Predictors of Respiratory Insufficiency in Amyotrophic Lateral Sclerosis

University Of Pennsylvania, Philadelphia PA

Investigators

Linked publications & trials

Abstract

Project Summary/Abstract Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with high morbidity and universal mortality due to respiratory failure. The onset of respiratory weakness heralds an increased risk of aspiration related to bulbar muscle weakness and ineffective cough, hypercapnic respiratory failure due to chronic hypoventilation, and pulmonary infections, ultimately leading to death. Despite the key role of respiratory failure in the morbidity and mortality associated with ALS, there remains uncertainty concerning optimum initiation and maintenance of respiratory care for this disease. ALS has a very heterogeneous clinical presentation and symptom progression, which causes variable evolution of respiratory involvement. Given the significance of respiratory morbidity with this disease combined with the unclear timing, a better understanding of the risk factors for onset and progression of respiratory muscle weakness could improve quality of life and even survival in ALS. Unfortunately, there is no prediction model for respiratory failure in ALS, no established phenotypes for ALS respiratory progression (most importantly, ?rapid progressors?), and there is significant variation between clinicians and centers regarding the management of respiratory failure in ALS. A prediction model and defined phenotypes of respiratory weakness in ALS could improve timeliness of interventions, facilitate communication, inform clinical trial design, and elucidate novel disease mechanisms. The goals of this study are to identify predictors of onset of respiratory failure and to identify distinct phenotypes of rapidity and pattern of the progression of respiratory dysfunction. First, I aim to develop and validate a prediction model for developing respiratory insufficiency within six months of diagnosis using a logistic regression model and predictive model analytics. Second, I aim to categorize individuals based on the rate of change of respiratory muscle strength (as measured by spirometry) over time using group-based trajectory modeling. Next, I will identify group membership probabilities according to baseline characteristics using a multinomial logistic regression model. This project will provide essential preliminary data for a Career Development Award that will (1) examine the impact of risk of respiratory failure on short and long-term outcomes of patients with ALS, (2) prospectively validate the prediction model, (3) develop an intervention to mitigate patient-centered outcomes associated with respiratory insufficiency in ALS, and (4) identify which characteristics are associated with different trajectories of respiratory function, thus allowing for personalized medicine.