Background: Concerns exist regarding exacerbation of existing disparities in health care access with the rapid implementation of telemedicine during the coronavirus disease 2019 (COVID-19) pandemic. However, data on pre-existing disparities in telemedicine utilization is currently lacking. Objective: We aimed to study: (1) the prevalence of outpatient telemedicine visits before the COVID-19 pandemic by patient subgroups based on age, comorbidity burden, residence rurality, and median household income; and (2) associated diagnosis categories. Research Design: This was a retrospective cohort study. Subject: Commercial claims data from the Truven MarketScan database (2014−2018) representing n=846,461,609 outpatient visits. Measures: We studied characteristics and utilization of outpatient telemedicine services before the COVID-19 pandemic by patient subgroups based on age, comorbidity burden, residence rurality, and median household income. Disparities were assessed in unadjusted and adjusted (regression) analyses. Results: With overall telemedicine uptake of 0.12% (n=1,018,092/846,461,609 outpatient visits) we found that pre-COVID-19 disparities in telemedicine use became more pronounced over time with lower use in patients who were older, had more comorbidities, were in rural areas, and had lower median household incomes (all trends and effect estimates P <0.001). Conclusion: These results contextualize pre-existing disparities in telemedicine use and are crucial in the monitoring of potential disparities in telemedicine access and subsequent outcomes after the rapid expansion of telemedicine during the COVID-19 pandemic.

Purpose The present study aims to conduct a systematic review of literature reporting on the dose and dosing schedule of dexamethasone (DXM) in relation to clinical outcomes in malignant brain tumor patients, with particular attention to evidence-based practice. Methods A systematic search was performed in PubMed, Embase, Web of Science, Cochrane, Academic Search Premier, and PsycINFO to identify studies that reported edema volume reduction, symptomatic relief, adverse events and survival in relation to dexamethasone dose in glioma or brain metastasis (BM) patients. Results After screening 1812 studies, fifteen articles were included for qualitative review. Most studies reported a dose of 16 mg, mostly in a schedule of 4 mg four times a day. Due to heterogeneity of studies, it was not possible to perform quantitative meta-analysis. For BMs, best available evidence suggests that higher doses of DXM may give more adverse events, but may not necessarily result in better clinical condition. Some studies suggest that higher DXM doses are associated with shorter survival in the palliative setting. For glioma, DXM may lead to symptomatic improvement, yet no studies directly compare different doses. Results regarding edema reduction and survival in glioma patients are conflicting. Conclusions Evidence on the safety and efficacy of different DXM doses in malignant brain tumor patients is scarce and conflicting. Best available evidence suggests that low DXM doses may be noninferior to higher doses in certain circumstances, but more comparative research in this area is direly needed, especially in light of the increasing importance of immunotherapy for brain tumors. Electronic supplementary material The online version of this article (10.1007/s11060-019-03238-4) contains supplementary material, which is available to authorized users.

Purpose To describe practice patterns and patient outcomes with respect to the use of postoperative systemic therapy (ST) after resection of a solitary breast cancer brain metastasis (BCBM). Methods A multi-institutional retrospective review of consecutive patients undergoing resection of a single BCBM without extracranial metastases was performed to describe subtype-specific postoperative outcomes and assess the impact of types of ST on site of recurrence, progression-free survival (PFS), and overall survival (OS). Results Forty-four patients were identified. Stratified estimated survival was 15, 24, and 23 months for patients with triple negative, estrogen receptor positive (ER+), and HER2+ BCBMs, respectively. Patients receiving postoperative ST had a longer median PFS (8 versus 4 months, adjusted p-value 0.01) and OS (32 versus 15 months, adjusted p-value 0.21). Nine patients (20%) had extracranial progression, 23 (52%) had intracranial progression, three (8%) had both, and nine (20%) did not experience progression at last follow-up. Multivariate analysis showed that postoperative hormonal therapy was associated with longer OS (HR 0.26; 95% CI 0.08-0.89; p = 0.03) but not PFS (HR 0.35, 95% CI 0.08-1.47, p = 0.15) in ER+ patients. Postoperative HER2-targeted therapy was not associated with longer OS or PFS in HER2+ patients. Conclusions Disease progression occurred intracranially more often than extracranially following resection of a solitary BCBM. In ER+ patients, postoperative hormonal therapy was associated with longer OS. Postoperative HER2-targeted therapy did not show survival benefit in HER2+ patients. These results should be validated in larger cohorts.

