Botulinum Toxin Type A (BoNT-A) Use for Post-Stroke Spasticity: A Multicenter Study Using Natural Language Processing and Machine Learning.
María Jesús AntónMontserrat MolinaJosé Gabriel PérezSantiago PinaNoemí TapiadorBeatriz De La CalleMónica MartínezPaula OrtegaMaría Belén RuspaggiariConsuelo TudelaMarta ConejoPedro LenoMarta LópezCarmen MarhuendaCarlos Arias-CabralesPascal MaisonobeAlberto HerreraErnesto CandauPublished in: Toxins (2024)
We conducted a multicenter and retrospective study to describe the use of botulinum toxin type A (BoNT-A) to treat post-stroke spasticity (PSS). Data were extracted from free-text in electronic health records (EHRs) in five Spanish hospitals. We included adults diagnosed with PSS between January 2015 and December 2019, stratified into BoNT-A-treated and untreated groups. We used EHRead ® technology, which incorporates natural language processing and machine learning, as well as SNOMED CT terminology. We analyzed demographic data, stroke characteristics, BoNT-A use patterns, and other treatments. We reviewed the EHRs of 1,233,929 patients and identified 2190 people with PSS with a median age of 69 years; in total, 52.1% were men, 70.7% had cardiovascular risk factors, and 63.2% had suffered an ischemic stroke. Among the PSS patients, 25.5% received BoNT-A at least once. The median time from stroke to spasticity onset was 205 days, and the time from stroke to the first BoNT-A injection was 364 days. The primary goal of BoNT-A treatment was pain control. Among the study cohort, rehabilitation was the most common non-pharmacological treatment (95.5%). Only 3.3% had recorded monitoring scales. In conclusion, a quarter of patients with PSS received BoNT-A mainly for pain relief, typically one year after the stroke. Early treatment, disease monitoring, and better data documentation in EHRs are crucial to improve PSS patients' care.
Keyphrases
- botulinum toxin
- electronic health record
- machine learning
- end stage renal disease
- newly diagnosed
- atrial fibrillation
- ejection fraction
- cardiovascular risk factors
- chronic kidney disease
- spinal cord injury
- prognostic factors
- healthcare
- magnetic resonance imaging
- big data
- computed tomography
- type diabetes
- autism spectrum disorder
- cardiovascular disease
- patient reported outcomes
- metabolic syndrome
- spinal cord
- artificial intelligence
- combination therapy
- ultrasound guided
- cerebral ischemia
- patient reported
- positron emission tomography
- double blind