Cubital Tunnel Syndrome: Does a Consensus Exist for Diagnosis?

PURPOSE: Cubital tunnel syndrome (CuTS) is the second most common compressive neuropathy of the upper extremity. We aimed to determine a consensus among experts using the Delphi method for clinical criteria that could be validated further for the diagnosis of CuTS. METHODS: The Delphi method was used for establishing a consensus among a group of expert panelists, comprising 12 hand and upper-extremity surgeons, who ranked the diagnostic clinical importance of 55 items related to CuTS on a scale from 1 (least important) to 10 (most important). The average and SDs of each item were calculated, and Cronbach α was used to assess homogeneity among the panelist-ranked items. RESULTS: All panelists answered the 55-item questionnaire. A Cronbach α value of 0.963 was obtained on the first iteration. The top criteria that were considered most clinically relevant to the diagnosis of CuTS among the group were determined based on the most highly ranked and correlated items among the expert panelist group. The criteria based on which there was agreement were as follows: (1) paresthesias in ulnar nerve distribution, (2) symptoms precipitated by increased elbow flexion/positive elbow flexion tests, (3) positive Tinel sign at the medial elbow, (4) atrophy/weakness/ late findings (eg, claw hand of the ring/small finger and Wartenberg or Froment sign) of ulnar nerve-innervated muscles of the hand, (5) loss of two-point discrimination in ulnar nerve distribution, and (6) similar symptoms on the involved side after successful treatment on the contralateral side. CONCLUSIONS: Our study demonstrated a consensus among an expert panelist group of hand and upper-extremity surgeons on potential diagnostic criteria for CuTS. This consensus on diagnostic criteria may help clinicians readily diagnose CuTS in a standardized form; however, further weighting and validation are necessary prior to the development of a formal diagnostic scale. CLINICAL RELEVANCE: This study is the first step in producing a consensus on how to diagnose CuTS.

Trends and projections in surgical stabilization of glenohumeral instability in the United States from 2009 to 2030: rise of the Latarjet procedure and fall of open Bankart repair.

BACKGROUND: Advances in surgical techniques have improved the ability to address recurrent glenohumeral instability via arthroscopic capsulolabral repair and bone-restoring procedures such as the Latarjet procedure. Given the paucity of studies analyzing temporal trends in the surgical management of glenohumeral instability, the purpose of this study was to assess trends in the treatment of anterior, posterior, and multidirectional instability over a 10-year period and model projections to 2030. METHODS: Using the IBM Watson MarketScan national database, we identified all patients who underwent glenohumeral instability procedures from 2009 to 2018. Procedures were identified using Current Procedural Terminology codes for open Bankart, Latarjet, anterior bone block, posterior bone block, multidirectional capsular shift, and arthroscopic Bankart procedures. Sample weights provided by the database were used to calculate national estimates. US Census Bureau annual population data were used to calculate incidence. Future projections to 2030 were modeled using Poisson and linear regression. RESULTS: There were an estimated 446,072 glenohumeral instability cases from 2009 to 2018. The per capita incidence (per 100,000) remained constant, from 14.8 in 2009 to 14.5 in 2018. Arthroscopic Bankart procedures comprised the highest number of procedures throughout the study period, accounting for 89% of all procedures in 2009 and 93% in 2018. The number of open Bankart procedures decreased by 65% from 2009 to 2018, whereas the number of Latarjet procedures showed a 250% increase over the same period. Patient demographics did not change over the study period, and male patients aged 18-25 years comprised the largest demographic group undergoing anterior instability procedures. Multidirectional instability procedures exhibited the least pronounced sex differences. Future modeling from 2018 to 2030 projected a continued steady rise in arthroscopic Bankart procedures (from 40,000 to 49,000 cases/yr), rapid growth in Latarjet procedures (from 1370 to 4300 cases/yr), and continued decline in open Bankart procedures (from 1000 to 250 cases/yr). CONCLUSIONS: Arthroscopic Bankart repair continues to be the most common glenohumeral instability procedure in the United States. From 2009 to 2018, the incidence of open Bankart procedures declined whereas the incidence of Latarjet procedures markedly increased. Future projections to 2030 mirrored these findings. These data may provide an enhanced understanding of the evolution of surgical treatment of glenohumeral instability within the United States, laying the foundation for continued prospective studies into the appropriate indications and advancements in surgical techniques.

Winter sport musculoskeletal injuries: epidemiology and factors predicting hospital admission.

