HEALTHCARE DECISION AND PATIENT CHOICE: THE ROLE OF EVIDENCE-BASED MEDICINE

Integrating Patient Preferences into Evidence-Based Medicine Healthcare decision-making is a complex and evolving process in public health systems, driven by the need to optimize outcomes for individual patients and the broader population. The transition from traditional intuition-based practices to evidence-based approaches has been fundamental in improving the quality of care and reducing variations in clinical practices. Evidence-Based Medicine (EBM), introduced by Dr. Gordon H. Guyatt, has played a pivotal role in this transformation by em-phasizing the integration of clinical expertise, scientific evidence, and patient values and prefer-ences. Integrating patient preferences into the clinical decision-making process is emerging as a critical aspect of evidence-based medicine. This PhD project explores three fundamental facets: how to find, collect, and analyse Patient Preference Information (PPI) to enrich healthcare decision-making. The first step: How to sintetize the evidence Systematic Reviews, a cornerstone of EBM, provide a structured method for synthesizing research evidence and bridging the gap between research and practice. During this PhD program, was producted, a systematic review on predictive models for assessing the survival of patients undergo-ing Extracorporeal Membrane Oxygenation (ECMO). The methodology employed in this systematic review encompassed tailored search strategies, stringent inclusion and exclusion criteria, and meticulous database searches to ensure the selection of pertinent studies. The transparent application of these criteria aimed to maintain the review's in-tegrity. This work provides in-depth insights into the methodology, data sources, and specific find-ings of the systematic review, offering a comprehensive understanding of the landscape of ECMO survival prediction models. The systematic review process involved consulting multiple databases, including PubMed, CINAHL, Embase, MEDLINE, and Scopus, to identify relevant articles published between January 2011 and February 2022. Inclusion criteria focused on studies involving adult patients undergoing ECMO, re-porting newly developed and validated predictive models for mortality. Studies based on animal models, systematic reviews, case reports, and conference abstracts were excluded. Data extrac-tion encompassed study characteristics, risk model details, and model performance. The prediction model risk-of-bias assessment tool (PROBAST) was employed to assess the risk of bias in the in-cluded studies, and the protocol was registered in the Open Science Framework (https://osf.io/fevw5). The results of this systematic review identified twenty-six prognostic scores for in-hospital mortality, with varying study sizes ranging from 60 to 4557 patients. Common variables considered in these models included age, lactate concentration, creatinine concentration, bilirubin concentration, and days of mechanical ventilation before ECMO initiation. Importantly, only a fraction of these scores had been externally validated, and none were judged to be at low risk of bias. While several prognostic models exist for ECMO patient outcomes, most have not undergone ex-ternal validation, and their application often occurs after ECMO initiation. This raises questions about the appropriateness of initiating ECMO in certain cases. Future research must explore new methodological perspectives to enhance the performance of predictive models for ECMO, with the ultimate goal of positively influencing patient outcomes. The integration of patient preferences and values, as discussed in the context of Evidence-Based Medicine (EBM), may play a crucial role in refining these predictive models to align with patient-centered healthcare approaches.