Characterizing Clinical Trials in Plastic and Reconstructive Surgery: A Systematic Review of ClinicalTrials.gov From 2007 to 2020.

BACKGROUND: Clinical trials form the backbone of evidence-based medicine. ClinicalTrials.gov is the world's largest clinical trial registry, and the state of clinical trials in plastic and reconstructive surgery (PRS) within that database has not been comprehensively studied. To that end, we explored the distribution of therapeutic areas that are under investigation, impact of funding on study design and data reporting, and trends in research patterns of all PRS interventional clinical trials registered with ClinicalTrials.gov. METHODS: Using the ClinicalTrials.gov database, we identified and extracted all clinical trials relevant to PRS that were submitted between 2007 and 2020. Studies were classified based on anatomic locations, therapeutic categories, and specialty topics. Cox proportional hazard was used to calculate adjusted hazard ratios (HRs) for early discontinuation and results reporting. RESULTS: A total of 3224 trials that included 372,095 participants were identified. The PRS trials grew at an annual rate of 7.9%. The therapeutic classes most represented were wound healing (41.3%) and cosmetics (18.1%). Funding for PRS clinical trials is largely provided through academic institutions (72.7%), while industry and US government constituted a minority. Industry-funded studies were more likely to be discontinued early than those funded by academics (HR, 1.89) or government (HR, 1.92) and to be nonblinded and nonrandomized. Academic-funded studies were the least likely to report results data within 3 years of trial completion (odds ratio, 0.87). CONCLUSIONS: A gulf exists in the representation of different PRS specialties among clinical trials. We highlight the role of funding source in trial design and data reporting to identify a potential source of financial waste and to stress the need for continued appropriate oversight.

Neurosurgical management of vertebral lesions in pediatric chronic recurrent multifocal osteomyelitis: patient series.

BACKGROUND: Chronic recurrent multifocal osteomyelitis (CRMO) is a rare pediatric autoinflammatory disorder involving 2 or more inflammatory bone lesions separated in time and space associated with pathological vertebral fractures. There are no current guidelines for the role of pediatric spine surgeons in the management of this condition. The authors demonstrate the importance of close and early involvement of neurosurgeons in caring for patients with CRMO with vertebral involvement. OBSERVATIONS: Fifty-six pediatric patients with clinical and radiographic evidence of CRMO were identified and clinical, radiographic, laboratory, and histopathological data were reviewed. All were evaluated via Jansson and Bristol CRMO diagnostic criteria. Ten had radiographic evidence of vertebral involvement (17.9%). Nine of these had multifocal disease. Five patients had multiple vertebrae affected. Six patients were evaluated for possible surgical intervention and one required intervention due to vertebra plana leading to a progressive kyphotic deformity and significant spinal canal stenosis. LESSONS: In conjunction with management by the primary pediatric rheumatology team using nonsteroidal anti-inflammatory drugs, disease-modifying anti-rheumatic drugs, immunotherapies, and bisphosphonates, given the risk of pathological fractures and potential resulting long-term neurological deficits, the authors recommend close monitoring and management by pediatric spine surgeons for any patient with CRMO with vertebral lesions.

Pituitary macroadenoma causing vision loss in Wyburn-Mason syndrome: illustrative case.

BACKGROUND: Wyburn-Mason syndrome (WMS) is a neurocutaneous disorder consisting of vascular malformations of the brain, eye, and skin. These include characteristically high-flow intracranial and intraorbital arteriovenous malformations (AVMs) that present commonly with visual deterioration, headache, and hemiplegia. Complete removal of these lesions is challenging. Most patients are followed closely, and intervention occurs only in the setting of worsening symptoms secondary to AVM growth or hemorrhage. Here the authors present the first known case of a patient with WMS and a pituitary macroadenoma. OBSERVATIONS: A 62-year-old man with a 30-year history of WMS with right basal ganglia and orbital AVMs and right eye blindness presented for new-onset left-sided vision loss. A pituitary adenoma was identified compressing the optic chiasm and left optic nerve. Magnetic resonance imaging and digital subtraction angiography studies were obtained for surgical planning, and the patient underwent an endoscopic transnasal transsphenoidal resection, with significant postoperative vision improvement. LESSONS: Given the variable presentation and poor characterization of this rare syndrome, patients with WMS presenting with new symptoms must undergo evaluation for growth and hemorrhage of known AVMs, as well as new lesions. Further, in patients undergoing intracranial surgery, extensive preoperative imaging and planning are crucial for safe and successful procedures.

Research reporting in cubital tunnel syndrome studies: an analysis of the literature.

