Citations

Dilated perivascular spaces (PVSs) are common and easily recognized on imaging. However, rarer giant tumefactive PVSs (GTPVSs) can have unusual multilocular cystic configurations, and are often confused for other pathologic entities, including neoplasms, cystic infarctions, and neuroepithelial cysts. Because GTPVSs are scarcely encountered and even more infrequently operated upon, many radiologists are unaware of the imaging and pathologic features of these lesions. Here, a case of a resected GTPVS is presented, highlighting both its radiologic and histologic characteristics, and discussing how such lesions can be differentiated from their closest mimickers on imaging.

OBJECTIVE Chiari malformation type I (CM-I) is a congenital disorder occurring in 0.1% of the population. In symptomatic cases, surgery with posterior fossa decompression (PFD) is the treatment of choice. Surgery is, however, associated with peri- and postoperative complications that may require readmission or renewed surgical intervention. Given the associated financial costs and the impact on patients’ well-being, there is a need for predictive tools that can assess the likelihood of such adverse events. The aim of this study was therefore to leverage machine learning algorithms to develop a predictive model for 30-day readmissions and reoperations after PFD in pediatric patients with CM-I. METHODS This was a retrospective study based on data from the National Surgical Quality Improvement Program–Pediatric database. Eligible patients were those undergoing PFD (Current Procedural Terminology code 61343) for CM-I between 2012 and 2021. Patients undergoing surgery for tumors or vascular lesions were excluded. Unplanned 30-day readmission and unplanned 30-day reoperation were the main study outcomes. Additional outcome data considered included the length of hospital stay, 30-day complications, discharge disposition, and 30-day mortality. Training and testing samples were randomly generated (80:20) to study the 30-day readmission and reoperation using logistic regression, decision tree, random forest (RF), K-nearest neighbors, and Gaussian naive Bayes algorithms. RESULTS A total of 7106 pediatric patients undergoing PFD were included. The median age was 9.2 years (IQR 4.7, 14.2 years). Most of the patients were female (56%). The 30-day readmission and reoperation rates were 7.5% and 3.4%, respectively. Headaches (32%) and wound-related complications (30%) were the most common reasons for 30-day readmission, while wound revisions and evacuation of fluid or blood (62%), followed by CSF diversion-related procedures (28%), were the most common reasons for 30-day reoperation. RF classifiers had the highest predictive accuracy for both 30-day readmissions (area under the curve [AUC] 0.960) and reoperations (AUC 0.990) compared with the other models. On feature importance analysis, sex, developmental delay, ethnicity, respiratory disease, premature birth, hydrocephalus, and congenital/genetic anomaly were some of the variables contributing the most to both RF models. CONCLUSIONS Using a large-scale nationwide dataset, machine learning models for the prediction of both 30-day readmissions and reoperations were developed and achieved high accuracy. This highlights the utility of machine learning in risk stratification and surgical decision-making for pediatric CM-I.

OBJECTIVE Unplanned returns to the operating room (RORs) constitute an important quality metric in surgical practice. In this study, the authors present a methodology to compare a department’s unplanned ROR rates with national benchmarks in the context of large-scale quality of care surveillance. METHODS The authors identified unplanned RORs within 30 days from the initial surgery at their institution during the period 2014–2018 using an institutional documentation platform that facilitates the collection of reoperation information by providers in the clinical setting. They divided the procedures into 28 groups by Current Procedural Terminology and International Classification of Diseases, 9th and 10th Revision codes. They estimated national benchmarks of unplanned RORs for these procedure groups via querying the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) registry during the period 2014–2018. Finally, they numerically assessed the unplanned ROR rates at their institution compared with those calculated from the ACS NSQIP registry. RESULTS Using the above methodology, the authors were able to classify 12,575 of the cases performed in their department during the period of interest, including 6037 (48%) cranial cases and 6538 (52%) spinal or peripheral nerve cases. Among those, 161 (1.3%) presented with complications that required an unplanned ROR within 30 days from the initial surgery. The respective cumulative unplanned ROR rate in the ACS NSQIP registry during the same timeframe was 3.6%. Among 15 categories of cranial procedures, the cumulative unplanned ROR rate was 1.3% in the authors’ department and 5.6% in the ACS NSQIP registry. Among 13 categories of spinal and peripheral nerve procedures, the cumulative unplanned ROR rate was 1.3% in the authors’ department and 2.8% in the ACS NSQIP registry. Unplanned ROR rates at the authors’ institution were lower than the national average for each of the 28 procedure groups of interest. Yearly analysis of institutional ROR rates for the five most commonly performed procedures showed lower reoperation rates compared with the national benchmarks. CONCLUSIONS Using an institutional documentation tool and a widely available national database, the authors developed a reproducible and standardized method of comparing their department’s outcomes with national benchmarks per procedure subgroup. This methodology accommodates longitudinal quality surveillance across the different subspecialties in a neurosurgical department and may illuminate potential shortcomings of care delivery in the future.

