RESUMEN
BACKGROUND: Posterior fossa irradiation with or without whole brain irradiation results in high doses of radiation to the thalamus, hippocampus, and putamen, structures critical to cognitive functioning. As a result, children with brain tumors treated with cranial irradiation (CRT) may experience significant cognitive late effects. We sought to determine the effect of radiation to those structures on neuropsychological outcome. METHODS: Forty-seven children with a history of posterior fossa tumor (17 treated with surgery; 11 with surgery and chemotherapy; and 19 with surgery, chemotherapy, and CRT) underwent neuroimaging and neuropsychological assessment at a mean of 4.8 years after treatment, along with 17 healthy sibling controls. The putamen, thalamus, and hippocampus were segmented on each participant's magnetic resonance imaging for diffusion indices and volumes, and in the radiation treatment group, radiation dose to each structure was calculated. RESULTS: Performance on visuoconstruction and spatial learning and memory was lower in patient groups than controls. Volume of the thalamus, when controlling for age, was smaller in the patient group treated with CRT than other groups. Higher radiation doses to the putamen correlated with higher fractional anisotropy in that structure. Higher radiation dose to the hippocampus correlated with lower spatial learning, and higher dose to thalami and putamina to lower verbal and nonverbal reasoning. CONCLUSIONS: All children with posterior fossa tumors, regardless of treatment modality, had cognitive deficits compared to their sibling controls. Posterior fossa irradiation may affect thalamic volume and aspects of verbal and nonverbal cognitive functioning.
Asunto(s)
Irradiación Craneana , Neoplasias Infratentoriales , Humanos , Niño , Masculino , Femenino , Neoplasias Infratentoriales/radioterapia , Neoplasias Infratentoriales/diagnóstico por imagen , Irradiación Craneana/efectos adversos , Adolescente , Tálamo/diagnóstico por imagen , Tálamo/patología , Pruebas Neuropsicológicas , Hipocampo/diagnóstico por imagen , Hipocampo/patología , Hipocampo/efectos de la radiación , Imagen por Resonancia Magnética , Putamen/diagnóstico por imagen , Relación Dosis-Respuesta en la Radiación , Encéfalo/diagnóstico por imagen , Encéfalo/fisiopatologíaRESUMEN
Background: The United States lacks equitable surgical access, prompting us to investigate whether there is an inverse relationship between Social Vulnerability Indices and the number of surgeons in a census tract, using the Inland Empire as a model. Methods: The Centers for Disease Control's (CDC) SVI 2018 database, composed of 823 census tracts, was compared against demographics of 1008 surgeons, from the American Medical Association's (AMA) 2018 Physician Masterfile. Analysis was performed via Spearman's bivariate and multiple regression. Results: An inverse relationship exists between surgeon number and overall social vulnerability (ρ = -0.266 [95 % CI -0.330 to -0.199], p < .001), and between surgeon number and each category of social vulnerability: Socioeconomic (ρ = -0.345 [95 % CI -0.0405 to -0.281], p < .001), Household Composition and Disability (ρ = -0.121 [95 % CI -0.190 to -0.051], p < .001), Minority Status and Language (ρ = -0.0317 [95 % CI -0.379 to -0.252], p < .001), and Housing Type and Transportation (ρ = -0.093 [95 % CI -0.153 to -0.023], p = .005). Multiple regression analysis revealed that the following were associated with a higher number of surgeons: higher "Per Capita Income" (B = 0.000151 [95 % CI 0.000079 to 0.000223], t(820) = 4.104, p < .001), larger Daytime Population (B = 0.000143 [95 % CI 0.000072 to 0.000214]; t(820) = 3.956, p < .001), larger Total Population (B = -0.013 [95 % CI -0.022 to -0.003]; t(820) = -2.672, p = .008), and smaller number of Persons aged 17 and younger (B = -0.005 [95 % CI -0.008 to -0.001]; t(820) = -2.794, p = .005). Conclusions: This study concludes that social vulnerability is predictive of, and significantly linked to, differences in surgical access and continues to advocate for research into understanding the surgeon's role in both individual and population health. Key message: Our work demonstrates that the number of surgeons in a census tract is inversely proportional to the census tract's overall Social Vulnerability Indices. Thus, this research can serve to educate the public, physicians, and other healthcare providers about the importance of incorporating social determinants of health into the construction of healthcare policy and practice, as well as the importance of continued funding for local and national social service programs as a means to alleviate specific health inequities, such as language and transportation.
RESUMEN
BACKGROUND: Accurately predicting which patients are most likely to benefit from massive transfusion protocol (MTP) activation may help patients while saving blood products and limiting cost. The purpose of this study is to explore the use of modern machine learning (ML) methods to develop and validate a model that can accurately predict the need for massive blood transfusion (MBT). METHODS: The institutional trauma registry was used to identify all trauma team activation cases between June 2015 and August 2019. We used an ML framework to explore multiple ML methods including logistic regression with forward and backward selection, logistic regression with lasso and ridge regularization, support vector machines (SVM), decision tree, random forest, naive Bayes, XGBoost, AdaBoost, and neural networks. Each model was then assessed using sensitivity, specificity, positive predictive value, and negative predictive value. Model performance was compared to that of existing scores including the Assessment of Blood Consumption (ABC) and the Revised Assessment of Bleeding and Transfusion (RABT). RESULTS: A total of 2438 patients were included in the study, with 4.9% receiving MBT. All models besides decision tree and SVM attained an area under the curve (AUC) of above 0.75 (range: 0.75-0.83). Most of the ML models have higher sensitivity (0.55-0.83) than the ABC and RABT score (0.36 and 0.55, respectively) while maintaining comparable specificity (0.75-0.81; ABC 0.80 and RABT 0.83). CONCLUSIONS: Our ML models performed better than existing scores. Implementing an ML model in mobile computing devices or electronic health record has the potential to improve the usability.