.3 and 735 predictors. For the optimal cut point for the model, the sensitivity was 79% and specificity was 88%.

Conclusions:

A claims-based, ALS predictive model using only demographic characteristics and diagnosis codes had excellent discriminability and may be useful to facilitate earlier diagnosis of ALS patients.