Tertiary Pediatric Academic Institution’s Experience With Intraoperative Neuromonitoring for Nonspinal Surgery in Children With Mucopolysaccharidosis, Based on a Novel Evidence-Based Care Algorithm
Autor: | Vidya Chidambaran, Matthew Careskey, Emily Chesnut, Cindy S Pettit, Veronica O. Busso, Junichi Tamai, Nancy D. Leslie, David Buck, John J. McAuliffe, Lisa Berry, A. Kandil |
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Rok vydání: | 2020 |
Předmět: |
Adult
medicine.medical_specialty Adolescent Intraoperative Neurophysiological Monitoring Mucopolysaccharidosis Decision Making MEDLINE Pediatrics Tertiary Care Centers Academic institution Young Adult 03 medical and health sciences 0302 clinical medicine 030202 anesthesiology Evoked Potentials Somatosensory medicine Electronic Health Records Humans Interdisciplinary communication Kyphosis Young adult Child Retrospective Studies Academic Medical Centers Evidence-Based Medicine business.industry Infant Retrospective cohort study Evidence-based medicine Mucopolysaccharidoses Surgical procedures Evoked Potentials Motor medicine.disease Surgery Anesthesiology and Pain Medicine Scoliosis Child Preschool Surgical Procedures Operative Interdisciplinary Communication business Algorithms 030217 neurology & neurosurgery |
Zdroj: | Anesthesia & Analgesia. 130:1678-1684 |
ISSN: | 0003-2999 |
DOI: | 10.1213/ane.0000000000004215 |
Popis: | Musculoskeletal deformities in mucopolysaccharidoses (MPSs) patients pose unique challenges when patients present for surgery, especially nonspinal surgery. MPS patients have developed postsurgical neurological deficits after nonspinal surgery. While the incidence of neurological deficits after nonspinal surgery under anesthesia is unknown, accumulating evidence provides impetus to change current practice and increased neurological monitoring in these patients. Intraoperative neurophysiologic monitoring (IONM) with somatosensory evoked potentials (SSEPs) and transcranial motor evoked potentials (TcMEPs) has been implemented at select institutions with varying degree of success. This report describes our experience with IONM in the context of a multidisciplinary evidence-based care algorithm we developed at Cincinnati Children's Hospital Medical Center.We conducted a retrospective chart review of the electronic medical record (EPIC), for data from all MPS patients at our institution undergoing nonspinal surgery between September 2016 and March 2018. Patients were identified from IONM logs, which include procedure and patient comorbidities. Data concerning demographics, morbidities, degree of kyphoscoliosis, intraoperative administered medications and vital signs, surgical procedure, the IONM data, duration of surgery, and blood loss were extracted. Descriptive analyses were generated for all variables in the data collected. In addition, any IONM changes noted during the surgeries were identified and factors contributing to the changes described.Thirty-eight patients with a diagnosis of MPS underwent nonspinal surgery, and of those 38, 21 received IONM based on preoperative decision-making according to our care algorithm. Of the 21 patients who received IONM, we were able to get reliable baseline potentials on all patients. Of the 21 patients, 3 had significant neurophysiologic changes necessitating surgical/anesthetic intervention. All of these changes lasted several minutes, and the real-time IONM monitoring was able to capture them as they arose. None of the patients sustained residual neurological deficits. Thus, children who did not fit the criteria for IONM (n = 13) based on our algorithm had 0% incidence of any untoward neurological deficits after surgery (97.5% confidence interval [CI], 00%-25.5%), while 14% (95% CI, 11.5%-30.1%) of children who did fit criteria for IONM and had IONM had significant IONM changes.Through this case series, we describe our experience with the use of IONM and a novel care algorithm for guiding the anesthetic management of MPS patients undergoing nonspinal surgery. We conclude that they can be useful tools for provision of safe anesthetic care in this high-risk cohort. |
Databáze: | OpenAIRE |
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