Rationalism and Empiricism in Modern Medicine

The roots of rationalism and empiricism in the Hippocratic tradition are explored. The triumph of the rationalists in the founding of modern medicine is emphasized. The development of clinical epidemiology and the evidence-based medicine over the last 30 years is described. The tension illuminates fundamental clinical and policy questions that doctors, the health care system, and the legal system confront today

A Pilot Study of Value of Information Analysis to Support Research Recommendations for the National Institute for Health and Clinical Excellence

Background - This project developed as a result of the activities of the Research Teams at the Centre for Health Economics, University of York, and ScHARR at the University of Sheffield in the methods and application of decision analysis and value of information analysis as a means of informing the research recommendations made by NICE, as part of its Guidance to the NHS in England and Wales, and informing the deliberations of the NICE Research and Development Committee. Objectives - The specific objectives of the pilot study were to: • Demonstrate the benefits of using appropriate decision analytic methods and value of information analysis to inform research recommendations. • Establish the feasibility and resource implications of applying these methods in a timely way, to inform NICE. • Identify critical issues and methodological challenges to the use of value of information methods for research recommendations (with particular regard to the new reference case as a suitable basis for this type of analysis).

A Review on Computer Aided Diagnosis of Acute Brain Stroke.

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas

How artificial intelligence tools can be used to assess individual patient risk in cardiovascular disease: problems with the current methods

BACKGROUND: In recent years a number of algorithms for cardiovascular risk assessment has been proposed to the medical community. These algorithms consider a number of variables and express their results as the percentage risk of developing a major fatal or non-fatal cardiovascular event in the following 10 to 20 years DISCUSSION: The author has identified three major pitfalls of these algorithms, linked to the limitation of the classical statistical approach in dealing with this kind of non linear and complex information. The pitfalls are the inability to capture the disease complexity, the inability to capture process dynamics, and the wide confidence interval of individual risk assessment. Artificial Intelligence tools can provide potential advantage in trying to overcome these limitations. The theoretical background and some application examples related to artificial neural networks and fuzzy logic have been reviewed and discussed. SUMMARY: The use of predictive algorithms to assess individual absolute risk of cardiovascular future events is currently hampered by methodological and mathematical flaws. The use of newer approaches, such as fuzzy logic and artificial neural networks, linked to artificial intelligence, seems to better address both the challenge of increasing complexity resulting from a correlation between predisposing factors, data on the occurrence of cardiovascular events, and the prediction of future events on an individual level

A systematic review of machine learning models for predicting outcomes of stroke with structured data

Background and purposeMachine learning (ML) has attracted much attention with the hope that it could make use of large, routinely collected datasets and deliver accurate personalised prognosis. The aim of this systematic review is to identify and critically appraise the reporting and developing of ML models for predicting outcomes after stroke.MethodsWe searched PubMed and Web of Science from 1990 to March 2019, using previously published search filters for stroke, ML, and prediction models. We focused on structured clinical data, excluding image and text analysis. This review was registered with PROSPERO (CRD42019127154).ResultsEighteen studies were eligible for inclusion. Most studies reported less than half of the terms in the reporting quality checklist. The most frequently predicted stroke outcomes were mortality (7 studies) and functional outcome (5 studies). The most commonly used ML methods were random forests (9 studies), support vector machines (8 studies), decision trees (6 studies), and neural networks (6 studies). The median sample size was 475 (range 70-3184), with a median of 22 predictors (range 4-152) considered. All studies evaluated discrimination with thirteen using area under the ROC curve whilst calibration was assessed in three. Two studies performed external validation. None described the final model sufficiently well to reproduce it.ConclusionsThe use of ML for predicting stroke outcomes is increasing. However, few met basic reporting standards for clinical prediction tools and none made their models available in a way which could be used or evaluated. Major improvements in ML study conduct and reporting are needed before it can meaningfully be considered for practice

Analysis of time-to-event for observational studies: Guidance to the use of intensity models

This paper provides guidance for researchers with some mathematical background on the conduct of time-to-event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time-dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative.Comment: 28 pages, 12 figures. For associated Supplementary material, see http://publicifsv.sund.ku.dk/~pka/STRATOSTG8

Diabetes-Related Complication in Canada; Prevalence of Complication, Their Association with Determinants and Future Potential Cost-Effectiveness of Pharmacy-Based Intervention

