Dalton, Tara

Therapeutic clinical trial enrollment does not match glioma incidence across demographics. Traditional statistical methods have identified independent predictors of trial enrollment; however, our understanding of the interactions between these factors remains limited. To test the interactive effects of demographic, socioeconomic, and oncologic variables on trial enrollment, we designed boosted neural networks (BNNs) for all glioma patients ( n = 1042), women ( n = 445, 42.7%), and minorities ( n = 151, 14.5%) and externally validated these models [whole cohort, n = 230; women, n = 89 (38.7%); minority, n = 66 (28.7%)]. For the whole-cohort BNN, the most influential variables on enrollment were oncologic variables, including KPS [total effect (TE), 0.327], chemotherapy (TE, 0.326), tumor location (TE, 0.322), and seizures (TE, 0.239). The women-only BNN exhibited a similar trend. Conversely, for the minority-only BNN, socioeconomic variables [insurance status (TE, 0.213), occupation classification (TE, 0.204), and employment status (TE, 0.150)] were most influential. These results may help prioritize patient-specific initiatives to increase accrual.

Abstract BACKGROUND Despite changes in national policy, women and minority patients remain under-accrued in glioma clinical trials. Supervised machine learning may serve as a novel approach to understanding which variables and their complex interplay most influence enrollment for underrepresented patients. We aimed to design and externally validate machine learning models to predict therapeutic clinical trial enrollment for a) all patients, b) women, and c) NIH-designated minority patients with low- and high-grade glioma. METHODS In a 20-year retrospective cohort of 1042 adult patients with low- and high-grade glioma, boosted neural network (BNN) models for the whole, women, and minority cohorts were used to analyze the combined effects of demographic, socioeconomic, and oncologic variables on clinical trial enrollment. These models were validated using an external cohort (n=103). RESULTS In the development cohort (445 [42.7%] women; 151 [14.5%] minorities; median age, 51.5 [interquartile range: 39.4-62.5] years), 350 patients (33.6%) enrolled in a therapeutic clinical trial. The whole cohort BNN (AUC: 0.827, misclassification rate (MR): 0.175), the women cohort BNN (AUC: 0.895, MR: 0.125), and the minority cohort BNN (AUC: 0.983, MR: 0.053) models accurately predicted trial enrollment on external validation. For the whole cohort and women models, several demographic (race, ethnicity, minority status, sex), socioeconomic (insurance, employment status), and oncologic variables (seizure, chemotherapy, KPS, WHO grade, tumor lobe) were equally influential (Main effect (ME): 1.000, Total effect (TE): 1.000) on enrollment. For the minority model, socioeconomic variables including insurance status (ME: 0.089, TE: 0.417) and occupation classification (ME: 0.087, TE: 0.381) drove enrollment. CONCLUSION We designed and externally validated the first machine learning model for predicting therapeutic clinical trial enrollment for patients with diffuse glioma. Model features driving trial enrollment varied for underrepresented patients. These results may help prioritize personalized patient-specific initiatives to address barriers to equitable patient accrual.

Study Design. Retrospective review of prospective, multicenter and international cohort study. Objective. To describe the effect of gender on HRQoL, clinical outcomes and survival for patients with spinal metastases treated with either surgery and/or radiation. Summary of Background Data. Gender differences in health-related outcomes are demonstrated in numerous studies, with women experiencing worse outcomes and receiving lower standards of care than men, however, the influence that gender has on low health-related quality of life (HRQoL) and clinical outcomes after spine surgery remains unclear. Methods. Patient demographic data, overall survival, treatment details, perioperative complications, and HRQoL measures including EQ-5D, pain NRS, the short form 36 version 2 (SF-36v2) and the Spine Oncology Study Group Outcomes Questionnaire (SOSGOQ2.0) were reviewed. Patients were stratified by sex, and a separate sensitivity analysis that excluded gender-specific cancers (i.e., breast, prostate, etc.) was performed. Results. The study cohort included 207 female and 183 male patients, with age, smoking status, and site of primary cancer being significantly different between the two cohorts (P<0.001). Both males and females experienced significantly improved SOSGOQ2.0, EQ-5D, and pain NRS scores at all study time points from baseline (P<0.001). Upon sensitivity analysis, (gender-specific cancers removed from analysis), the significant improvement in SOSGOQ physical, mental, and social subdomains and on SF-36 domains disappeared for females. Males experienced higher rates of postoperative complications. Kaplan-Meier survival analysis of both the overall and sensitivity analysis cohorts showed females lived longer than males after treatment (P=0.001 and 0.043, respectively). Conclusion. Both males and females experienced significantly improved HRQoL scores after treatment, but females demonstrated longer survival and a lower complication rate. This study suggests that gender may be a prognostic factor in survival and clinical outcomes for patients undergoing treatment for spine metastases and should be taken into consideration when counseling patients accordingly.

Abstract Background Few studies have assessed the impact of race on short-term patient outcomes in the brain metastasis population. The goal of this study is to evaluate the association of race with inpatient clinical presentation, treatment, in-hospital complications, and in-hospital mortality rates for patients with brain metastases (BM). Method Using data collected from the National Inpatient Sample between 2004 and 2014, we retrospectively identified adult patients with a primary diagnosis of BM. Outcomes included nonroutine discharge, prolonged length of stay (pLOS), in-hospital complications, and mortality. Results Minority (Black, Hispanic/other) patients were less likely to receive surgical intervention compared to White patients (odds ratio [OR] 0.70; 95% confidence interval [CI] 0.66–0.74, p &lt; 0.001; OR 0.88; 95% CI 0.84–0.93, p &lt; 0.001). Black patients were more likely to develop an in-hospital complication than White patients (OR 1.35, 95% CI 1.28–1.41, p &lt; 0.001). Additionally, minority patients were more likely to experience pLOS than White patients (OR 1.48; 95% CI 1.41–1.57, p &lt; 0.001; OR 1.34; 95% CI 1.27–1.42, p &lt; 0.001). Black patients were more likely to experience a nonroutine discharge (OR 1.25; 95% CI 1.19–1.31, p &lt; 0.001) and higher in-hospital mortality than White (OR 1.13; 95% CI 1.03–1.23, p = 0.008). Conclusion Our analysis demonstrated that race is associated with disparate short-term outcomes in patients with BM. More efforts are needed to address these disparities, provide equitable care, and allow for similar outcomes regardless of care.