CovidNeuroOnc: A UK multicenter, prospective cohort study of the impact of the COVID-19 pandemic on the neuro-oncology service

BackgroundThe COVID-19 pandemic has profoundly affected cancer services. Our objective was to determine the effect of the COVID-19 pandemic on decision making and the resulting outcomes for patients with newly diagnosed or recurrent intracranial tumors.MethodsWe performed a multicenter prospective study of all adult patients discussed in weekly neuro-oncology and skull base multidisciplinary team meetings who had a newly diagnosed or recurrent intracranial (excluding pituitary) tumor between 01 April and 31 May 2020. All patients had at least 30-day follow-up data. Descriptive statistical reporting was used.ResultsThere were 1357 referrals for newly diagnosed or recurrent intracranial tumors across 15 neuro-oncology centers. Of centers with all intracranial tumors, a change in initial management was reported in 8.6% of cases (n = 104/1210). Decisions to change the management plan reduced over time from a peak of 19% referrals at the start of the study to 0% by the end of the study period. Changes in management were reported in 16% (n = 75/466) of cases previously recommended for surgery and 28% of cases previously recommended for chemotherapy (n = 20/72). The reported SARS-CoV-2 infection rate was similar in surgical and non-surgical patients (2.6% vs. 2.4%, P > .9).ConclusionsDisruption to neuro-oncology services in the UK caused by the COVID-19 pandemic was most marked in the first month, affecting all diagnoses. Patients considered for chemotherapy were most affected. In those recommended surgical treatment this was successfully completed. Longer-term outcome data will evaluate oncological treatments received by these patients and overall survival

Outcomes following surgery in subgroups of comatose and very elderly patients with chronic subdural hematoma

Increasing age and lower pre-operative Glasgow coma score (GCS) are associated with worse outcome after surgery for chronic subdural haematoma (CSDH). Only few studies have quantified outcomes specific to the very elderly or comatose patients. We aim to examine surgical outcomes in these patient groups. We analysed data from a prospective multicentre cohort study, assessing the risk of recurrence, death, and unfavourable functional outcome of very elderly (≥ 90 years) patients and comatose (pre-operative GCS ≤ 8) patients following surgical treatment of CSDH. Seven hundred eighty-five patients were included in the study. Thirty-two (4.1%) patients had pre-operative GCS ≤ 8 and 70 (8.9%) patients were aged ≥ 90 years. A higher proportion of comatose patients had an unfavourable functional outcome (38.7 vs 21.7%; p = 0.03), although similar proportion of comatose (64.5%) and non-comatose patients (61.8%) functionally improved after surgery (p = 0.96). Compared to patients aged < 90 years, a higher proportion of patients aged ≥ 90 years had unfavourable functional outcome (41.2 vs 20.5%; p < 0.01), although approximately half had functional improvement following surgery. Mortality risk was higher in both comatose (6.3 vs 1.9%; p = 0.05) and very elderly (8.8 vs 1.1%; p < 0.01) groups. There was a trend towards a higher recurrence risk in the comatose group (19.4 vs 9.5%; p = 0.07). Surgery can still provide considerable benefit to very elderly and comatose patients despite their higher risk of morbidity and mortality. Further research would be needed to better identify those most likely to benefit from surgery in these groups

Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis

Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario. Objective: The aims were to describe how to:(i) develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and(ii) automatically identify the features that best distinguish the groups. Methods: The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described - simple or complex; presentation order - which type of picture was described first; and age). In this study, the descriptions by 144 of the subjects studied in Toledo18 were used,which included 200 healthy Brazilians of both genders. Results and Conclusion: A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS) is a strong candidate to replace manual feature selection methods