Étudiante en doctorate

Deniz Cetin-Sahin, PhD
(514) 483-2121 (1801)
deniz.sahin@mail.mcgill.ca
Formée en tant que médecin, la Dre Deniz Cetin-Sahin a obtenu une maîtrise en médecine expérimentale et en médecine familiale à l'Université McGill en 2012. La Dre Cetin-Sahin a participé à des projets de recherche axés sur le délirium des résidents vivant dans les CHSLD, les maladies chroniques affectant le physique et la dépression chez les personnes âgées vivant dans la communauté, ainsi que sur les soins d’urgence adaptés aux aînés. Suite à sa participation dans ces nombreux projets, Cetin-Sahin s'est intéressée à la recherche sur les soins gériatriques interdisciplinaires.
Elle a rejoint l’équipe de recherche en 2014 en tant que chercheuse post-doctorale et est actuellement dans la dernière année de ses études doctorales en médecine familiale et soins de première ligne à McGill, sous la supervision de la Dre Machelle Wilchesky. Son projet de doctorat est financé par le Fonds de recherche du Québec – Santé. Sa recherche portait principalement sur les interventions visant à réduire les transferts de soins aigus potentiellement évitables des CHSLD, y compris aussi l'évaluation de leur efficacité et l'amélioration des approches pour l’analyse. Au-delà de ses contributions méthodologiques et implications cliniques, sa thèse donnera lieu à un nouveau protocole d'étude observationnelle. Ce protocole sera ultimement mis en œuvre et ses résultats seront transformés en recommandations d'action auprès des médecins et infirmières de première ligne en soins de longue durée, ainsi qu'auprès des résidents et de leurs familles au CIUSSS-Centre-Ouest-de-l’île-de-Montréal.
“Reducing potentially avoidable acute care transfers from long-term care homes: Evaluating intervention effectiveness and improving approaches for their analysis”, Dr. Deniz Cetin-Sahin, PhD Student.
Background: Potentially avoidable acute care transfers from long-term care homes (CHSLDs) may occur contrary to residents’ advance directives, result in adverse patient outcomes, and represent inefficiencies in our health care system. As such, long-term care physicians and other frontline staff are increasingly being directed to reduce them. However, the current understanding of the effectiveness of interventions aiming to reduce these transfers is incomplete. As well, novel causal inference and data simulation methods have yet to be applies in this area although they offer wide-ranging potential in reducing design bias and increasing precision.
Aim and Objectives: The aim of my doctoral dissertation is to conduct a thorough investigation of potentially avoidable emergency department transfers and hospitalizations from long-term care homes with a view of designing an observational study using causal inference methods.
Methods: I will conduct four sub-studies, which will yield four thesis manuscripts, to:
- Conduct a comprehensive systematic review to synthesize the published evidence that assesses the effectiveness/efficacy of interventions that aim to reduce emergency department transfers and/or hospitalizations for long-term care home residents experiencing an acute health-status change.
- Compare potentially avoidable emergency department transfers and hospitalizations among our CIUSSS CHSLD residents and describe these outcome measures for conditions that are, in theory, ‘clinically manageable’ in the CHSLD setting. In a cross-sectional study using real-world data pertaining to our residents who received care at the Jewish General Hospital emergency department, I aim to prioritize a transfer outcome measure for the proposed observational study.
- Gain better understanding of the anticipated results and their sensitivity to violations of underlying statistical model assumptions. Using a data simulation approach, I aim to:
- Assess the potential magnitude of biases and performance of the statistical inference models used to analyze the observational study.
- Estimate the statistical power and minimum sample size needed to detect clinically relevant effect sizes.
- Develop an observational study protocol applying a causal inference framework to estimate the average causal effect of a selected exposure existing in real world (e.g., advance care planning) on reducing the prioritized outcome measure, using the findings of the 3 substudies.
Expected Results: My thesis project will be the first to demonstrate designing an observational study informed by a systematic review, a cross-sectional study, and data simulations, through the lens of a causal inference framework. Deliverables emanating from this dissertation will provide new and important insights for long-term care stakeholders, policy makers, and researchers. Future steps will involve executing the study protocol, and moving the results into recommendations for action through deliberative dialogues with long-term care stakeholders.
Étudiants en maîtrise
- Stephanie Ballard, MSc
- Matteo Peretti, MSc
- Kathleen (Kayte) Andersen, MSc
Internes
- Brandon Azimov
- Arielle Grossman