Adapting PRISMA For System Dynamics Models In Healthcare: A Case Study Of Transcatheter Aortic Valve Implantation In Canada
Author(s)
Joao Carapinha, PhD1, Trisha Hutzul, MSPH2, Richard Lech, BSc2, Sylvie Nichols, MSc3, Crystal Lubbe, Ph.D4, Rene Pretorius, Ph.D5;
1Northeastern University, Researcher, Boston, MA, USA, 2Edwards Lifesciences, Toronto, ON, Canada, 3Edwards Lifesciences, Quebec City, QC, Canada, 4Syenza, Johannesburg, South Africa, 5Syenza, Boston, MA, USA
1Northeastern University, Researcher, Boston, MA, USA, 2Edwards Lifesciences, Toronto, ON, Canada, 3Edwards Lifesciences, Quebec City, QC, Canada, 4Syenza, Johannesburg, South Africa, 5Syenza, Boston, MA, USA
Presentation Documents
OBJECTIVES: System dynamics models (SDMs) are frequently used in healthcare decision-making to simulate complex systems, enabling policymakers to predict outcomes, optimize resources, and make informed decisions for better long-term health outcomes. Systematic literature reviews to develop SDM in healthcare face challenges, as PRISMA primarily synthesizes clinical evidence often overlooking the broader contextual and systems-level data required for comprehensive modeling. This research adapts the PRISMA checklist for SDM-specific systematic reviews. This adapted approach leverages the introduction and methods items of the PRISMA checklist to ensure a transparent and reproducible process for identifying and extracting data relevant to SDM development. This approach is demonstrated through a case study of a SDM for transcatheter aortic valve implantation (TAVI) in Canada.
METHODS: Systematic literature searches for SDM development mirrors early review stages and includes a multi-database search. The strategy incorporates both top-down (disease-specific terms) and bottom-up (primary healthcare terms) approaches. Titles and abstracts are screened for factors like disease progression, diagnosis, treatment, and resource use. Full-text articles are reviewed against predefined eligibility criteria, focusing on data informing the model's structure and parameters. Data extraction uses a customized tool to capture variables like definitions, sources, methods, and study comparisons. The tool prioritizes quantifiable data, such as time-to-event metrics (e.g., time from murmur detection to echocardiogram), drop-off rates between stages, and resource utilization.
RESULTS: This approach yielded diverse studies for the TAVI SDM, moving beyond traditional clinical evidence. It integrated broader data for modeling the TAVI patient journey. The top-down and bottom-up strategy identified quantifiable primary healthcare variables. This information will contribute to a more comprehensive understanding of the factors influencing early disease detection and referral patterns, ultimately leading to more robust model parameterization.
CONCLUSIONS: This adapted PRISMA framework supports structured, transparent searches for SDM development, enabling robust simulation models for decision-making.
METHODS: Systematic literature searches for SDM development mirrors early review stages and includes a multi-database search. The strategy incorporates both top-down (disease-specific terms) and bottom-up (primary healthcare terms) approaches. Titles and abstracts are screened for factors like disease progression, diagnosis, treatment, and resource use. Full-text articles are reviewed against predefined eligibility criteria, focusing on data informing the model's structure and parameters. Data extraction uses a customized tool to capture variables like definitions, sources, methods, and study comparisons. The tool prioritizes quantifiable data, such as time-to-event metrics (e.g., time from murmur detection to echocardiogram), drop-off rates between stages, and resource utilization.
RESULTS: This approach yielded diverse studies for the TAVI SDM, moving beyond traditional clinical evidence. It integrated broader data for modeling the TAVI patient journey. The top-down and bottom-up strategy identified quantifiable primary healthcare variables. This information will contribute to a more comprehensive understanding of the factors influencing early disease detection and referral patterns, ultimately leading to more robust model parameterization.
CONCLUSIONS: This adapted PRISMA framework supports structured, transparent searches for SDM development, enabling robust simulation models for decision-making.
Conference/Value in Health Info
2025-05, ISPOR 2025, Montréal, Quebec, CA
Value in Health, Volume 28, Issue S1
Code
MSR140
Topic
Methodological & Statistical Research
Disease
No Additional Disease & Conditions/Specialized Treatment Areas, SDC: Cardiovascular Disorders (including MI, Stroke, Circulatory), STA: Surgery