In the 21st century, diabetes mellitus (DM) emerged as one of the most prevalent non-communicable diseases, and poses a major problem for every health system in the world. Its global prevalence has more than doubled in the last three decades. As diabetes has become more prevalent, the health programming designed to target diabetes patients has remained inadequate and only heightened the burden. This heightened burden has manifested itself in the increased risk of complications common among patients with diabetes. These complications vary widely, and are typically categorized as either micro-vascular or macro-vascular depending upon the size of blood vessels that are compromised. Due to the havoc that can ensue by either type of complication, the increased risk of diabetes-related complications has been recognized as a serious threat to population health. To gain insight into the threat posed and how it will likely present in the Canadian population, patient’s data from the diabetes component of Survey on Living with Chronic Diseases in Canada (SLCDC-DM-2011) was analyzed. This analysis revealed that among Canadian diabetes patients, 80.26 percent reported having at least one type of diabetes-related complication. The most frequently reported complications were high blood pressure (54.65%), cataracts (29.52%), poor circulation (21.68%), and heart disease (19.4%). This analysis also revealed the predictive role of socio-economic factors associated with diabetes-related complications in Canada. Being married, having a higher income, and having a higher level of education were protective against most complications. In contrast, low levels of physical activity and high levels of HbA1C were important risk factors for many diabetes–related complications. Identifying common diabetes-related complications, protective factors and risk factors is useful for combating the threat posed by diabetes-related complications. To combat this threat in practice, healthcare professionals will play a significant role in the control and management of diabetes and its complications. Diabetes is a chronic disease that needs long-term treatment, and thus multi-disciplinary teams will be required. Increasingly, pharmacists are being determined as having a prominent position on these teams due to their accessibility to the Canadian population, and their expanding scope of practice. This profession has contributed positively to the long-term prognosis of patients with diabetes, in part, by aiding in the control and management of the disease. This aid oftentimes comes in the form of pharmacy-based interventions. Pharmacy-based interventions include a variety of services aimed at enabling patients with diabetes to have better control of their condition. I conducted a systematic review and meta-analysis to evaluate the effects of pharmacy-based interventions on clinical and non-clinical outcomes associated with diabetes-related complications. Four main databases were searched. Based upon my meta-analysis, the standardized absolute mean difference in reduction of HbA1C (%) from baseline to the time of the last follow-up significantly favoured patients in the pharmacy-based intervention group compared to those receiving care as usual (0.96%; 95% CI 0.71: 1.22, P<0.001). In addition, the standardized absolute mean difference in reduction of BMI unit (kg/m2) was 0.61 (95% CI 0.20: 1.03, P<0.001) in favour of the pharmacy-based intervention group. Both of these results demonstrate the positive effect pharmacy-based interventions can have on clinical outcomes. However, there is a dearth of evidence about the effects of pharmacy-based interventions on non-clinical outcomes, including health care utilization and quality of life. Therefore, it was not possible to evaluate non-clinical outcomes associated with diabetes-related complications in the same way. Each year healthcare expenses incurred from diabetes and its complications total more than US$827 billion. This health care cost is significant, and is only expected to grow alongside diabetes’ increasing prevalence. In light of this, a debate over the comparative effectiveness of the different strategies used to manage diabetes and its complications has been sparked. The development of analytic models that can be used as tools in determining budget prioritization and cost-effectiveness of interventions is beginning to be prioritized. To conduct an economic evaluation of these interventions, simulation models are necessary. These models estimate health outcomes, such as life years saved or Quality Adjusted Life Years (QALYs) gained, and account for the costs and health consequences associated with diabetes, its complications and risk factors. I developed a hybrid (agent-based/system dynamic) individual-level micro simulation model using 2,931 patient records from the SLCDC-2011. This model extrapolated the effects of pharmacy-based interventions on health outcomes, costs and health-related quality of life (HRQOL) over time through time-varying risk factors of diabetes-related complications. The treatment effects of pharmacy-based interventions were modeled as reductions in HbA1c levels, BMI, systolic blood pressure and LDL, all of which can affect the risk of progressing long-term complications. The annual costs of diabetes-related complications, as well as, costs associated with pharmacy-based intervention from a societal prospective, were also considered. Using this data, the micro-simulation model was able to estimate the expected number of major health events (heart failure, stroke, amputation, and blindness), QALYs over a patient’s lifetime, the patient’s economic burden on the health care system, and the extent to which pharmacy-based intervention can modify these outcomes. Deterministic and probabilistic sensitivity analyses were conducted to evaluate the uncertainty around the results. Based on the results from my micro-simulation model, a pharmacy–based intervention could avert a total of 155 deaths associated with complications, 19 heart failures, 159 strokes, 24 amputations and 29 blindness events in a population of 2,931 patients over the next 50 years. In addition, the intervention could add 1,246 additional life-years (0.42 per patients) and 953 additional quality-adjusted life-years (0.32 per patients). The intervention would also be cost-effective in comparison to usual care, as indicated by the incremental discounted cost per QALY gained ($3928). Overall, these results suggest that an integrated pharmacy-based intervention could be a cost-effective strategy to control and manage diabetes-related complications in Canada. This is promising and has important public health implications that should not be ignored

Proteomic Biomarkers of Atherosclerosis

Biomarkers provide a powerful approach to understanding the spectrum of cardiovascular diseases. They have application in screening, diagnostic, prognostication, prediction of recurrences and monitoring of therapy. The “omics” tool are becoming very useful in the development of new biomarkers in cardiovascular diseases. Among them, proteomics is especially fitted to look for new proteins in health and disease and is playing a significant role in the development of new diagnostic tools in cardiovascular diagnosis and prognosis. This review provides an overview of progress in applying proteomics to atherosclerosis. First, we describe novel proteins identified analysing atherosclerotic plaques directly. Careful analysis of proteins within the atherosclerotic vascular tissue can provide a repertoire of proteins involved in vascular remodelling and atherogenesis. Second, we discuss recent data concerning proteins secreted by atherosclerotic plaques. The definition of the atheroma plaque secretome resides in that proteins secreted by arteries can be very good candidates of novel biomarkers. Finally we describe proteins that have been differentially expressed (versus controls) by individual cells which constitute atheroma plaques (endothelial cells, vascular smooth muscle cells, macrophages and foam cells) as well as by circulating cells (monocytes, platelets) or novel biomarkers present in plasma