Choice of intraoperative ultrasound adjuncts for brain tumor surgery.

BACKGROUND: Gliomas are among the most typical brain tumors tackled by neurosurgeons. During navigation for surgery of glioma brain tumors, preoperatively acquired static images may not be accurate due to shifts. Surgeons use intraoperative imaging technologies (2-Dimensional and navigated 3-Dimensional ultrasound) to assess and guide resections. This paper aims to precisely capture the importance of preoperative parameters to decide which type of ultrasound to be used for a particular surgery. METHODS: This paper proposes two bagging algorithms considering base classifier logistic regression and random forest. These algorithms are trained on different subsets of the original data set. The goodness of fit of Logistic regression-based bagging algorithms is established using hypothesis testing. Furthermore, the performance measures for random-forest-based bagging algorithms used are AUC under ROC and AUC under the precision-recall curve. We also present a composite model without compromising the explainability of the models. RESULTS: These models were trained on the data of 350 patients who have undergone brain surgery from 2015 to 2020. The hypothesis test shows that a single parameter is sufficient instead of all three dimensions related to the tumor ([Formula: see text]). We observed that the choice of intraoperative ultrasound depends on the surgeon making a choice, and years of experience of the surgeon could be a surrogate for this dependence. CONCLUSION: This study suggests that neurosurgeons may not need to focus on a large set of preoperative parameters in order to decide on ultrasound. Moreover, it personalizes the use of a particular ultrasound option in surgery. This approach could potentially lead to better resource management and help healthcare institutions improve their decisions to make the surgery more effective.

Implications of use of different intraoperative ultrasound modalities during glioma surgery - A comparative study of factors affecting outcomes.

BACKGROUND: The main goal of glioma surgery is to remove the maximum amount of tumor without worsening the patient's neurological condition. Intraoperative ultrasound (US) imaging technologies (2D and 3D) are available to assist surgeons, providing real-time updates. Considering additional time, personnel, and cost, we investigate if comparable outcomes can be achieved using basic (2D) and advanced (3D) technology. OBJECTIVE: We propose predictive models for (i) glioma tumor resectability (ii) surgical outcome, and (iii) a model to predict the outcome of surgery aided with a particular ultrasound and compare outcomes between 2D and 3D US. METHODOLOGY: We used real-world surgery data from a tertiary cancer centre. Three groups of cases were analyzed (2D US used, 3D US used, and no US used during resection). The data analysis uses hypothesis testing, bootstrap sampling, and logistic regression. RESULTS: The preoperatively anticipated extent of tumor removal correlated with the postoperative MRI measurement of tumor removal for US-supported surgery (p=0.01) but not for no US-supported surgeries (p = 0.13). A combination of delineation, eloquence, and the multifocal/multicentric nature of the tumor effectively predicted resectability. The eventual outcome of surgery (actual extent of resection achieved) can be predicted by prior treatment status, delineation, eloquence, and satellite nodules. Based on our prediction model (training set of 350 cases and test of 40 cases of US-guided surgeries), we identify some cases where 3D US seems to offer superior EORs. CONCLUSION: The resectability of glioma tumors is crucial in determining surgical objectives, and the type of ultrasound used as support impacts tumor removal. The findings in this study aid informed decision-making and optimize imaging technology usage, providing a decision flow for selecting ultrasound based on tumor characteristics.

A Unified Citywide Dashboard for Allocation and Scheduling Dialysis for COVID-19 Patients on Maintenance Hemodialysis.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has caused significant global disruption, especially for chronic care like hemodialysis treatments. Approximately 10,000 end-stage kidney disease (ESKD) patients are receiving maintenance hemodialysis (MHD) at 174 dialysis centers in Greater Mumbai. Because of the fear of transmission of infection and inability to isolate patients in dialysis centers, chronic hemodialysis care was disrupted for COVID-19-infected patients. Hence, we embarked on a citywide initiative to ensure uninterrupted dialysis for these patients. Materials and Methods: The Municipal Corporation of Greater Mumbai (MCGM) designated 23 hemodialysis facilities as COVID-positive centers, two as COVID-suspect centers, and the rest continued as COVID-negative centers to avoid transmission of infection and continuation of chronic hemodialysis treatment. Nephrologists and engineers of the city developed a web-based-portal so that information about the availability of dialysis slots for COVID-infected patients was easily available in real time to all those providing care to chronic hemodialysis patients. Results: The portal became operational on May 20, 2020, and as of December 31, 2020, has enrolled 1,418 COVID-positive ESKD patients. This initiative has helped 97% of enrolled COVID-infected ESKD patients to secure a dialysis slot within 48 hours. The portal also tracked outcomes and as of December 31, 2020, 370 (27%) patients died, 960 patients recovered, and 88 patients still had an active infection. Conclusions: The portal aided the timely and smooth transfer of COVID-19-positive ESKD patients to designated facilities, thus averting mortality arising from delayed or denied dialysis. Additionally, the portal also documented the natural history of the COVID-19 pandemic in the city and provided information on the overall incidence and outcomes. This aided the city administration in the projected resource needs to handle the pandemic.