Optimising outcomes for potentially resectable pancreatic cancer through personalised predictive medicine : the application of complexity theory to probabilistic statistical modeling

Survival outcomes for pancreatic cancer remain poor. Surgical resection with adjuvant therapy is the only potentially curative treatment, but for many people surgery is of limited benefit. Neoadjuvant therapy has emerged as an alternative treatment pathway however the evidence base surrounding the treatment of potentially resectable pancreatic cancer is highly heterogeneous and fraught with uncertainty and controversy. This research seeks to engage with conjunctive theorising by avoiding simplification and abstraction to draw on different kinds of data from multiple sources to move research towards a theory that can build a rich picture of pancreatic cancer management pathways as a complex system. The overall aim is to move research towards personalised realistic medicine by using personalised predictive modeling to facilitate better decision making to achieve the optimisation of outcomes. This research is theory driven and empirically focused from a complexity perspective. Combining operational and healthcare research methodology, and drawing on influences from complementary paradigms of critical realism and systems theory, then enhancing their impact by using Cilliers’ complexity theory ‘lean ontology’, an open-world ontology is held and both epistemic reality and judgmental relativity are accepted. The use of imperfect data within statistical simulation models is explored to attempt to expand our capabilities for handling the emergent and uncertainty and to find other ways of relating to complexity within the field of pancreatic cancer research. Markov and discrete-event simulation modelling uncovered new insights and added a further dimension to the current debate by demonstrating that superior treatment pathway selection depended on individual patient and tumour factors. A Bayesian Belief Network was developed that modelled the dynamic nature of this complex system to make personalised prognostic predictions across competing treatments pathways throughout the patient journey to facilitate better shared clinical decision making with an accuracy exceeding existing predictive models.Survival outcomes for pancreatic cancer remain poor. Surgical resection with adjuvant therapy is the only potentially curative treatment, but for many people surgery is of limited benefit. Neoadjuvant therapy has emerged as an alternative treatment pathway however the evidence base surrounding the treatment of potentially resectable pancreatic cancer is highly heterogeneous and fraught with uncertainty and controversy. This research seeks to engage with conjunctive theorising by avoiding simplification and abstraction to draw on different kinds of data from multiple sources to move research towards a theory that can build a rich picture of pancreatic cancer management pathways as a complex system. The overall aim is to move research towards personalised realistic medicine by using personalised predictive modeling to facilitate better decision making to achieve the optimisation of outcomes. This research is theory driven and empirically focused from a complexity perspective. Combining operational and healthcare research methodology, and drawing on influences from complementary paradigms of critical realism and systems theory, then enhancing their impact by using Cilliers’ complexity theory ‘lean ontology’, an open-world ontology is held and both epistemic reality and judgmental relativity are accepted. The use of imperfect data within statistical simulation models is explored to attempt to expand our capabilities for handling the emergent and uncertainty and to find other ways of relating to complexity within the field of pancreatic cancer research. Markov and discrete-event simulation modelling uncovered new insights and added a further dimension to the current debate by demonstrating that superior treatment pathway selection depended on individual patient and tumour factors. A Bayesian Belief Network was developed that modelled the dynamic nature of this complex system to make personalised prognostic predictions across competing treatments pathways throughout the patient journey to facilitate better shared clinical decision making with an accuracy exceeding existing predictive models

How Do We Study Satisfaction With Academic E‐Book Collections?

Much of the existing literature on patron satisfaction with e‐books in academic settings does not differentiate between platforms, formats, and other conditions that drastically change the user’s ability to read, annotate, and use e‐book content. The Charlotte Initiative is a project funded by the Mellon Foundation to convene a working group that investigates principles for permanent acquisition of e‐books for academic libraries. As part of this project, a user experience research team has been created to review the existing literature on patron satisfaction with multiple aspects of e‐books. During summer 2015, this research team began a metastudy to determine areas of the user experience with e‐books in academic libraries that have been studied comprehensively and to identify areas that have not received formal evaluation. In this paper, we not only convey the results of our research team’s literature review but also provide criteria that librarians and institutions can use to guide assessments of user experience with e‐books in academic library settings