Summary Histiocytic neoplasms are diverse clonal haematopoietic disorders, and clinical disease is mediated by tumorous infiltration as well as uncontrolled systemic inflammation. Individual subtypes include Langerhans cell histiocytosis (LCH), Rosai–Dorfman–Destombes disease (RDD) and Erdheim–Chester disease (ECD), and these have been characterized with respect to clinical phenotypes, driver mutations and treatment paradigms. Less is known about patients with mixed histiocytic neoplasms (MXH), that is two or more coexisting disorders. This international collaboration examined patients with biopsy‐proven MXH with respect to component disease subtypes, oncogenic driver mutations and responses to conventional (chemotherapeutic or immunosuppressive) versus targeted (BRAF or MEK inhibitor) therapies. Twenty‐seven patients were studied with ECD/LCH (19/27), ECD/RDD (6/27), RDD/LCH (1/27) and ECD/RDD/LCH (1/27). Mutations previously undescribed in MXH were identified, including KRAS, MAP2K2, MAPK3, non‐V600‐BRAF, RAF1 and a BICD2‐BRAF fusion. A repeated‐measure generalized estimating equation demonstrated that targeted treatment was statistically significantly (1) more likely to result in a complete response (CR), partial response (PR) or stable disease (SD) (odds ratio [OR]: 17.34, 95% CI: 2.19–137.00, p = 0.007), and (2) less likely to result in progression (OR: 0.08, 95% CI: 0.03–0.23, p < 0.0001). Histiocytic neoplasms represent an entity with underappreciated clinical and molecular diversity, poor responsiveness to conventional therapy and exquisite sensitivity to targeted therapy.

Abstract Objectives Lumbar spine surgery is a common procedure for treating disabling spine‐related pain. In recent decades, both the number and cost of spine surgeries have increased despite technological advances and modification in surgical technique. For those patients that have continued uncontrolled back and/or lower extremity pain following lumbar spine surgery, spinal cord stimulation (SCS) has emerged as a viable treatment option. However, the impact of lumbar spine surgical history remains largely unstudied. Specifically, the current study considers the impact of number of prior lumbar spine surgeries on pain relief outcomes following SCS implantation. Materials and Methods We queried the electronic medical record of five separate pain practices for all patients who have undergone a SCS implant between January 1, 2017, and March 1, 2020. Inclusion criteria consisted of any patients with an SCS implant who underwent a prior lumbar spine surgery. The primary outcome was the mean calculated percentage pain relief in patients based on number of prior lumbar spine surgeries. Results There was a total of 1974 total SCS implant cases identified across five separate pain clinics. There was no difference in mean calculated pain relief in patients with one prior spine surgery versus those with two or more prior spine surgeries (28.2% vs. 25.8%, adjusted β‐coefficient –3.1, 95% CI –8.9 to 2.7, p = 0.290). Similarly, when analyzing number of spine surgeries as a continuous variable, there was no association between number of spine surgeries and calculated pain relief (adjusted β‐coefficient –1.5, 95% CI –4.0 to 1.1, p = 0.257). Additionally, after patients were stratified based on waveform, there was no association between number of prior lumbar spine surgeries (analyzed both as a categorical and continuous variable) and calculated percentage pain relief. Conclusions This multicentered retrospective study found that there was no significant difference in pain scores in individuals who received SCS following one or more lumbar spine surgeries. Additionally, the waveform of the SCS device had no statistically significant impact on post‐operative pain scores following one or more lumbar spine surgeries.

