Efficacy of Intravenous versus Intrathecal Meperidine on Post-Spinal Shivering after Knee Arthroscopy: A Randomized Controlled Study

Background and Aims: Hypothermia is considered a common problem with anesthesia. Spinal anesthesia, affects the process of temperature regulation. The aim of this study was to compare the prophylactic effect of intravenous (IV) meperidine with intrathecal (IT) meperidine on the prevention of shivering during spinal anesthesia in patients underwent knee arthroscopy. Aim of the Study: The aim of this work is to study the effect of meperidine either intravenous (IV) vs intrathecal (IT) on controlling postoperative shriving after knee arthroscopy. Materials and Methods: This study will be conducted in accordance with the principles of the ethics committee Benha University hospitals. Full written informed consent will be obtained from all patients before inclusion in the study, and a prospective randomized double-blinded study was conducted in adult patients undergoing knee arthroscopy under spinal anesthesia. Cases are divided into three equal groups (20 patients in each). Group IT: will receive spinal anesthesia (12.5 mg heavy bupivacaine) with intrathecal meperidine (25 mg). Group IV: will receive spinal anesthesia (12.5 mg heavy bupivacaine) with intravenous meperidine (25 mg). Group C: will receive spinal anesthesia (12.5 mg heavy bupivacaine) only postoperatively. Incidence and degree of shivering (SS), sedation score (RSS), pain score (VAS), hemodynamics, and PONV will be measured at the following intervals: 0 min (once arrived at PACU), 20 min later, 40 min later, 80 min later. Results: Shivering was observed in 0%, 0%, and 10.5% of patients in Groups IT, IV, and C, respectively. There was a significant difference between Group IT and IV compared to Group C (P Conclusion: We concluded that IT meperidine and IV meperidine comparably can decrease intensity and incidence of shivering compared to control group as well as decrease the requirement for additional doses of meperidine for shivering control without any hemodynamic side effect. And intrathecal meperidine showed better postoperative pain control and less postoperative nausea and vomiting (PONV).

Cyclops syndrome following anterior cruciate ligament reconstruction: Can relapse occur after surgery?

Symptomatic cyclops lesions are complications that can be seen at rates of up to approximately 10%after anterior cruciate ligament reconstruction.However,recurrent cyclops lesions have rarely been documented.There are case rare series in the literature regarding the treatment of recurrent cyclops lesion.Future large studies are needed to investigate factors contributing to the development of cyclops lesions and syndrome and treatment options.

Recurrent cyclops lesion after primary anterior cruciate ligament reconstruction using bone tendon bone allograft:A case report

BACKGROUND Cyclops lesions are a known complication of anterior cruciate ligament(ACL)reconstruction,with symptomatic cyclops syndrome occurring in up to 11%of surgeries.Recurrent cyclops lesions have been rarely documented;this case study documents the successful treatment of a recurrent cyclops lesion.CASE SUMMARY A 28-year-old female presented following a non-contact injury to the right knee.Workup and clinical exam revealed an ACL tear,and arthroscopic reconstruction was performed.Two years later a cyclops lesion was discovered and removed via arthroscopic synovectomy.Seven months postoperatively,the patient presented with pain,stiffness,and difficulty achieving terminal extension.A smaller recurrent cyclops lesion was diagnosed,and a repeat synovectomy was performed.The patient recovered fully.CONCLUSION To the best of our knowledge,this is the first documented case of recurrent cyclops lesion after bone-patellar tendon-bone allograft ACL reconstruction presenting as cyclops syndrome.

