Ki-67/MIB-1 and Recurrence in Pituitary Adenoma

Objectives Ki-67/MIB-1 is a marker of cellular proliferation used as a pathological parameter in the clinical assessment of pituitary adenomas, where its expression has shown utility in predicting the invasiveness of these tumors. However, studies have shown variable results when using Ki-67/MIB-1 association with recurrence. The purpose of this study is to determine if a high Ki-67/MIB-1 labeling index (LI) is predictive of recurrence in pituitary adenomas. Methods A retrospective chart review was performed for patients undergoing pituitary adenoma resection with at least 1 year of follow-up. Additionally, systematic data searches were performed and included studies that correlated recurrence rate to Ki-67/MIB-1 LI. Our institutional data were included in a synthesis with previously published data. Results Our institutional review included 79 patients with a recurrence rate of 26.6%. We found that 8.8% of our patients had a high Ki-67/MIB-1 LI (>3%); however, high Ki-67/MIB-1 was not associated with recurrence. The systematic review identified 244 articles and 49 full-text articles that were assessed for eligibility. Quantitative analysis was performed on 30 articles including our institutional data and 18 studies reported recurrence by level of Ki-67/MIB-1 LI. Among studies that compared Ki-67/MIB-1 ≥3 vs. <3%, 10 studies reported odds ratios (OR) greater than 1 of which 6 were statistically significant. A high Ki-67/MIB-1 had higher odds of recurrence via the pooled odds ratio (OR = 4.15, 95% confidence interval [CI]: 2.31–7.42). Conclusion This systematic review suggests that a high Ki-67/MIB-1 should prompt an increased duration of follow-up due to the higher odds of recurrence of pituitary adenoma.

Volunteering Among Pre-clinical Medical Students: Study of Its Association With Academic Performance Using Institutional Data.

Background: Participating in volunteering activities during students’ higher education experience is becoming more commonplace. Studies have noted that volunteering has a positive impact on the academic performance of undergraduate medical students. However, the majority of these studies rely on self-reported data like surveys, interviews, and journals. In this study, we leverage actual institutional data to examining the relationship between volunteering and academic performance among medical students in the pre-clinical phases of the Bachelor of Medicine and Bachelor of Surgery (MBBS) program. The current study also explores the factors that might influence the studied relationship. Methods: A retrospective-longitudinal analysis was conducted in the College of Medicine at the Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU) in Dubai, United Arab Emirates. Three years of volunteering records for three cohorts of undergraduate medical students enrolled in the MBBS program between 2016 – 2018 were reviewed and analyzed to complete this study. In addition, the correlation between the Annual Grade Point Average (AGPA) and volunteering was studied, and the regression coefficients for the two variables were observed across the three cohorts in each study year. Results: Analysis of 153 undergraduate medical students' volunteering records revealed a significant positive relationship between the AGPA and the number of volunteering in year two. The correlation was insignificant in year one, year three, and across the three cohorts. Conclusions: The association between academic performance and volunteering among undergraduate medical students appeared to be positive. However, this relationship differs across the pre-clinical study years in medical school and is likely influenced by factors that impact students’ motivation towards volunteering. Therefore, having the volunteerism program embedded in curriculum planning and testing students’ personality traits related to volunteering motivation, are aspects to be explored to enhance medical students’ learning and professional development through volunteerism.

Infant hydrocephalus: what valve first?

Purpose To review the use of different valve types in infants with hydrocephalus, in doing so, determining whether an optimal valve choice exists for this patient cohort. Methods We conducted (1) a literature review for all studies describing valve types used (programmable vs. non-programmable, valve size, pressure) in infants (≤ 2 years) with hydrocephalus, (2) a review of data from the pivotal BASICS trial for infant patients and (3) a separate, institutional cohort study from Alder Hey Children’s Hospital NHS Foundation Trust. The primary outcome was any revision not due to infection. Results The search identified 19 studies that were included in the review. Most did not identify a superior valve choice between programmable and non-programmable, small compared to ultra-small, and differential pressure compared to flow-regulating valves. Five studies investigated a single-valve type without a comparator group. The BASICS data identified 391 infants, with no statistically significant difference between gravitational and programmable subgroups. The institutional data from our tertiary referral centre did not reveal any significant difference in failure rate between valve subtypes. Conclusion Our review highlights the challenges of valve selection in infant hydrocephalus, reiterating that the concept of an optimal valve choice in this group remains a controversial one. While the infant-hydrocephalic population is at high risk of valve failure, heterogeneity and a lack of direct comparison between valves in the literature limit our ability to draw meaningful conclusions. Data that does exist suggests at present that there is no difference in non-infective failure rate are increasing in number, with the British valve subtypes in infant hydrocephalus, supported by both the randomised trial and institutional data in this study.

