Streamlining brain tumor surgery care during the COVID-19 pandemic: A case-control study.

BACKGROUND: The COVID-19 pandemic forced a reconsideration of surgical patient management in the setting of scarce resources and risk of viral transmission. Herein we assess the impact of implementing a protocol of more rigorous patient education, recovery room assessment for non-ICU admission, earlier mobilization and post-discharge communication for patients undergoing brain tumor surgery. METHODS: A case-control retrospective review was undertaken at a community hospital with a dedicated neurosurgery and otolaryngology team using minimally invasive surgical techniques, total intravenous anesthesia (TIVA) and early post-operative imaging protocols. All patients undergoing craniotomy or endoscopic endonasal removal of a brain, skull base or pituitary tumor were included during two non-overlapping periods: March 2019-January 2020 (pre-pandemic epoch) versus March 2020-January 2021 (pandemic epoch with streamlined care protocol implemented). Data collection included demographics, preoperative American Society of Anesthesiologists (ASA) status, tumor pathology, and tumor resection and remission rates. Primary outcomes were ICU utilization and hospital length of stay (LOS). Secondary outcomes were complications, readmissions and reoperations. FINDINGS: Of 295 patients, 163 patients were treated pre-pandemic (58% women, mean age 53.2±16 years) and 132 were treated during the pandemic (52% women, mean age 52.3±17 years). From pre-pandemic to pandemic, ICU utilization decreased from 92(54%) to 43(29%) of operations (p<0.001) and hospital LOS≤1 day increased from 21(12.2%) to 60(41.4%), p<0.001, respectively. For craniotomy cohort, median LOS was 2 days for both epochs; median ICU LOS decreased from 1 to 0 days (p<0.001), ICU use decreased from 73(80%) to 29(33%),(p<0.001). For endonasal cohort, median LOS decreased from 2 to 1 days; median ICU LOS was 0 days for both epochs; (p<0.001). There were no differences pre-pandemic versus pandemic in ASA scores, resection/remission rates, readmissions or reoperations. CONCLUSION: This experience suggests the COVID-19 pandemic provided an opportunity for implementing a brain tumor care protocol to facilitate safely decreasing ICU utilization and accelerating discharge home without an increase in complications, readmission or reoperations. More rigorous patient education, recovery room assessment for non-ICU admission, earlier mobilization and post-discharge communication, layered upon a foundation of minimally invasive surgery, TIVA anesthesia and early post-operative imaging are possible contributors to these favorable trends.

Artificial Intelligence for Rapid Meta-Analysis: Case Study on Ocular Toxicity of Hydroxychloroquine.

BACKGROUND: Rapid access to evidence is crucial in times of an evolving clinical crisis. To that end, we propose a novel approach to answer clinical queries, termed rapid meta-analysis (RMA). Unlike traditional meta-analysis, RMA balances a quick time to production with reasonable data quality assurances, leveraging artificial intelligence (AI) to strike this balance. OBJECTIVE: We aimed to evaluate whether RMA can generate meaningful clinical insights, but crucially, in a much faster processing time than traditional meta-analysis, using a relevant, real-world example. METHODS: The development of our RMA approach was motivated by a currently relevant clinical question: is ocular toxicity and vision compromise a side effect of hydroxychloroquine therapy? At the time of designing this study, hydroxychloroquine was a leading candidate in the treatment of coronavirus disease (COVID-19). We then leveraged AI to pull and screen articles, automatically extract their results, review the studies, and analyze the data with standard statistical methods. RESULTS: By combining AI with human analysis in our RMA, we generated a meaningful, clinical result in less than 30 minutes. The RMA identified 11 studies considering ocular toxicity as a side effect of hydroxychloroquine and estimated the incidence to be 3.4% (95% CI 1.11%-9.96%). The heterogeneity across individual study findings was high, which should be taken into account in interpretation of the result. CONCLUSIONS: We demonstrate that a novel approach to meta-analysis using AI can generate meaningful clinical insights in a much shorter time period than traditional meta-analysis.