PURPOSE: There is a strong need for a set of consensus outcomes to be utilized for future studies on cubital tunnel syndrome. The goal was to assess the outcome measures utilized in the cubital tunnel syndrome literature as a way of measuring popularity/acceptability and then to perform a literature review for the most commonly used outcomes. METHODS: A literature search was performed using the pubmed.gov database and Medical Subject Headings (MeSH). For each article, the following data were abstracted: study type, motor outcome(s), sensory outcome(s), composite outcome(s), patient-reported outcome (PRO) metric(s), pain outcome(s), psychological outcome(s), electrodiagnostic outcome(s), and any other outcomes that were used. RESULTS: A composite outcome was reported in 52/85 (61%) studies, with the modified Bishop score (27/85; 32%) most common. A motor outcome was reported in 44/85 (52%) studies, with dynamometry (38/85; 45%) most common. The majority of studies (55%) did not report a sensory outcome. The majority of studies (52%) did not report a PRO. A specific pain outcome was reported in the minority (23/85; 27%), with the visual analogue scale (VAS) (22/85; 26%) most common. Pre- and postoperative electrodiagnostic results were presented in 22/85 studies (26%). DISCUSSION: Understanding current clinical practice and historical outcomes reporting provides a foundation for discussion regarding the development of a core outcome set for cubital tunnel syndrome. We hope that the data provided in the current study will stoke a discussion that will culminate in a consensus statement for research reporting in cubital tunnel syndrome studies.

Machine learning approach to differentiation of peripheral schwannomas and neurofibromas: A multi-center study.

BACKGROUND: Non-invasive differentiation between schwannomas and neurofibromas is important for appropriate management, preoperative counseling, and surgical planning, but has proven difficult using conventional imaging. The objective of this study was to develop and evaluate machine learning approaches for differentiating peripheral schwannomas from neurofibromas. METHODS: We assembled a cohort of schwannomas and neurofibromas from 3 independent institutions and extracted high-dimensional radiomic features from gadolinium-enhanced, T1-weighted MRI using the PyRadiomics package on Quantitative Imaging Feature Pipeline. Age, sex, neurogenetic syndrome, spontaneous pain, and motor deficit were recorded. We evaluated the performance of 6 radiomics-based classifier models with and without clinical features and compared model performance against human expert evaluators. RESULTS: One hundred and seven schwannomas and 59 neurofibromas were included. The primary models included both clinical and imaging data. The accuracy of the human evaluators (0.765) did not significantly exceed the no-information rate (NIR), whereas the Support Vector Machine (0.929), Logistic Regression (0.929), and Random Forest (0.905) classifiers exceeded the NIR. Using the method of DeLong, the AUCs for the Logistic Regression (AUC = 0.923) and K Nearest Neighbor (AUC = 0.923) classifiers were significantly greater than the human evaluators (AUC = 0.766; p = 0.041). CONCLUSIONS: The radiomics-based classifiers developed here proved to be more accurate and had a higher AUC on the ROC curve than expert human evaluators. This demonstrates that radiomics using routine MRI sequences and clinical features can aid in differentiation of peripheral schwannomas and neurofibromas.

Machine-Learning Approach to Differentiation of Benign and Malignant Peripheral Nerve Sheath Tumors: A Multicenter Study.

BACKGROUND: Clinicoradiologic differentiation between benign and malignant peripheral nerve sheath tumors (PNSTs) has important management implications. OBJECTIVE: To develop and evaluate machine-learning approaches to differentiate benign from malignant PNSTs. METHODS: We identified PNSTs treated at 3 institutions and extracted high-dimensional radiomics features from gadolinium-enhanced, T1-weighted magnetic resonance imaging (MRI) sequences. Training and test sets were selected randomly in a 70:30 ratio. A total of 900 image features were automatically extracted using the PyRadiomics package from Quantitative Imaging Feature Pipeline. Clinical data including age, sex, neurogenetic syndrome presence, spontaneous pain, and motor deficit were also incorporated. Features were selected using sparse regression analysis and retained features were further refined by gradient boost modeling to optimize the area under the curve (AUC) for diagnosis. We evaluated the performance of radiomics-based classifiers with and without clinical features and compared performance against human readers. RESULTS: A total of 95 malignant and 171 benign PNSTs were included. The final classifier model included 21 imaging and clinical features. Sensitivity, specificity, and AUC of 0.676, 0.882, and 0.845, respectively, were achieved on the test set. Using imaging and clinical features, human experts collectively achieved sensitivity, specificity, and AUC of 0.786, 0.431, and 0.624, respectively. The AUC of the classifier was statistically better than expert humans (P = .002). Expert humans were not statistically better than the no-information rate, whereas the classifier was (P = .001). CONCLUSION: Radiomics-based machine learning using routine MRI sequences and clinical features can aid in evaluation of PNSTs. Further improvement may be achieved by incorporating additional imaging sequences and clinical variables into future models.

Imaging of Damaged Nerves.

Nerve imaging is an important component in the assessment of patients presenting with suspected peripheral nerve pathology. Although magnetic resonance neurography and ultrasound are the most commonly utilized techniques, several promising new modalities are on the horizon. Nerve imaging is useful in localizing the nerve injury, determining the severity, providing prognostic information, helping establish the diagnosis, and helping guide surgical decision making. The focus of this article is imaging of damaged nerves, focusing on nerve injuries and entrapment neuropathies.