Purpose To use Google trends to explore differences in public interest among types of anterior cruciate ligament (ACL) autografts, specifically quadriceps tendon, patellar tendon, and hamstring tendon autografts, between 2008 and 2019. Methods Data were obtained by querying Google Trends for key terms and phrases for online search data ranging from January 2008 to December 2019. Relative search volumes were created based on searches related to ACL reconstruction with comparative analysis generated for search terms related to quadriceps ACL, patellar tendon ACL, and hamstring ACL autografts. Statistical analysis included linear regression analysis, comparison of quarterly search volume trends over time, and comparison of cumulative annual search volumes for 2008 versus 2019. Results Linear models for respective search terms were statistically significant for the quadriceps ( P < .001) and patellar ( P = .007) tendon autograft groups but not the hamstring group ( P = .129). The quadriceps autograft group demonstrated a 12‐year search volume trend change of 0.56, which was significantly greater than the hamstring (0.07; P < .001) and patellar tendon (0.168; P < .001) groups. There was no significant difference in the trend change between hamstring and patellar tendon groups ( P = .20). Percent change in cumulative relative annual search volumes between 2008 and 2019 was 112% for the quadriceps tendon group, 12.9% for the hamstring group, and 18.6% for the patellar tendon group. Conclusions This study indicates a consistently increasing public interest in quadriceps tendon autograft for ACL reconstruction. The quadriceps autograft group demonstrated a significantly greater 12‐year online search volume, greater linear correlation, and larger percent change between 2008 and 2019 compared with patellar tendon or hamstring autograft groups. Clinical Relevance Awareness of patient perceptions has value in informing shared decision‐making, aligning patient expectations, and guiding areas of future research. Each of these has an impact on patient care. Being aware of patient interest and expectations is particularly important in areas with controversial or emerging research.

Objectives Compare the risk of extended major adverse cardiovascular (CV) event (MACE) composite outcomes and component events in patients with rheumatoid arthritis (RA) treated with tofacitinib versus tumour necrosis factor inhibitors (TNFi) in Oral Rheumatoid Arthritis Trial (ORAL) Surveillance. Methods Patients with RA aged ≥50 years and with ≥1 additional CV risk factor received tofacitinib 5 mg or 10 mg two times per day or TNFi. MACE (non-fatal myocardial infarction (MI), non-fatal stroke or CV death (MACE-3)) was extended by sequential addition of CV events (hospitalisation for unstable angina (MACE-4), coronary revascularisation (MACE-5), transient ischaemic attack (MACE-6), peripheral vascular disease (MACE-7)), heart failure (HF) hospitalisation (MACE-8) and venous thromboembolism (VTE; (MACE-8 plus VTE)). HRs (tofacitinib vs TNFi) were evaluated for MACE and individual components. Results HRs for MACE-4 to MACE-8 with combined and individual tofacitinib doses versus TNFi were similar. Risk of MACE-8 plus VTE appeared similar with tofacitinib 5 mg two times per day versus TNFi (HR 1.12 (0.82 to 1.52)), but higher with tofacitinib 10 mg two times per day versus TNFi (HR 1.38 (1.02 to 1.85)). Risk of MI was higher with tofacitinib versus TNFi, but difference in risk of other individual CV events was not suggested. Across extended MACE definitions, risk appeared higher with tofacitinib versus TNFi in those with atherosclerotic CV disease or age ≥65 years. Conclusion In ORAL Surveillance, risk of composite CV endpoints combining all ischaemic CV events and HF did not appear different with tofacitinib versus TNFi. The totality of CV risk was higher with tofacitinib 10 mg two times per day versus TNFi, driven by an increase in VTE. Trial registration number NCT02092467.

PURPOSE Patients diagnosed with advanced-stage Hodgkin lymphoma (aHL) have historically been risk-stratified using the International Prognostic Score (IPS). This study investigated if a machine learning (ML) approach could outperform existing models when it comes to predicting overall survival (OS) and progression-free survival (PFS). PATIENTS AND METHODS This study used patient data from the Danish National Lymphoma Register for model development (development cohort). The ML model was developed using stacking, which combines several predictive survival models (Cox proportional hazard, flexible parametric model, IPS, principal component, penalized regression) into a single model, and was compared with two versions of IPS (IPS-3 and IPS-7) and the newly developed aHL international prognostic index (A-HIPI). Internal model validation was performed using nested cross-validation, and external validation was performed using patient data from the Swedish Lymphoma Register and Cancer Registry of Norway (validation cohort). RESULTS In total, 707 and 760 patients with aHL were included in the development and validation cohorts, respectively. Examining model performance for OS in the development cohort, the concordance index (C-index) for the ML model, IPS-7, IPS-3, and A-HIPI was found to be 0.789, 0.608, 0.650, and 0.768, respectively. The corresponding estimates in the validation cohort were 0.749, 0.700, 0.663, and 0.741. For PFS, the ML model achieved the highest C-index in both cohorts (0.665 in the development cohort and 0.691 in the validation cohort). The time-varying AUCs for both the ML model and the A-HIPI were consistently higher in both cohorts compared with the IPS models within the first 5 years after diagnosis. CONCLUSION The new prognostic model for aHL on the basis of ML techniques demonstrated a substantial improvement compared with the IPS models, but yielded a limited improvement in predictive performance compared with the A-HIPI.