Turnover Time Between Elective Operative Cases: Does the Witching Hour Exist for the Operating Room?

BACKGROUND: Efficient resource management in the operating room (OR) contributes significantly to healthcare expenditure and revenue generation for health systems. We aim to assess the influence that surgeon, anesthesiology, and nursing team assignments and time of day have on turnover time (TOT) in the OR. METHODS: We performed a retrospective review of elective cases at a single academic hospital that were completed between Monday and Friday between the hours of 0700 and 2359 from July 1, 2017, through March 31, 2018. Emergent cases and unplanned, add-on cases were excluded. Data regarding patient characteristics, OR teams, TOT, and procedure start and end times were collected and analyzed. RESULTS: A total of 2174 total cases across 13 different specialties were included in our study. A multivariate regression of relevant variables affecting TOT was performed. Consecutive specialty (p < 0.0001), consecutive surgeon (p < 0.0001), anesthesiologist (p < 0.0001), and prior case ending before 1400 (p < 0.0001) were independent predictors of lower TOT. A receiver operating characteristic analysis demonstrated an area under the curve of 0.848 and a cutoff of 1400 having the highest sensitivity and specificity for TOT difference. CONCLUSIONS: TOT can be significantly affected by the time of the day the procedure is performed. Staffing availability during late procedures and the differences in how OR team staff are scheduled may affect OR efficiency. Additional studies may be needed to determine the long-term implications of changes implemented to decrease organizational operational costs related to the OR.

Consecutive Surgeon and Anesthesia Team Improve Turnover Time in the Operating Room.

Efficient management of the operating room (OR) contributes to much of today's healthcare expenditure and plays a critical role in generating revenue for most healthcare systems. Scheduling of OR cases with the same team and surgeon have been reported to improve turnover time between cases which in turn, improves efficiency and resource utilization. We aim to assess different operating room procedures within multiple subspecialties and explore the factors that positively and negatively influence turnover time (TOT) in the operating room. We conducted a retrospective review of cases that were completed on weekdays between 0600 and 2359 from July 2017 through March 2018. Cases between 0000 and 0559 were excluded from this study. Of the total 2,714 cases included in our study, transplant surgery had the highest mean TOT (71 ± 48 min) with orthopedic surgery cases without robots having the lowest mean TOT. OR cases in rooms with the same specialty had significantly less mean TOT compared to rooms switching between different subspecialties (70 vs. 117 min; p < 0.0001). Similarly, cases with the same surgeon and anesthesia team had a significant lower TOT (p < 0.0001). Consecutive specialty, surgeon, anesthesiologist, and prior procedure ending before 15:00 were all independent predictors of lower TOT (p < 0.0001). Our study shows scheduling cases with the same OR team for elective cases can decrease TOT and potentially increase operating room efficiency during the day. Further studies may be needed to assess the long-term effects of such variables affecting OR TOT on healthcare expenditure.

Emphasizing the Role of Neurosurgery Within Global Health and National Health Systems: A Call to Action.

Background: Worldwide, neurological disorders are the leading cause of disability-adjusted life years lost and the second leading cause of death. Despite global health capacity-building efforts, each year, 22.6 million individuals worldwide require neurosurgeon's care due to diseases such as traumatic brain injury and hydrocephalus, and 13.8 million of these individuals require surgery. It is clear that neurosurgical care is indispensable in both national and international public health discussions. This study highlights the role neurosurgeons can play in supporting the global health agenda, national surgical plans, and health strengthening systems (HSS) interventions. Methods: Guided by a literature review, the authors discuss key topics such as the global burden of neurosurgical diseases, the current state of neurosurgical care around the world and the inherent benefits of strong neurosurgical capability for health systems. Results: Neurosurgical diseases make up an important part of the global burden of diseases. Many neurosurgeons possess the sustained passion, resilience, and leadership needed to advocate for improved neurosurgical care worldwide. Neurosurgical care has been linked to 14 of the 17 Sustainable Development Goals (SDGs), thus highlighting the tremendous impact neurosurgeons can have upon HSS initiatives. Conclusion: We recommend policymakers and global health actors to: (i) increase the involvement of neurosurgeons within the global health dialogue; (ii) involve neurosurgeons in the national surgical system strengthening process; (iii) integrate neurosurgical care within the global surgery movement; and (iv) promote the training and education of neurosurgeons, especially those residing in Low-and middle-income countries, in the field of global public health.