Eagle Syndrome Presenting in a Patient as Dysphagia and Failed Intubations

Eagle syndrome is a rare elongation of the styloid process or calcification of the stylohyoid ligament. Most cases are asymptomatic but compression on head and neck structures can result in dysphagia and neck pain. Etiology may be related to surgical scar tissue but is unclear due to limited case presentation. A 52 year-old male with surgical history of tonsillectomy presented to the hospital with shortness of breath. He developed sudden onset dyspnea and pink frothy sputum. He desaturated and was put on BiPaP in the ED. Chest X-ray revealed flash pulmonary edema and he was admitted for hypoxic respiratory failure. Within three days his status was complicated by bacteremia and atrial fibrillation and cardiology recommended a trans-esophageal echo before discharge. He was unable to tolerate TEE at the bedside and anesthesia with intubation was planned instead. Three intubation attempts failed despite fiberoptic and glidescope assistance and limited neck and laryngeal tissue mobility were noted. Follow-up imaging of CT neck/chest without contrast showed complete calcified left stylohyoid ligament extending from the styloid process to the hyoid bone. Post-procedure he endorsed dysphagia with both solids and liquids and voice change. Swallow studies and flexible laryngoscopy showed oropharyngeal dysphagia and silent aspiration with nectar thick liquids. He has not been able to tolerate NGT and has been placed on TPN pending gastrostomy tube. This case shows a rare symptomatic presentation of Eagle Syndrome with CT visualization and contributes to gathering more information as to the etiology and clinical presentation of the condition.https://scholarlycommons.henryford.com/merf2020caserpt/1026/thumbnail.jp

Personalized pancreatic cancer management : a systematic review of how machine learning is supporting decision-making

This review critically analyzes how machine learning is being utilized to support clinical decision-making in the management of potentially resectable pancreatic cancer. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, electronic searches of MEDLINE, Embase, PubMed and Cochrane Database were undertaken. Studies were assessed using the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modeling Studies (CHARMS) checklist. In total 89,959 citations were retrieved. Six studies met the inclusion criteria. Three studies were Markov decision-analysis models comparing neoadjuvant therapy versus upfront surgery. Three studies predicted survival time using Bayesian modeling (n = 1), Artificial Neural Network (n = 1), and one study explored machine learning algorithms including: Bayesian Network, decision trees, nearest neighbor, and Artificial Neural Networks. The main methodological issues identified were: limited data sources which limits generalizability and potentiates bias, lack of external validation, and the need for transparency in methods of internal validation, consecutive sampling, and selection of candidate predictors. The future direction of research relies on expanding our view of the multidisciplinary team to include professionals from computing and data science with algorithms developed in conjunction with clinicians and viewed as aids, not replacement, to traditional clinical decision making

"Once a sleuth, always a sleuth": A study of fan publications as secondary materials for research in girls' series books

This paper describes a study of two fan-based periodicals devoted to the study of girls' series books. The study was designed to evaluate the usefulness of these publications as secondary research resources for research collections that focus on historical children's literature or girls' series books in particular. The periodicals studied were Susabella Passengers and Friends and The Whispered Watchword, each produced by a group of amateur writers and editors dedicated to collecting girls' series books. A total of 537 articles from 33 issues were examined, using a set of ten criteria developed to ascertain the emphasis on research quality or fan-oriented material

A mixed methods evaluation of a digital intervention to improve sedentary behaviour across multiple workplace settings

Background: Prolonged sedentary behaviour (SB) is associated with risk of chronic diseases. Digital interventions in SB require mixed method evaluations to understand potential for impact in real-world settings. In this study, the RE-AIM QuEST evaluation framework will be used to understand the potential of a digital health promotion application which targets reducing and breaking up SB across multiple workplace settings. Methods: Four companies and 80 employees were recruited to use a digital application. Questionnaires were used to measure SB, and additional health and work-related outcomes at baseline, one month, three month and six month follow-up. Qualitative data was collected through focus groups with employees and interviews with stakeholders. Questionnaire data was analysed using Wilcoxon Sign Rank tests and qualitative data was thematically analysed. Results: The digital application significantly increased standing time at one month for the total group and transitions per hour in one of the companies. Facilitators and barriers were identified across RE-AIM. Conclusions: Addressing the barriers which have been identified, while maintaining the positive attributes will be critical to producing an effective digital application which also has the potential for impact in the real world