INTRODUCTION: Participation in winter sports such as skiing, snowboarding and snowmobiling is associated with risk of musculoskeletal injury. The purpose of our study was to describe and quantify emergency department encounters associated with these sports. METHODS: The National Electronic Injury Surveillance System (NEISS) was queried for skiing-, snowboarding- and snowmobiling-related injuries from 2009 to 2018. Patient demographics and disposition data were collected from emergency department encounters. Descriptive statistics were utilized to describe the trends in injuries from each sport and factors associated with the sports-specific injuries. RESULTS: From 2009 to 2018, there were an estimated 156,353 injuries related to snowboarding, skiing, or snowmobiling. Estimated injury incidence per 100,000 people decreased over time for skiing (3.24-1.23), snowboarding (3.98-1.22,) and snowmobiling (0.71-0.22,). The most common injury location by sport was shoulder for skiing (29.6%), wrist for snowboarding (32.5%) and shoulder for snowmobiling (21.9%), with fractures being the most common diagnosis. Only 4.5% required admission to the hospital. Fracture or dislocation was associated with highest likelihood of hospital admission (OR 42.34; 95% CI 22.59-79.37). Snowmobiling injuries (OR 1.63; 95% CI 1.20-2.22) and white race (OR 1.42; 95% CI 1.17-1.72) were also both associated with increased risk of hospital admission. CONCLUSIONS: Upper extremity injuries, particularly those involving fractures, were more common than lower extremity injuries for all three sports, with the shoulder being the most common location of injury for skiing and snowmobiling. This study can serve as the foundation for future research in sports safety and health policy to continue the declining trend of musculoskeletal injuries in the future. LEVEL OF EVIDENCE: III.

Validating Coding for the Identification of Pediatric Treatment Resistant Epilepsy Patients.

The International Classification of Diseases (ICD) system includes sub codes to indicate that an individual with epilepsy is treatment resistant. These codes would be a valuable tool to identify individuals for quality improvement and population health, as well as for recruitment into clinical trials. However, the accuracy of these codes is unclear. We performed a single center cross sectional study to understand the accuracy of ICD codes for treatment resistant epilepsy. We identified 344 individuals, roughly half with treatment resistant epilepsy The ICD code had a sensitivity of 90% (147 of 164) and specificity of 86% (155 of 180). The miscoding of children with refractory epilepsy was attributed to the following reasons: 5 patients had epilepsy surgery, 4 had absence epilepsy, 4 patients were seen by different providers, and 1 patient was most recently seen in movement disorders clinic. ICD codes accurately identify children with treatment resistant epilepsy.

Evaluation of an Activity Tracker to Detect Seizures Using Machine Learning.

Currently, the tracking of seizures is highly subjective, dependent on qualitative information provided by the patient and family instead of quantifiable seizure data. Usage of a seizure detection device to potentially detect seizure events in a population of epilepsy patients has been previously done. Therefore, we chose the Fitbit Charge 2 smart watch to determine if it could detect seizure events in patients when compared to continuous electroencephalographic (EEG) monitoring for those admitted to an epilepsy monitoring unit. A total of 40 patients were enrolled in the study that met the criteria between 2015 and 2016. All seizure types were recorded. Twelve patients had a total of 53 epileptic seizures. The patient-aggregated receiver operating characteristic curve had an area under the curve of 0.58 [0.56, 0.60], indicating that the neural network models were generally able to detect seizure events at an above-chance level. However, the overall low specificity implied a false alarm rate that would likely make the model unsuitable in practice. Overall, the use of the Fitbit Charge 2 activity tracker does not appear well suited in its current form to detect epileptic seizures in patients with seizure activity when compared to data recorded from the continuous EEG.

Measuring the impact of epilepsy on families.

OBJECTIVE: Caring for a child with illness or a child with disability impacts family in various ways. The ability to assess the impact of this care on families is one way to proactively provide the necessary support and resources for impacted families. Accordingly, the goal of the current study was to assess the impact of pediatric epilepsy on individual families in a comprehensive epilepsy clinic using a slightly modified version of the Impact on Families Scale (IFS). METHODS: Families of patients with epilepsy completed the IFS up to three times. The IFS score and the six categories (i.e., total impact, financial impact, general impact, family/social impact, coping, and sibling impact) were assessed using Student's two sample t-test to determine the differences between binary groups and Pearson's correlation to assess the associations with continuous variables. Linear regression modeling was used to develop a model to predict IFS score. RESULTS: Three hundred and forty-one patients completed the scale at one time point, 314 at two time points, and 61 at three time points. The overall impact of epilepsy on families was 109 (95% confidence interval (CI): 106-112) at time point 1, 111 (95% CI: 108-114) at time point 2, and 112 (95% CI: 105-119) at time point 3. There was no statistical difference in IFS score among the three time points. There were no associations with age or gender. Multivariable modeling using stepwise regression indicated that treatment resistance and seizure-free status were associated with IFS score. No interaction effects were identified. CONCLUSIONS: Findings from the current study suggest that the impact of epilepsy is highest for families that have children with active seizures at the time of their clinical visit and for those with children having treatment-resistant epilepsy. Although intuitive, this is the first study, to our knowledge, that has empirically verified these findings.