Sensitivity analysis and parameter estimation of a coronary circulation model for triple-vessel disease

Enhancing cardiac assessments: accurate and efficient prediction of quantitative fractional flow reserve

Background

Obstruction within the left anterior descending coronary artery (LAD) is prevalent, serving as a prominent and independent predictor of mortality. Invasive Fractional flow reserve (FFR) is the gold standard for Coronary Artery Disease risk assessment. Despite advances in computational and imaging techniques, no definitive methodology currently assures clinicians of reliable, non-invasive strategies for future planning.

Method

The present research encompassed a cohort of 150 participants who were admitted to the Rajaie Cardiovascular, Medical, and Research Center. The method includes a three-dimensional geometry reconstruction, computational fluid dynamics simulations, and methodology optimization for the computation time. Four patients are analyzed within this study to showcase the proposed methodology. The invasive FFR results reported by the clinic have validated the optimized model.

Results

The computational FFR data derived from all methodologies are compared with those reported by the clinic for each case. The chosen methodology has yielded virtual FFR values that exhibit remarkable proximity to the clinically reported patient-specific FFR values, with the MSE of 6.186e-7 and R2 of 0.99 (p = 0.00434).

Conclusion

This approach has shown reliable results for all 150 patients. The results are both computationally and clinically user-friendly, with the accumulative pre and post-processing time of 15 min on a desktop computer (Intel i7 processor, 16 GB RAM). The proposed methodology has the potential to significantly assist clinicians with diagnosis.

Characterization of cardiac resynchronization therapy response through machine learning and personalized models

INTRODUCTION

The characterization and selection of heart failure (HF) patients for cardiac resynchronization therapy (CRT) remain challenging, with around 30% non-responder rate despite following current guidelines. This study aims to propose a novel hybrid approach, integrating machine-learning and personalized models, to identify explainable phenogroups of HF patients and predict their CRT response.

METHODS

The paper proposes the creation of a complete personalized model population based on preoperative CRT patient strain curves. Based on the parameters and features extracted from these personalized models, phenotypes of patients are identified thanks to a clustering algorithm and a random forest classification is provided.

RESULTS

A close match was observed between the 162 experimental and simulated myocardial strain curves, with a mean RMSE of 4.48% (±1.08) for the 162 patients. Five phenogroups of personalized models were identified from the clustering, with response rates ranging from 52% to 94%. The classification results show a mean area under the curves (AUC) of 0.86 ± 0.06 and provided a feature importance analysis with 22 features selected. Results show both regional myocardial contractility (from 22.5% to 33.0%), tissue viability and electrical activation delays importance on CRT response for each HF patient (from 55.8 ms to 88.4 ms).

DISCUSSION

The patient-specific model parameters' analysis provides an explainable interpretation of HF patient phenogroups in relation to physiological mechanisms that seem predictive of the CRT response. These novel combined approaches appear as promising tools to improve understanding of LV mechanical dyssynchrony for HF patient characterization and CRT selection.

Determination of acceptable Hounsfield units uncertainties via a sensitivity analysis for an accurate dose calculation in the context of prostate MRI-only radiotherapy

Radiation therapy is moving from CT based to MRI guided planning, particularly for soft tissue anatomy. An important requirement of this new workflow is the generation of synthetic-CT (sCT) from MRI to enable treatment dose calculations. Automatic methods to determine the acceptable range of CT Hounsfield Unit (HU) uncertainties to avoid dose distribution errors is thus a key step toward safe MRI-only radiotherapy. This work has analysed the effects of controlled errors introduced in CT scans on the delivered radiation dose for prostate cancer patients. Spearman correlation coefficient has been computed, and a global sensitivity analysis performed following the Morris screening method. This allows the classification of different error factors according to their impact on the dose at the isocentre. sCT HU estimation errors in the bladder appeared to be the least influential factor, and sCT quality assessment should not only focus on organs surrounding the radiation target, as errors in other soft tissue may significantly impact the dose in the target volume. This methodology links dose and intensity-based metrics, and is the first step to define a threshold of acceptability of HU uncertainties for accurate dose planning.