Bardet-Biedl syndrome, renal transplant and percutaneous nephrolithotomy: a case report and review of the literature

Bardet-Biedl syndrome is an autosomal recessive disorder with obesity, polydactly, retinitis pigmentosa, hypogenitalism, intellectual impairment and varying degree of renal abnormalities. Fewer than ten cases of paediatric renal transplantation for BBS have been reported in literature so far. This is the only case report of BBS transplant urolithiasis which was dealt with percutaneous nephrolithotomy and has been stone free for seven years. This is a complex case with a rare genetic disorder, renal transplant, renal stone, ileal conduit, long loop and inversely placed kidney. This case exemplifies the need for multidisciplinary management of complex cases and emphasises PCNL as the safe method

Viability of Hunting as a Means of Wild Hog Population Management on Federal Property

The Big South Fork National River and Recreation Area allows hunter to purchase permits and hunt wild hogs on property with the intention of curbing increases in wild hog populations. In order to assess outcomes of the wild hog hunting permit program, researchers collaborated with site managers to develop protocol and solicit information from permit holders regarding number of animals seen and harvested, sex of animals harvested, geographic areas hunted, length and number of hunts, and open qualitative feedback regarding the program. All permit holders agreeing to be contacted during permit registration were called with 37.57% (N=65) of permit holder completing the telephone survey. While more hogs were seen than harvested, the total harvested hogs was 52. Results indicate hunting is not a viable option for population management in and of itself, as the number if wild hogs harvested was minimal. A longitudinal study is necessary to overcome case study (single year) limitations, such as weather, hunter economics, herd movement, herd reproduction and so forth. Salient variables may warrant consideration, including marketing of permit program, number of hunters within acceptable driving distance to hunting location, and much more. Herd management initiatives beyond hunting may be considered when necessary to control wild hog populations

Personalized prognostic bayesian network for pancreatic cancer : delivering personalized pancreatic cancer management throughout the patient journey

Background and Objectives: The aim of this study is to create the first Personalized Prognostic Bayesian Network for Pancreatic Cancer (PPBN-PC) to provide personalized predictions of 3-year or more survival time post resection. PPBN-PC’s ability to handle the dynamic nature of the care processes, with predictions evolving as more information becomes available, was assessed at the pre and post-operative stage of the patient journey. Materials and Methods: Parent nodes were identified from PubMed survival analysis studies (n=48691) and included: tumour factors, patient factors, tumour markers, inflammatory markers, neoadjuvant therapy, pathology and adjuvant therapy. Variables underwent a two-stage weighting process to summarise both the weight of the evidence against conflicting findings and a normalized weighting process placing each variable’s weighting within the entirety of the existing body of evidence. Priors for the model were calculated using the normalized weight for each variable as the weighted mean of the TNormal distribution for the corresponding parent node. Results: The PPBN-PC was validated against a dataset of 365 patients who presented to a tertiary referral centre with potentially resectable pancreatic cancer. Model performance measured by Area Under the Curve (AUC) ranged from 0.94 (P-value 0.002; 95% CI 0.859-1.000) for 0 missing data points to AUC 0.74 (P-value 0.000; 95% CI 0.660-0.809) accepting more than 4 missing data points in the validation dataset, for accuracy of pre-operative predictions. PPBN-PC performance for prognostic updating based on post-operatively available information ranged from AUC 0.97 (P-value 0.000; 95% CI 0.908-1.000) for 0 missing data points in pre and post-operative validation dataset to AUC 0.75 (P-value 0.000; 95% CI 0.655-0.838) accepting more than 4 missing data points in the pre and up to and including 2 missing data points in the post-operative validation dataset. The latter was the only point at which AUC fell below 0.80. Validated against every other combination of missing pre and post-operative data points PPBN-PC maintained an AUC greater than 0.8 (range 0.97-0.80) with P-value consistently below 0.001. Conclusion: This marks an important step towards achieving the delivery of precision medicine, as the next step will be to incorporated genomic data into the model hence combining genetic, pathology and clinical data, creating a vehicle to deliver personalized precision medicine