An analysis of significant neuromonitoring changes (NMCs) and evaluation of the efficacy of multimodality neuromonitoring in spinal deformity surgery. A retrospective review of prospectively collected data in 320 consecutive paediatric and adult spinal deformity operations. Patients were sub-grouped according to demographics (age, gender), diagnosis, radiographic findings (Cobb angles, MR abnormalities) and operative features (surgical approach, duration, levels of fixation). Post-operative neurological deficit was documented and defined as either spinal cord or nerve root deficit.Aim:
Method:
To establish the current practice of spinal cord monitoring in units carrying out scoliosis surgery in the UK. To illustrate the benefit of routinely monitoring motor evoked potentials (MEPs). Questionaire: Nationwide survey of spinal monitoring modalities used by spinal units carrying out deformity surgery. 10 out of 27 units routinely measure motor evoked potentials (MEPs), the remainder use only sensory potentials (SEPs). There is significant variability in use of monitoring around the UK and we have compared this to the practice elsewhere in the world. We report the case of a thirteen year old girl who underwent posterior instrumentation for correction of an idiopathic scoliosis. Intra-operatively there was a significant reduction in the amplitude of the MEPs without any corresponding change in the SEPs. These changes reversed when the correction was released. The surgery was abandoned and was carried out as a staged procedure, initially anteriorly then posteriorly. There was no loss of motor potentials during either operation and no post operative neurological abnormalities. We propose that the changes noted initially were due to transient ischaemia of the cord which would not have been detected without MEPs and may have led to long term sequelae. This highlights the safety benefit of routinely using MEPs in scoliosis surgery. Nationally there is wide variation in the monitoring of spinal cord function during scoliosis surgery. We feel that monitoring of motor potentials is a vital component in ensuring scoliosis surgery is as safe as possible.
Data from the wait list management system and hospital databases was used to develop a computer model simulating the resource requirements required during patient flow into, through, and out of orthopaedic surgery for TKR, THR and knee arthroscopy. Results from the simulation model suggested that inpatient beds, rather than operating room time was the constraining resource and an extra twenty-five beds and 30% more OR time would stabilize and subsequently reduce the wait time at the institution. In addition, simulations suggested that pooling surgeon wait lists reduced patient wait time. Simulation models are an effective resource allocation decision-making tool for orthopaedic surgery. To develop and implement a wait list simulation model to analyze the existing system and guide resource allocation decision-making at the QEII Health Sciences Centre. The simulation model suggests an immediate increase in inpatient surgical beds from sixty-six to ninety-one followed by a 30% increase in OR time in thirty months to stabilize and subsequently reduce patient wait times. Simulations showed that pooling surgeon waiting lists reduced patient wait time, however, dividing orthopaedics resources among two facilities had little effect. Adding twenty-five beds reduced the wait time growth rate substantially, but not to zero, while adding fifty beds reduced the wait time growth rate to zero. Adding twenty-five beds and 30% more OR time had the same result as adding fifty beds. Simulation models can be effective for guiding resource allocation decisions for orthopaedic surgery. Recommendations based on the wait list simulation model results were immediately adopted by the provincial Department of Health. A simulation model of the orthopaedic surgery system at the institution was created using Arena simulation software. Empirical statistical distributions were developed based on Wait List Management System and administrative data to assign values to model variables: number of patient referrals seen per office session; proportion of patient referrals actually converting to a surgery booking; type of procedure required; admission status; time required for surgery; and length of stay. The model was tested, and validated. Several scenarios with adjusted levels of resources variables (OR time, number of surgeons, length of stay, inpatient bed availability) were simulated.