Abnormal neurological pupil index is associated with malignant cerebral edema after mechanical thrombectomy in large vessel occlusion patients.

BACKGROUND AND OBJECTIVES: Malignant cerebral edema (MCE) is a feared complication in patients suffering from large vessel occlusion. Variables associated with the development of MCE have not been clearly elucidated. Use of pupillometry and the neurological pupil index (NPi) as an objective measure in patients undergoing mechanical thrombectomy (MT) has not been explored. We aim to evaluate variables significantly associated with MCE in patients that undergo MT and hypothesize that abnormal NPi is associated with MCE in this population. METHODS: A retrospective analysis of patients with acute ischemic stroke who had undergone MT at our institution between 2017 and 2020 was performed. Baseline and outcome variables were collected, including NPi values from pupillometry readings of patients within 72 h after the MT. Patients were divided into two groups: MCE versus non-MCE group. A univariate and multivariate analysis was performed. RESULTS: Of 284 acute ischemic stroke patients, 64 (22.5%) developed MCE. Mean admission glucose (137 vs. 173; p < 0.0001), NIHSS on admission (17 vs. 24; p < 0.01), infarct core volume (27.9 vs. 17.9 mL; p = 0.0036), TICI score (p = 0.001), and number of passes (2.9 vs. 1.8; p < 0.0001) were significantly different between the groups. Pupillometry data was present for 64 patients (22.5%). Upon multivariate analysis, abnormal ipsilateral NPi (OR 21.80 95% CI 3.32-286.4; p = 0.007) and hemorrhagic conversion were independently associated with MCE. CONCLUSION: Abnormal NPi and hemorrhagic conversion are significantly associated with MCE in patients following MT. Further investigation is warranted to better define an association between NPi and patient outcomes in this patient population.

A Semisupervised Learning Scheme with Self-Paced Learning for Classifying Breast Cancer Histopathological Images.

The unavailability of large amounts of well-labeled data poses a significant challenge in many medical imaging tasks. Even in the likelihood of having access to sufficient data, the process of accurately labeling the data is an arduous and time-consuming one, requiring expertise skills. Again, the issue of unbalanced data further compounds the abovementioned problems and presents a considerable challenge for many machine learning algorithms. In lieu of this, the ability to develop algorithms that can exploit large amounts of unlabeled data together with a small amount of labeled data, while demonstrating robustness to data imbalance, can offer promising prospects in building highly efficient classifiers. This work proposes a semisupervised learning method that integrates self-training and self-paced learning to generate and select pseudolabeled samples for classifying breast cancer histopathological images. A novel pseudolabel generation and selection algorithm is introduced in the learning scheme to generate and select highly confident pseudolabeled samples from both well-represented classes to less-represented classes. Such a learning approach improves the performance by jointly learning a model and optimizing the generation of pseudolabels on unlabeled-target data to augment the training data and retraining the model with the generated labels. A class balancing framework that normalizes the class-wise confidence scores is also proposed to prevent the model from ignoring samples from less represented classes (hard-to-learn samples), hence effectively handling the issue of data imbalance. Extensive experimental evaluation of the proposed method on the BreakHis dataset demonstrates the effectiveness of the proposed method.

Biomarkers for immunotherapy for treatment of glioblastoma.

Immunotherapy is a promising new therapeutic field that has demonstrated significant benefits in many solid-tumor malignancies, such as metastatic melanoma and non-small cell lung cancer. However, only a subset of these patients responds to treatment. Glioblastoma (GBM) is the most common malignant primary brain tumor with a poor prognosis of 14.6 months and few treatment advancements over the last 10 years. There are many clinical trials testing immune therapies in GBM, but patient responses in these studies have been highly variable and a definitive benefit has yet to be identified. Biomarkers are used to quantify normal physiology and physiological response to therapies. When extensively characterized and vigorously validated, they have the potential to delineate responders from non-responders for patients treated with immunotherapy in malignancies outside of the central nervous system (CNS) as well as GBM. Due to the challenges of current modalities of radiographic diagnosis and disease monitoring, identification of new predictive and prognostic biomarkers to gauge response to immune therapy for patients with GBM will be critical in the precise treatment of this highly heterogenous disease. This review will explore the current and future strategies for the identification of potential biomarkers in the field of immunotherapy for GBM, as well as highlight major challenges of adapting immune therapy for CNS malignancies.