RESUMO
Purpose: The primary objective of this study is to determine if ultrasound-guided erector spinae plane blocks (ESPB) prior to thoracolumbar spinal fusion reduces opioid consumption in the first 24 hours postoperatively. Secondary objectives include ESPB effects on administration of opioids, utilization of intravenous patient-controlled analgesia (IV-PCA), pain scores, length of stay, and opioid related side effects. Methods: A retrospective cohort analysis was performed on consecutive, adult patients undergoing primary thoracolumbar fusion procedures. Demographic and baseline characteristics including diagnoses of chronic pain, anxiety, depression, and preoperative use of opioids were collected. Surgical data included surgical levels, opioid administration, and duration. Postoperative data included pain scores, opioid consumption, IV-PCA duration, opioid-related side effects, ESPB-related complications, and length of stay (LOS). Statistical analysis was performed using chi-squared and t-test analyses, multivariable analysis, and covariate adjustment with propensity score. Results: A total of 118 consecutive primary thoracolumbar fusions were identified between October 2019 and December 2021 (70 ESPB, 48 no-block [NB]). There were no significant demographic or surgical differences between groups. Median surgical time (262.50 mins vs 332.50 mins, p = 0.04), median intraoperative opioid consumption (8.11 OME vs 1.73 OME, p = 0.01), and median LOS (152.00 hrs vs 128.50 hrs, p = 0.01) were significantly reduced in the ESPB group. Using multivariable covariate adjustment with propensity score analysis only intraoperative opioid administration was found to be significantly less in the ESPB cohort. Conclusion: ESPB for thoracolumbar fusion can be performed safely in index cases. There was a reduction of intraoperative opioid administration in the ESPB group, however the care team was not blinded to the intervention. Extensive thoracolumbar spinal fusion surgery may require a different approach to regional anesthesia to be similarly effective as ESPB in isolated lumbar surgeries.
RESUMO
BACKGROUND: Accurate and rapid identification of implant manufacturer and model is critical in the evaluation and management of patients requiring revision total shoulder arthroplasty (TSA). Failure to correctly identify implant designs in these circumstances may lead to delay in care, unexpected intraoperative challenges, increased morbidity, and excess health care costs. Deep learning (DL) permits automated image processing and holds the potential to mitigate such challenges while improving the value of care rendered. The purpose of this study was to develop an automated DL algorithm to identify shoulder arthroplasty implants from plain radiographs. METHODS: A total of 3060 postoperative images from patients who underwent TSA between 2011 and 2021 performed by 26 fellowship-trained surgeons at 2 independent tertiary academic hospitals in the Pacific Northwest and Mid-Atlantic Northeast were included. A DL algorithm was trained using transfer learning and data augmentation to classify 22 different reverse TSA and anatomic TSA prostheses from 8 implant manufacturers. Images were split into training and testing cohorts (2448 training and 612 testing images). Optimized model performance was assessed using standardized metrics including the multiclass area under the receiver operating characteristic curve (AUROC) and compared with a reference standard of implant data from operative reports. RESULTS: The algorithm classified implants at a mean speed of 0.079 seconds (±0.002 seconds) per image. The optimized model discriminated between 8 manufacturers (22 unique implants) with AUROCs of 0.994-1.000, accuracy of 97.1%, and sensitivities between 0.80 and 1.00 on the independent testing set. In the subset of single-institution implant predictions, a DL model identified 6 specific implants with AUROCs of 0.999-1.000, accuracy of 99.4%, and sensitivity >0.97 for all implants. Saliency maps revealed key differentiating features across implant manufacturers and designs recognized by the algorithm for classification. CONCLUSION: A DL model demonstrated excellent accuracy in identifying 22 unique TSA implants from 8 manufacturers. This algorithm may provide a clinically meaningful adjunct in assisting with preoperative planning for the failed TSA and allows for scalable expansion with additional radiographic data and validation efforts.