Probabilistic clinical studies and deterministic physiology: building a Monte Carlo bridge with the "Frank" project.
7 affiliations
- 1Laboratório de Neuropsicofarmacologia (LANP), Departamento de Neurociências de Saúde mental, Faculdade de Medicina, Universidade Federal da Bahia, Hospital Universitário Professor Edgard Santos, Salvador, BA, Brazil.
- 2Instituto de Psiquiatria, Universidade de São Paulo, São Paulo, SP, Brazil.
- 3Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, BA, Brazil.
- 4Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, SP, Brazil.
- 5Instituto do Coração, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil.
- 6Department or Epidemiology, Rollins School of Public Health, Emory University, Georgia, United States.
- 7Escola Bahiana de Medicina e Saúde Pública, Salvador, BA, Brazil.
Abstract
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.
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BibTeX
@article{argolo2026probabilistic,
title = {{Probabilistic clinical studies and deterministic physiology: building a Monte Carlo bridge with the "Frank" project.}},
author = {Felipe Coelho Argolo and Arthur Vasconcelos de Luna Freire and Larrie Rabelo Laporte and Guilherme Moreira Magnavita and Luís Cláudio Lemos Correia},
journal = {Einstein (Sao Paulo, Brazil)},
year = {2026},
month = oct,
doi = {10.31744/einstein_journal/2026ae2239},
url = {https://europepmc.org/article/MED/42912034},
note = {CC BY},
}
RIS
TY - JOUR
TI - Probabilistic clinical studies and deterministic physiology: building a Monte Carlo bridge with the "Frank" project.
AU - Felipe Coelho Argolo
AU - Arthur Vasconcelos de Luna Freire
AU - Larrie Rabelo Laporte
AU - Guilherme Moreira Magnavita
AU - Luís Cláudio Lemos Correia
PY - 2026
DA - 2026/10/19
JO - Einstein (Sao Paulo, Brazil)
DO - 10.31744/einstein_journal/2026ae2239
UR - https://europepmc.org/article/MED/42912034
AB - 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.
SN - 1679-4508
SN - 2317-6385
ER -