A decade of multi-modality PET and MR imaging in abdominal oncology

Objectives: To investigate trends observed in a decade of published research on multimodality PET(/CT)+MR imaging in abdominal oncology, and to explore how these trends are reflected by the use of multimodality imaging performed at our institution. Methods: First, we performed a literature search (2009–2018) including all papers published on the multimodality combination of PET(/CT) and MRI in abdominal oncology. Retrieved papers were categorized according to a structured labelling system, including study design and outcome, cancer and lesion type under investigation and PET-tracer type. Results were analysed using descriptive statistics and evolutions over time were plotted graphically. Second, we performed a descriptive analysis of the numbers of MRI, PET/CT and multimodality PET/CT+MRI combinations (performed within a ≤14 days interval) performed during a similar time span at our institution. Results: Published research papers involving multimodality PET(/CT)+MRI combinations showed an impressive increase in numbers, both for retrospective combinations of PET/CT and MRI, as well as hybrid PET/MRI. Main areas of research included new PET-tracers, visual PET(/CT)+MRI assessment for staging, and (semi-)quantitative analysis of PET-parameters compared to or combined with MRI-parameters as predictive biomarkers. In line with literature, we also observed a vast increase in numbers of multimodality PET/CT+MRI imaging in our institutional data. Conclusions: The tremendous increase in published literature on multimodality imaging, reflected by our institutional data, shows the continuously growing interest in comprehensive multivariable imaging evaluations to guide oncological practice. Advances in knowledge: The role of multimodality imaging in oncology is rapidly evolving. This paper summarizes the main applications and recent developments in multimodality imaging, with a specific focus on the combination of PET+MRI in abdominal oncology.

Implementing and Managing a Data Curation Workflow in the Cloud

Objective: To increase data quality and ensure compliance with appropriate policies, many institutional data repositories curate data that is deposited into their systems. Here, we present our experience as an academic library implementing and managing a semi-automated, cloud-based data curation workflow for a recently launched institutional data repository. Based on our experiences we then present management observations intended for data repository managers and technical staff looking to move some or all of their curation services to the cloud. Methods: We implemented tooling for our curation workflow in a service-oriented manner, making significant use of our data repository platform’s application programming interface (API). With an eye towards sustainability, a guiding development philosophy has been to automate processes following industry best practices while avoiding solutions with high resource needs (e.g., maintenance), and minimizing the risk of becoming locked-in to specific tooling. Results: The initial barrier for implementing a data curation workflow in the cloud was high in comparison to on-premises curation, mainly due to the need to develop in-house cloud expertise. However, compared to the cost for on-premises servers and storage, infrastructure costs have been substantially lower. Furthermore, in our particular case, once the foundation had been established, a cloud approach resulted in increased agility allowing us to quickly automate our workflow as needed. Conclusions: Workflow automation has put us on a path toward scaling the service and a cloud based-approach has helped with reduced initial costs. However, because cloud-based workflows and automation come with a maintenance overhead, it is important to build tooling that follows software development best practices and can be decoupled from curation workflows to avoid lock-in.

Multi-Institutional Data Collection and Analysis via the Pediatric Proton/Photon Consortium Registry

Care of patients with proton therapy has increased in the past decade. It is important to report on outcomes and disease specific utilization of particle therapy. In this chapter, we review our experience in developing a registry for pediatric patients treated with radiation to assess outcomes and provide a platform for shared research interests.

A primer on gathering and analysing multi-level quantitative evidence for differential student outcomes in higher education

A significant challenge currently facing the higher education sector is how to address differential student outcomes in terms of attainment and continuation gaps at various stages of students’ transitions. Worryingly, there appears to be a ‘deficit’ discourse among some university staff in which differential outcomes are perceived to be due to student deficiencies. This may be exacerbated by institutional analyses placing an over-emphasis on the presence of the gaps rather than the causes. The purpose of this primer is to provide advice about how institutions can carry out far more nuanced analyses of their institutional data without requiring specialist software or expertise. Drawing on a multi-level framework for explaining differential outcomes, we begin with guidance for gathering quantitative data on explanatory factors for attainment and continuation gaps, largely by linking sources of internal data that have not previously been connected. Using illustrative examples, we then provide tutorials for how to model explanatory factors employing IBM SPSS Statistics (IBM Corp., Armonk, NY, USA) to perform and interpret regression and meta-regression analyses of individual- and group-level (aggregated) student data, combined with data on micro- and meso-level factors. We propose that university staff with strategic responsibilities could use these approaches with their institutional data, and the findings could then inform the design of context-specific interventions that focus on changing practices associated with gaps. In doing so, institutions could enhance the evidence-base, raise awareness, and further ‘embed the agenda’ when it comes to understanding potential reasons for differential student outcomes during educational transitions.

Polynomial modeling of emergency department arrivals: An analysis of local and national utilization patterns

Emergency department (ED) overcrowding is a national problem that is associated with ambulance diversion, decreased patient and provider satisfaction and poor patient outcomes. This study presents a novel approach to modeling the relationship between time of day, day of week, and ED arrivals using a hierarchical polynomial regression model. A series of hierarchical regression models were created to determine polynomial effects and capture the covariability (defined as R2) of the relationships from the 2009 to 2017 National Hospital Ambulatory Medical Care Survey (NHAMCS) Emergency Department Public Use Data File and institutional data from a regional medical center from 2018 to 2019. The following hierarchical regression models were constructed: cubic main effects, cubic interaction effects, quartic main effects, quartic interaction effects, quintic main effects, and quantic interaction effects. Based on maximal improvement in R2 and significance of each of the four effects in both the national and institutional data sets, the quartic main effects model was determined to be optimal for describing ED arrival patterns. In alignment with prior studies, significantly higher ED arrival volumes were observed on Mondays in comparison to all other weekdays.