Challenge of managing distal femur fractures with long-stemmed total knee implants.

OBJECTIVE: Treatment of distal femur fractures by long-stemmed total knee arthroplasty (TKA) is challenging, because of poor bone stock, decreased blood supply, history of multiple knee surgeries and an absence of standard treatment. Few published studies are available concerning this. The purpose of this study was to share surgical technique and better describe our patients' comorbidities, which add to the challenge of managing individuals with these fractures. METHODS: Between August 2008 and September 2013, seven patients presented to our level I trauma center with distal femoral fractures associated with long-stemmed TKA implants. Their average age was 68.71 years (range, 52-81 years).The most common mechanism of injury was fall (five patients), followed by a traumatic fracture of the femur while walking (one patient), and being lifted out of bed (the one nonambulatory patient). This retrospective study reports a treatment protocol, including surgical technique, and short-term outcome in seven patients in whom locking compression plates (LCP) were used. RESULTS: Six fractures were classified as Rorabeck type II, and one as type III. The average time to full-weight-bearing was 5.5 months. At this institution, good short-term results have been achieved by using an LCP with screws placed proximal to the long-stem and distal to the fracture. The six patients all achieved full-weight-bearing,taking an average of 5.5 months (range, 3-7 months). CONCLUSIONS: LCP is an effective form of management of distal femur fractures around long stem TKAs. An individualized operative approach possibly incorporating bone-graft substitutes, cerclage wire and a post-operative bone stimulator is recommended.

Intraoperative MRI electrical noise and monitor ECG filters affect arrhythmia detection and identification.

Most electrical equipment in the modern operating room (OR) radiates electrical noise (EN) that can interfere with patient monitors. We have described the EN that an intraoperative magnetic resonance imaging (iMRI) system emits and have shown that this high-energy EN diminishes the quality of the ECG waveform during iMRI scans in our neurosurgical OR. We have also shown that the ECG signal filters in our iMRI-compatible patient monitor reduce this interference but, in the process, disturb the true morphology of the displayed waveform. This simulation study evaluates how iMRI-generated EN affects the ability of the anesthetist to detect and identify ECG arrhythmias and whether the patient monitor's ECG signal filters can improve arrhythmia recognition. Using an ECG simulator, we generated Lead II and V5 ECG signal segments that contained either no arrhythmia or one of four common cardiac arrhythmias. We filtered the ECG segments with four filters available on our iMRI-compatible monitor (Veris MR, MEDRAD Inc., Indianola, PA USA). We then digitized the segments and mixed simulated iMRI EN into the resultant tracings. With institutional approval and written informed consent, board-certified anesthesiologists reviewed the tracings, determined if an arrhythmia was present and identified the arrhythmia. We conducted the study anonymously. We reported the data as percent correct arrhythmia detection and correct arrhythmia identification. Thirty-one anesthesiologists completed the study. Overall, the participants correctly detected 79.5% (95% CI: 77.2, 81.7%) of the arrhythmias and correctly identified 62.5% (95% CI: 59.8, 65.3%) of the arrhythmias, regardless of EN presence. Although the proportions among monitor noise filters studied were not significant, the manufacturer-designated MR5 Veris MR filter optimized arrhythmia detection and arrhythmia identification for our participants, regardless if EN was present in the ECG tracings. In the neurosurgical OR, the anesthetist must be able to effectively monitor a patient in the presence of iMRI-generated EN. Depending on the OR design, the patient may be out of the anesthetist's direct view during a scan procedure. The anesthetist must rely on monitored physiologic parameters to assess patient status during this time. He/she should be familiar with his/her monitor's filtering capabilities and routinely adjust the ECG filters to achieve the best compromise between minimized EN effects and maximized displayed ECG signal quality.