Clinical,imaging,arthroscopic,and histologic features of bilateral anteromedial meniscofemoral ligament:A case report

BACKGROUND The anteromedial meniscofemoral ligament(AMMFL)is a very rare entity,commonly unrecognized and underreported.Although it was not proved to be a cause of anterior knee pain,concerns have been raised on the relationship between the presence of this structure and medial meniscus injury secondary to its abnormal motion.Regarding histologic examination,some studies have shown meniscus-like fibrocartilage,while others have identified it as ligament-like collagenous fibrous connective tissue.CASE SUMMARY We report the case of a 34-year-old ballerina with an AMMFL associated with a torn medial meniscus of both knees.Surgery was performed to treat the meniscal injury and two biopsies of each AMMFL were taken in different locations to define the histopathological composition.Histologic examination revealed fibrocartilaginous tissue compatible with meniscus.Follow-up evaluation one year after surgery evidenced full remission of symptoms and the patient had resumed her athletic activities.CONCLUSION Clinical,magnetic resonance imaging,arthroscopic,and histological features have been carefully described to better characterize the AMMFL.

Arthroscopic scene segmentation using multispectral reconstructed frames and deep learning

Background Knee arthroscopy is one of the most complex minimally invasive surgeries,and it is routinelyperformed to treat a range of ailments and injuries to the knee joint.Its complex ergonomic design imposesvisualization and navigation constraints,consequently leading to unintended tissue damage and a steep learningcurve before surgeons gain proficiency.The lack of robust visual texture and landmark frame features furtherlimits the success of image-guided approaches to knee arthroscopy Feature-and texture-less tissue structures ofknee anatomy,lighting conditions,noise,blur,debris,lack of accurate ground-truth label,tissue degeneration,and injury make semantic segmentation an extremely challenging task.To address this complex research problem,this study reported the utility of reconstructed surface reflectance as a viable piece of information that could beused with cutting-edge deep learning technique to achieve highly accurate segmented scenes.Methods We proposed an intraoperative,two-tier deep learning method that makes full use of tissue reflectanceinformation present within an RGB frame to segment texture-less images into multiple tissue types from kneearthroscopy video frames.This study included several cadaver knees experiments at the Medical and EngineeringResearch Facility,located within the Prince Charles Hospital campus,Brisbane Queensland.Data were collectedfrom a total of five cadaver knees,three were males and one from a female.The age range of the donors was 56–93 years.Aging-related tissue degeneration and some anterior cruciate ligament injury were observed in mostcadaver knees.An arthroscopic image dataset was created and subsequently labeled by clinical experts.Thisstudy also included validation of a prototype stereo arthroscope,along with conventional arthroscope,to attainlarger field of view and stereo vision.We reconstructed surface reflectance from camera responses that exhibiteddistinct spatial features at different wavelengths ranging from 380 to 730 nm in the RGB spectrum.Toward theaim to segment texture-less tissue types,this data was used within a two-stage deep learning model.Results The accuracy of the network was measured using dice coefficient score.The average segmentationaccuracy for the tissue-type articular cruciate ligament(ACL)was 0.6625,for the tissue-type bone was 0.84,and for the tissue-type meniscus was 0.565.For the analysis,we excluded extremely poor quality of frames.Here,a frame is considered extremely poor quality when more than 50%of any tissue structures are over-orunderexposed due to nonuniform light exposure.Additionally,when only high quality of frames was consideredduring the training and validation stage,the average bone segmentation accuracy improved to 0.92 and theaverage ACL segmentation accuracy reached 0.73.These two tissue types,namely,femur bone and ACL,have ahigh importance in arthroscopy for tissue tracking.Comparatively,the previous work based on RGB data achieveda much lower average accuracy for femur,tibia,ACL,and meniscus of 0.78,0.50,0.41,and 0.43 using U-Net and0.79,0.50,0.51,and 0.48 using U-Net++.From this analysis,it is clear that our multispectral method outperformsthe previously proposed methods and delivers a much better solution in achieving automatic arthroscopic scenesegmentation.Conclusion The method was based on the deep learning model and requires a reconstructed surface reflectance.It could provide tissue awareness in an intraoperative manner that has a high potential to improve surgicalprecisions.It could be applied to other minimally invasive surgeries as an online segmentation tool for training,aiding,and guiding the surgeons as well as image-guided surgeries.