BACKGROUND In patients with locally recurrent brain metastases (LRBMs), the role of (repeat) craniotomy is controversial. This study aimed to analyze long-term oncological outcomes in this heterogeneous population. METHODS Craniotomies for LRBM were identified from a tertiary neuro-oncological institution. First, we assessed overall survival (OS) and intracranial control (ICC) stratified by molecular profile, prognostic indices, and multimodality treatment. Second, we compared LRBMs to propensity score-matched patients who underwent craniotomy for newly diagnosed brain metastases (NDBM). RESULTS Across 180 patients, median survival after LRBM resection was 13.8 months and varied by molecular profile, with &gt;24 months survival in ALK/EGFR+ lung adenocarcinoma and HER2+ breast cancer. Furthermore, 102 patients (56.7%) experienced intracranial recurrence; median time to recurrence was 5.6 months. Compared to NDBMs (n = 898), LRBM patients were younger, more likely to harbor a targetable mutation and less likely to receive adjuvant radiation (p &lt; 0.05). After 1:3 propensity matching stratified by molecular profile, LRBM patients generally experienced shorter OS (hazard ratio 1.67 and 1.36 for patients with or without a mutation, p &lt; 0.05) but similar ICC (hazard ratio 1.11 in both groups, p &gt; 0.20) compared to NDBM patients with similar baseline. Results across specific molecular subgroups suggested comparable effect directions of varying sizes. CONCLUSIONS In our data, patients with LRBMs undergoing craniotomy comprised a subgroup of brain metastasis patients with relatively favorable clinical characteristics and good survival outcomes. Recurrent status predicted shorter OS but did not impact ICC. Craniotomy could be considered in selected, prognostically favorable patients.

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BACKGROUND:The merit-based incentive payment system (MIPS) program was implemented to tie Medicare reimbursements to value-based care measures. Neurosurgical performance in MIPS has not yet been described. OBJECTIVE: To characterize neurosurgical performance in the first 2 years of MIPS. METHODS: Publicly available data regarding MIPS performance for neurosurgeons in 2017 and 2018 were queried. Descriptive statistics about physician characteristics, MIPS performance, and ensuing payment adjustments were performed, and predictors of bonus payments were identified. RESULTS: There were 2811 physicians included in 2017 and 3147 in 2018. Median total MIPS scores (99.1 vs 90.4, P < .001) and quality scores (97.9 vs 88.5, P < .001) were higher in 2018 than in 2017. More neurosurgeons (2758, 87.6%) received bonus payments in 2018 than in 2017 (2013, 71.6%). Of the 2232 neurosurgeons with scores in both years, 1347 (60.4%) improved their score. Reporting through an alternative payment model (odds ratio [OR]: 32.3, 95% CI: 16.0-65.4; P < .001) and any practice size larger than 10 (ORs ranging from 2.37 to 10.2, all P < .001) were associated with receiving bonus payments. Increasing years in practice (OR: 0.99; 95% CI: 0.982-0.998, P = .011) and having 25% to 49% (OR: 0.72; 95% CI: 0.53-0.97; P = .029) or ≥50% (OR: 0.48; 95% CI: 0.28-0.82; P = .007) of a physician's patients eligible for Medicaid were associated with lower rates of bonus payments. CONCLUSION: Neurosurgeons performed well in MIPS in 2017 and 2018, although the program may be biased against surgeons who practice in small groups or take care of socially disadvantaged patients.

PURPOSE The aim of this study was to develop an open-source natural language processing (NLP) pipeline for text mining of medical information from clinical reports. We also aimed to provide insight into why certain variables or reports are more suitable for clinical text mining than others. MATERIALS AND METHODS Various NLP models were developed to extract 15 radiologic characteristics from free-text radiology reports for patients with glioblastoma. Ten-fold cross-validation was used to optimize the hyperparameter settings and estimate model performance. We examined how model performance was associated with quantitative attributes of the radiologic characteristics and reports. RESULTS In total, 562 unique brain magnetic resonance imaging reports were retrieved. NLP extracted 15 radiologic characteristics with high to excellent discrimination (area under the curve, 0.82 to 0.98) and accuracy (78.6% to 96.6%). Model performance was correlated with the inter-rater agreement of the manually provided labels (ρ = 0.904; P < .001) but not with the frequency distribution of the variables of interest (ρ = 0.179; P = .52). All variables labeled with a near perfect inter-rater agreement were classified with excellent performance (area under the curve > 0.95). Excellent performance could be achieved for variables with only 50 to 100 observations in the minority group and class imbalances up to a 9:1 ratio. Report-level classification accuracy was not associated with the number of words or the vocabulary size in the distinct text documents. CONCLUSION This study provides an open-source NLP pipeline that allows for text mining of narratively written clinical reports. Small sample sizes and class imbalance should not be considered as absolute contraindications for text mining in clinical research. However, future studies should report measures of inter-rater agreement whenever ground truth is based on a consensus label and use this measure to identify clinical variables eligible for text mining.

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