Concomitant Botulinum Toxin Injections for Neurogenic Detrusor Overactivity and Spasticity-A Retrospective Analysis of Practice and Safety.
Arnaud LeilazCharles JoussainPierre DenysDjamel BensmailJonathan LévyPublished in: Toxins (2024)
As multiple indications for botulinum toxin injections (BTIs) can coexist for neurological patients, there are to date no description of concomitant injections (CIs) to treat both spasticity and neurogenic detrusor overactivity incontinence (NDOI) in patients with spinal cord injuries (SCIs) and multiple sclerosis (MS). We therefore identified patients followed at our institution by health data hub digging, using a specific procedure coding system in use in France, who have been treated at least once with detrusor and skeletal muscle BTIs within the same 1-month period, over the past 5 years (2017-2021). We analyzed 72 patients representing 319 CIs. Fifty (69%) were male, and the patients were mostly SCI (76%) and MS (18%) patients and were treated by a mean number of CIs of 4.4 ± 3.6 [1-14]. The mean cumulative dose was 442.1 ± 98.8 U, and 95% of CIs were performed within a 72 h timeframe. Among all CIs, five patients had symptoms evocative of distant spread but only one had a confirmed pathological jitter in single-fiber EMG. Eleven discontinued CIs for surgical alternatives: enterocystoplasty (five), tenotomy (three), intrathecal baclofen (two) and neurotomy (one). Concomitant BTIs for treating both spasticity and NDOI at the same time appeared safe when performed within a short delay and in compliance with actual knowledge for maximum doses.
Keyphrases
- end stage renal disease
- botulinum toxin
- newly diagnosed
- multiple sclerosis
- chronic kidney disease
- spinal cord injury
- skeletal muscle
- healthcare
- peritoneal dialysis
- prognostic factors
- mental health
- metabolic syndrome
- primary care
- lymph node
- patient reported outcomes
- mass spectrometry
- climate change
- machine learning
- public health
- risk assessment
- ms ms
- big data
- subarachnoid hemorrhage
- human health
- data analysis