ArticlePLoS computational biology2022
An in-silico analysis of experimental designs to study ventricular function: A focus on the right ventricle.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Developing a Digital Twin of the Cardiopulmonary System in a Mouse: Inferring Hemodynamics from Sparse Measurements.Annals of biomedical engineering · 2026Article
- Multiscale computational modeling of the cardiopulmonary consequences of postnatal hyperoxia with implications for preterm-born children.Biomechanics and modeling in mechanobiology · 2026Article
- Subject-specific modeling of response to physical stress via hypothalamic-pituitary-adrenal and sympathoadrenal axes.PloS one · 2026Article
- Multiscale Computational Modeling of the Cardiopulmonary Consequences of Postnatal Hyperoxia with Implications for Preterm Born Children.bioRxiv : the preprint server for biology · 2025Article
- Assessing parameter identifiability of a hemodynamics PDE model using spectral surrogates and dimension reduction.PLoS computational biology · 2025Article
- The impact of experimental designs & system sloppiness on the personalisation process: A cardiovascular perspective.PloS one · 2025Article
- Estimation of pre-Fontan pulmonary vascular resistance in children with single ventricle heart disease at the Glenn stage: a multicenter study.Frontiers in pediatrics · 2025Article
- Synergistic biophysics and machine learning modeling to rapidly predict cardiac growth probability.Computers in biology and medicine · 2025Article
- Guidelines for mechanistic modeling and analysis in cardiovascular research.American journal of physiology. Heart and circulatory physiology · 2024Review
- Synergistic Biophysics and Machine Learning Modeling to Rapidly Predict Cardiac Growth Probability.bioRxiv : the preprint server for biology · 2024Article
- Parameter subset reduction for imaging-based digital twin generation of patients with left ventricular mechanical discoordination.Biomedical engineering online · 2024Article
- Biventricular Interaction During Acute Left Ventricular Ischemia in Mice: A Combined In-Vivo and In-Silico Approach.Annals of biomedical engineering · 2023Article
- Thoracoabdominal asynchrony in a virtual preterm infant: computational modeling and analysis.American journal of physiology. Lung cellular and molecular physiology · 2023Article
- Biventricular interaction during acute left ventricular ischemia in mice: a combined in-vivo and in-silico approach.bioRxiv : the preprint server for biology · 2023Article
- Review of cardiac-coronary interaction and insights from mathematical modeling.WIREs mechanisms of diseaseReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
In-vivo studies of pulmonary vascular disease and pulmonary hypertension (PH) have provided key insight into the progression of right ventricular (RV) dysfunction. Additional in-silico experiments using multiscale computational models have provided further details into biventricular mechanics and hemodynamic function in the presence of PH, yet few have assessed whether model parameters are practically identifiable prior to data collection. Moreover, none have used modeling to devise synergistic experimental designs. To address this knowledge gap, we conduct a practical identifiability analysis of a multiscale cardiovascular model across four simulated experimental designs. We determine a set of parameters using a combination of Morris screening and local sensitivity analysis, and test for practical identifiability using profile likelihood-based confidence intervals. We employ Markov chain Monte Carlo (MCMC) techniques to quantify parameter and model forecast uncertainty in the presence of noise corrupted data. Our results show that model calibration to only RV pressure suffers from practical identifiability issues and suffers from large forecast uncertainty in output space. In contrast, parameter and model forecast uncertainty is substantially reduced once additional left ventricular (LV) pressure and volume data is included. A comparison between single point systolic and diastolic LV data and continuous, time-dependent LV pressure-volume data reveals that at least some quantitative data from both ventricles should be included for future experimental studies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.