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

CLINICAL, GENETICS AND MOLECULAR RISK FACTORS OF DENGUE SEVERITY

Ph.DDOCTOR OF PHILOSOPH

Development of a Model Predicting 30-Day Readmission Using Prescription Information from the Medical Short Stay Units of One NHS Trust

Emergency readmission is defined within the NHS as an emergency admission to hospital within 30 days of discharge. Excess readmissions are undesirable in terms of care quality and efficiency; yet, despite financial incentives for improvement, reports of increasing readmission rates continue. There is evidence that pharmacist intervention can prevent medication errors, discrepancies and adverse drug events; which can each contribute to readmission. The purpose of the work in this thesis was to develop a model based on routinely collected prescription information to enable the pharmacy team to estimate readmission risk in the clinical setting, thereby facilitating appropriate prioritisation of potentially preventative intervention. A multiple logistic regression model for estimating readmission risk using routinely recorded prescription information among patients discharged home from the medical short stay units of one NHS Trust was developed, and survival analysis was undertaken to characterise readmission behaviour in relation to the predictors. The readmission rate was 18% (220/1240). Readmission risk increased with increasing age and polypharmacy: each additional medicine prescribed increased the odds of readmission within 30 days by eight per cent and each additional year of age increased the odds of readmission within 30 days by two per cent. Each additional medicine prescribed decreased the time to readmission by seven per cent and each additional year of age decreased the time to readmission by one per cent. Over one-third of readmissions occurred within one week (73/200) and more than half (114/200) occurred within two weeks, supporting that identification of those at risk and intervention to prevent readmission should be provided promptly. The predictive model developed is suitable for application on admission and could therefore enable clinicians to identify the patients most likely to require intervention to prevent readmission before they are discharged home from hospital, thereby maximising the time available to organise and/or provide the necessary support. Although the logistic regression model improved accuracy by 36% compared to indiscriminate intervention whilst identifying 70% of patients who would be readmitted, it had relatively weak discriminative capability (c-statistic 0.637). It may be the case that clinical intuition is as effective for predicting readmission and further research should be undertaken to confirm whether this is the case

Prehospital critical care for out-of-hospital cardiac arrest: a complex intervention in a complex environment

BackgroundPrehospital critical care has the potential to improve the currently low survival rates following out-of-hospital cardiac arrest (OHCA). In some areas of the United Kingdom, prehospital critical care teams are dispatched to OHCA, while in others the standard of care of Advanced Life Support (ALS) is seen as sufficient. This thesis examines prehospital critical care for OHCA from different perspectives and aims to provide stakeholders in prehospital care with the information required to guide the funding and configuration of prehospital critical care for OHCA.Methods1. Qualitative analysis of stakeholders’ views on research and funding of prehospital critical care. Data from focus groups and interviews of five stakeholder groups were analysed using the framework approach.2. Economic analysis of ALS and prehospital critical care for OHCA. A decision analysis model of costs and effects of ALS for OHCA was created, using secondary data as well as data provided from relevant prehospital organisations. A range of possible effects of prehospital critical care for OHCA were simulated. A probabilistic sensitivity analysis was chosen to reflect the uncertainty of the underlying data.3. Prospective multicentre observational analysis, comparing survival to hospital discharge in patients with OHCA who received prehospital critical care or ALS. Propensity score matching was used to adjust for confounding and bias, subgroup analysis in patients with witnessed OHCA with shockable rhythm and two sensitivity analyses (primary dispatch and multiple imputation datasets) were used.4. Descriptive analysis of prehospital critical care interventions during and after OHCA. Frequencies of critical care interventions were analysed according to patient groups; a propensity score matching analysis examined the effect of treatment at a cardiac arrest centre in patients transferred to hospital.ResultsStakeholders expressed strong and often opposing views on a variety of topics discussed in regards to prehospital research, prehospital critical care and funding strategies.The current standard of care, Advanced Life Support (ALS) delivered by paramedics, was cost-effective at less than £20,000 per quality-adjusted life year (QALY) gained.After propensity score matching to account for an imbalance in prognostic factors, survival to hospital discharge did not differ between patients with OHCA receiving prehospital critical care or ALS care. These results were stable throughout the subgroup and sensitivity analyses. In addition, prehospital critical care for OHCA is considerable more expensive than ALS and therefore highly unlikely to be cost-effective.The reasons for this lack of clinical effectiveness of prehospital critical care can be likely found in the low frequency of interventions delivered and the relatively late arrival of critical care teams at the scene of an OHCA.Stakeholders’ considerations in regards to further funding of the complex intervention of prehospital critical care for OHCA will likely include additional factors such as social acceptability, available resources and the potential for indirect benefits.ConclusionsThis research provided a multi-faceted analysis of the complex intervention prehospital critical care for OHCA. The results can aid decision making in regards to future funding but also consider uncertainty in data analysis and the complex environment in which prehospital critical care is being delivered