Symptoms timeline and outcomes in amyotrophic lateral sclerosis using artificial intelligence Articles uri icon

authors

  • Segura, Tomás
  • Medrano, Ignacio H.
  • Collazo, Sergio
  • Maté, Claudia
  • SGUERA, CARLO
  • del Río Bermúdez, Carlos
  • Casero, Hugo
  • Salcedo, Ignacio
  • García García, Jorge
  • Alcahut Rodríguez, Cristian
  • Aquino, José
  • Casadevall, David
  • Donaire, David
  • Marin Corral, Judith
  • Menke, Sebastian
  • Polo, Natalia
  • Taberna, Miren

publication date

  • January 2023

start page

  • 1

end page

  • 10

issue

  • 702

volume

  • 13

International Standard Serial Number (ISSN)

  • 2045-2322

abstract

  • Amyotrophic lateral sclerosis (ALS) is a fatal, neurodegenerative motor neuron disease. Although an early diagnosis is crucial to provide adequate care and improve survival, patients with ALS experience a significant diagnostic delay. This study aimed to use real-world data to describe the clinical profile and timing between symptom onset, diagnosis, and relevant outcomes in ALS. Retrospective and multicenter study in 5 representative hospitals and Primary Care services in the SESCAM Healthcare Network (Castilla-La Mancha, Spain). Using Natural Language Processing (NLP), the clinical information in electronic health records of all patients with ALS was extracted between January 2014 and December 2018. From a source population of all individuals attended in the participating hospitals, 250 ALS patients were identified (61.6% male, mean age 64.7 years). Of these, 64% had spinal and 36% bulbar ALS. For most defining symptoms, including dyspnea, dysarthria, dysphagia and fasciculations, the overall diagnostic delay from symptom onset was 11 (6-18) months. Prior to diagnosis, only 38.8% of patients had visited the neurologist. In a median post-diagnosis follow-up of 25 months, 52% underwent gastrostomy, 64% non-invasive ventilation, 16.4% tracheostomy, and 87.6% riluzole treatment; these were more commonly reported (all Ps less than 0.05) and showed greater probability of occurrence (all Ps less than 0.03) in bulbar ALS. Our results highlight the diagnostic delay in ALS and revealed differences in the clinical characteristics and occurrence of major disease-specific events across ALS subtypes. NLP holds great promise for its application in the wider context of rare neurological diseases.

subjects

  • Computer Science
  • Medicine