Symptoms timeline and outcomes in amyotrophic lateral sclerosis using artificial intelligence

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 < 0.05) and showed greater probability of occurrence (all Ps < 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.

Advancements and challenges in amyotrophic lateral sclerosis

“…A challenge with treating this disease lays with the difficulties around diagnosing ALS. It can take up to 14 months between the first clinic visit to an accurate diagnosis, with some groups reporting a delay of up to 24 months at the high end ( Chiò, 1999 ; Segura et al, 2023 ). This delay in diagnosis is detrimental to the patient’s overall treatment, causing a heavy burden to both the patient’s mental health and more importantly, drastically delaying targeted treatment.…”

DALSO: domain ALS ontology

“…A fact that can aggravate prognosis of ALS is diagnostic delay (Campos et al 2021(Campos et al , 2023 averaging 5-27 months, depending on the disease onset region (Sennfält et al 2023), which urge to be changed (Andersen et al 2012;Segura et al 2023). This can be primarily attributed to the clinical heterogeneity of ALS, recognized as a consistent disease feature (e.g.…”

“…These features of ontological models are reasons for which they have been proven as practical solutions in biomedical areas such as: (i) knowledge formalization and sharing (Sánchez and Moreno 2008; Staab and Studer 2010), (ii) logical, unequivocal concept definition in biomedicine (Bug et al 2008; Silachan and Tantatsanawong 2011; Tao et al 2013) (iii) integration, comparison and reuse of data and information (Ruttenberg et al 2009; Ivanović and Budimac 2014; Nielsen 2014), and eventually (iv) new methods for genetic data analyses (Shah et al 2012; Bean et al 2020; Segura et al 2023).…”