Functional outcomes and complications of plate fixation for midshaft clavicle fractures by type and location: a systematic review and meta-analysis.

Background: Various plate types are used in the surgical treatment of displaced midshaft clavicle fractures. These plates can be positioned in different locations on the clavicle, although no studies to date have elucidated optimal plate type and location of fixation. This systematic review compares the functional outcomes and complications in the management of displaced midshaft clavicle fractures using plate fixation by stratifying by both plate type and location. Methods: A systematic review according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted to identify all papers reporting functional outcomes, union rates, and/or complications using plates for the management of midshaft clavicle fractures. Multiple databases and trial registries were searched from inception until March 2022. A meta-analysis was conducted for functional outcomes and type of complication, stratified by plate type (locking, compression, or reconstruction) and location (superior or anteroinferior). Pooled estimates of functional outcome scores and incidence of complications were calculated using a random effects model. Risk of bias and quality were assessed using the risk of bias version 2 and ROBINS-I (Risk Of Bias In Non-randomised Studies - of Interventions) tools. The confidence in estimates were rated and described according to the recommendations of the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) working group. Results: Forty-five studies were included in the systematic review and 43 were included in the meta-analysis. Depending on plate type and location, pooled Constant-Murley Scores ranged from 89.23 to 93.48 at 12 months. Nonunion rates were 3% (95% confidence interval [CI] 1-6) for superior locking plates (GRADE Low). Rates of any complication (nonunion, hardware failure, hardware irritation, wound dehiscence, keloid, superficial infection, deep infection, delayed union, malunion, and/or persistent pain) by plate type and location ranged from 3% to 17% (GRADE Very Low to Moderate). Superior compression plates had the highest incidence of any complications (17% [95% CI 5-44], GRADE Very Low), while anterior inferior compression plates had the lowest incidence of any complication (3% [95% CI 0-15], GRADE Very Low). Hardware irritation was the most reported individual complication for superior locking plates and superior compression plates, 11% (95% CI 7-17, GRADE Low) and 11% (95% CI 3-33, GRADE Very Low), respectively. Conclusion: Although most studies were of low quality, studies reporting functional outcomes generally showed good functional results and similar incidence of any complication regardless of plate type and location. There is no evidence of a plate and location combination to optimize patient functional outcomes or complications. We were unable to reliably evaluate union rates or individual complications for most plate types stratified by location.

Characterizing Trends in Diagnosis and Management of Sinusitis in a Large Health Care System: From Primary Care to Otolaryngology.

OBJECTIVES: Variations in management of sinusitis in primary care settings can be associated with inappropriate antibiotic prescriptions and delays in treatment. The objective of this study was to identify patient and provider characteristics associated with possible inaccurate diagnosis and management of sinusitis. METHODS: We performed a cross-sectional retrospective analysis using an established regional healthcare database of patients who received a diagnosis of sinusitis between 2011 and 2022 from a non-otolaryngologist provider. Patient's comorbidities, insurance status, chronicity of sinusitis, and prescriptions were included. We noted if patients were referred to an otolaryngology practice and if they received a diagnosis of sinusitis from an otolaryngologist. RESULTS: We analyzed 99 581 unique patients and 168 137 unique encounters. The mean age was 41.5 (±20.4 years) and 35.7% were male. Most patients had private insurance (88.5%), acute sinusitis (81.2%), and were seen at a primary care office (97.8%). Approximately 30% of patients were referred to an otolaryngology practice for sinusitis. Of referred patients, 50.6% did not receive a diagnosis of sinusitis from an otolaryngology practice. Patients without a sinusitis diagnosis by an otolaryngology practice received significantly more mean courses of antibiotics (5.04 vs 2.39, P < .0001) and oral steroids (3.53 vs 2.08, P < .0001). CONCLUSIONS: Over half of the patients referred to an otolaryngology practice from primary care for sinusitis did not receive a diagnosis of sinusitis from an otolaryngology practice. Further research should investigate implications for increased healthcare costs and inappropriate prescription trends associated with the management of sinusitis.

Data Gap in Sacral Neuromodulation Documentation: Call to Improve Documentation Protocols.

