Einstein (Sao Paulo, Brazil) · 2026
Probabilistic clinical studies and deterministic physiology: building a Monte Carlo bridge with the "Frank" project.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Argolo Felipe Coelho, Freire Arthur Vasconcelos de Luna, Laporte Larrie Rabelo, Magnavita Guilherme Moreira, Correia Luís Cláudio Lemos
Аннотация
OBJECTIVE: To resolve the apparent paradox between deterministic physiological laws and probabilistic clinical outcomes by framing clinical variability as epistemic uncertainty within a complex, non-linear system, demonstrating how deterministic mechanisms can account for empirical distributions and outcome heterogeneity.
METHODS: We developed the "Frank" project, a 0-dimensional computational agent-based model (ABM) of human cardiovascular hemodynamics implemented in Julia (Agents.jl). Using Monte Carlo simulations (n=100), myocardial infarction was modeled by varying initial conditions, specifically sampling the infarction area from a Gaussian distribution along with systemic compliance. Cardiogenic shock was defined by a systolic arterial pressure <90mmHg and a cardiac index <2.2L/min/m2, with parameters evaluated via grid search against literature incidence benchmarks (5-10%).
RESULTS: The simulation produced hemodynamically stable, physiologically sound boundary parameters and pressure-volume loops. When initialized with a mean infarction area of 35±5% and systemic compliance of 1.5±0.3mL/mmHg, the model yielded a 7\% incidence of cardiogenic shock, closely matching empirical data. A strong linear inverse correlation was observed between infarction area and post-infarction systolic blood pressure (mean 93.96mmHg).
CONCLUSION: Stochastic-appearing clinical trial outcomes can emerge from deterministic physiological systems characterized by sensitivity to initial parameters and hidden variables. The "Frank" framework establishes a Monte Carlo bridge between mechanistic physiology and epidemiological statistics, offering an approach to explore outcome heterogeneity and refine clinical trial interpretation and patient stratification.
METHODS: We developed the "Frank" project, a 0-dimensional computational agent-based model (ABM) of human cardiovascular hemodynamics implemented in Julia (Agents.jl). Using Monte Carlo simulations (n=100), myocardial infarction was modeled by varying initial conditions, specifically sampling the infarction area from a Gaussian distribution along with systemic compliance. Cardiogenic shock was defined by a systolic arterial pressure <90mmHg and a cardiac index <2.2L/min/m2, with parameters evaluated via grid search against literature incidence benchmarks (5-10%).
RESULTS: The simulation produced hemodynamically stable, physiologically sound boundary parameters and pressure-volume loops. When initialized with a mean infarction area of 35±5% and systemic compliance of 1.5±0.3mL/mmHg, the model yielded a 7\% incidence of cardiogenic shock, closely matching empirical data. A strong linear inverse correlation was observed between infarction area and post-infarction systolic blood pressure (mean 93.96mmHg).
CONCLUSION: Stochastic-appearing clinical trial outcomes can emerge from deterministic physiological systems characterized by sensitivity to initial parameters and hidden variables. The "Frank" framework establishes a Monte Carlo bridge between mechanistic physiology and epidemiological statistics, offering an approach to explore outcome heterogeneity and refine clinical trial interpretation and patient stratification.