PURPOSE: We quantified patient record documentation of sacral neuromodulation (SNM) threshold testing and programming parameters at our institution to identify opportunities to improve therapy outcomes and future SNM technologies. METHODS: A retrospective review was conducted using 127 records from 40 SNM patients. Records were screened for SNM documentation including qualitative and quantitative data. The qualitative covered indirect references to threshold testing and the quantitative included efficacy descriptions and device programming used by the patient. Findings were categorized by visit type: percutaneous nerve evaluation (PNE), stage 1 (S1), permanent lead implantation, stage 2 (S2) permanent impulse generator implantation, device-related follow-up, or surgical removal. RESULTS: Documentation of threshold testing was more complete during initial implant visits (PNE and S1), less complete for S2 visits, and infrequent for follow-up clinical visits. Surgical motor thresholds were most often referred to using only qualitative comments such as "good response" (88%, 100% for PNE, S1) and less commonly included quantitative values (68%, 84%), locations of response (84%, 83%) or specific contacts used for testing (0%). S2 motor thresholds were less well documented with qualitative, quantitative, and anatomical location outcomes at 70%, 48%, and 36% respectively. Surgical notes did not include specific stimulation parameters or contacts used for tests. Postoperative sensory tests were often only qualitative (80%, 67% for PNE, S1) with quantitative values documented much less frequently (39%, 9%) and typically lacked sensory locations or electrode-specific results. For follow-up visits, <10% included quantitative sensory test outcomes. Few records (<7%) included device program settings recommended for therapy delivery and none included therapy-use logs. CONCLUSION: While evidence suggests contact and parameter-specific programming can improve SNM therapy outcomes, there is a major gap in the documentation of this data. More detailed testing and documentation could improve therapeutic options for parameter titration and provide design inputs for future technologies.

Artificial intelligence in spine care: current applications and future utility.

PURPOSE: The field of artificial intelligence is ever growing and the applications of machine learning in spine care are continuously advancing. Given the advent of the intelligence-based spine care model, understanding the evolution of computation as it applies to diagnosis, treatment, and adverse event prediction is of great importance. Therefore, the current review sought to synthesize findings from the literature at the interface of artificial intelligence and spine research. METHODS: A narrative review was performed based on the literature of three databases (MEDLINE, CINAHL, and Scopus) from January 2015 to March 2021 that examined historical and recent advancements in the understanding of artificial intelligence and machine learning in spine research. Studies were appraised for their role in, or description of, advancements within image recognition and predictive modeling for spinal research. Only English articles that fulfilled inclusion criteria were ultimately incorporated in this review. RESULTS: This review briefly summarizes the history and applications of artificial intelligence and machine learning in spine. Three basic machine learning training paradigms: supervised learning, unsupervised learning, and reinforced learning are also discussed. Artificial intelligence and machine learning have been utilized in almost every facet of spine ranging from localization and segmentation techniques in spinal imaging to pathology specific algorithms which include but not limited to; preoperative risk assessment of postoperative complications, screening algorithms for patients at risk of osteoporosis and clustering analysis to identify subgroups within adolescent idiopathic scoliosis. The future of artificial intelligence and machine learning in spine surgery is also discussed with focusing on novel algorithms, data collection techniques and increased utilization of automated systems. CONCLUSION: Improvements to modern-day computing and accessibility to various imaging modalities allow for innovative discoveries that may arise, for example, from management. Given the imminent future of AI in spine surgery, it is of great importance that practitioners continue to inform themselves regarding AI, its goals, use, and progression. In the future, it will be critical for the spine specialist to be able to discern the utility of novel AI research, particularly as it continues to pervade facets of everyday spine surgery.

Artificial intelligence and spine imaging: limitations, regulatory issues and future direction.

BACKGROUND: As big data and artificial intelligence (AI) in spine care, and medicine as a whole, continue to be at the forefront of research, careful consideration to the quality and techniques utilized is necessary. Predictive modeling, data science, and deep analytics have taken center stage. Within that space, AI and machine learning (ML) approaches toward the use of spine imaging have gathered considerable attention in the past decade. Although several benefits of such applications exist, limitations are also present and need to be considered. PURPOSE: The following narrative review presents the current status of AI, in particular, ML, with special regard to imaging studies, in the field of spinal research. METHODS: A multi-database assessment of the literature was conducted up to September 1, 2021, that addressed AI as it related to imaging of the spine. Articles written in English were selected and critically assessed. RESULTS: Overall, the review discussed the limitations, data quality and applications of ML models in the context of spine imaging. In particular, we addressed the data quality and ML algorithms in spine imaging research by describing preliminary results from a widely accessible imaging algorithm that is currently available for spine specialists to reference for information on severity of spine disease and degeneration which ultimately may alter clinical decision-making. In addition, awareness of the current, under-recognized regulation surrounding the execution of ML for spine imaging was raised. CONCLUSIONS: Recommendations were provided for conducting high-quality, standardized AI applications for spine imaging.