How myocardial work could be relevant in patients with an aortic valve stenosis?

AIMS

Myocardial work (MW) calculation is an attractive method to assess left ventricular (LV) myocardial function. In case of aortic stenosis (AS), assessment of work indices is challenging because it requires an accurate evaluation of LV-pressure curves. We sought to evaluate the performances of two distinct methods and to provide a quantitative comparison with invasive data.

METHODS AND RESULTS

Model-based and template-based methods were defined and applied for the evaluation of LV-pressures on 67 AS-patient. Global Constructive (GCW), Wasted (GWW), Positive (GPW), Negative (GNW) MW and Global Work Efficiency (GWE), and Index (GWI) parameters were calculated using the available software computing the indices using brachial blood-pressure and trans-aortic mean pressure gradient (MPG) for estimating the LV-pressures vs. using a model-based and homemade software. A complete comparison was performed with invasive measurements. Patients were characterized by MPG of 49.8 ± 14.8 mmHg, the global longitudinal strain (GLS) was -15.0 ± 4.04%, GCW was 2107 ± 800 mmHg.% (model-based) and 2483 ± 1068 mmHg.% (template-based). The root mean square error (RMSE) and correlation were calculated for each patient and pressure estimation methods. The mean RMSE are 33.9 mmHg and 40.4 mmHg and the mean correlation coefficients are 0.81 and 0.72 for the model-based and template-based methods, respectively. The two methods present correlation coefficient r2 >0.75 for all the indices.

CONCLUSION

The two non-invasive methods of LV pressure estimation and work indices computation correlate with invasive measurements. Although the model-based approach requires less information and is associated with slightly better performances, the implementation of template-based method is easier and is appropriate for clinical practice.

Towards a Reduced In Silico Model Predicting Biochemical Recurrence After Radiotherapy in Prostate Cancer

OBJECTIVE

Purposes of this work were i) to develop an in silico model of tumor response to radiotherapy, ii) to perform an exhaustive sensitivity analysis in order to iii) propose a simplified version and iv) to predict biochemical recurrence with both the comprehensive and the reduced model.

METHODS

A multiscale computational model of tumor response to radiotherapy was developed. It integrated the following radiobiological mechanisms: oxygenation, including hypoxic death; division of tumor cells; VEGF diffusion driving angiogenesis; division of healthy cells and oxygen-dependent response to irradiation, considering, cycle arrest and mitotic catastrophe. A thorough sensitivity analysis using the Morris screening method was performed on 21 prostate computational tissues. Tumor control probability (TCP) curves of the comprehensive model and 15 reduced versions were compared. Logistic regression was performed to predict biochemical recurrence after radiotherapy on 76 localized prostate cancer patients using an output of the comprehensive and the reduced models.

RESULTS

No significant difference was found between the TCP curves of the comprehensive and a simplified version which only considered oxygenation, division of tumor cells and their response to irradiation. Biochemical recurrence predictions using the comprehensive and the reduced models improved those made from pre-treatment imaging parameters (AUC = 0.81 ± 0.02 and 0.82 ± 0.02 vs. 0.75 ± 0.03, respectively).

CONCLUSION

A reduced model of tumor response to radiotherapy able to predict biochemical recurrence in prostate cancer was obtained.

SIGNIFICANCE

This reduced model may be used in the future to optimize personalized fractionation schedules.

Model-based estimation of left ventricular pressure and myocardial work in aortic stenosis

This paper proposes a model-based estimation of left ventricular (LV) pressure for the evaluation of constructive and wasted myocardial work of patients with aortic stenosis (AS). A model of the cardiovascular system is proposed, including descriptions of i) cardiac electrical activity, ii) elastance-based cardiac cavities, iii) systemic and pulmonary circulations and iv) heart valves. After a sensitivity analysis of model parameters, an identification strategy was implemented using a Monte-Carlo cross-validation approach. Parameter identification procedure consists in two steps for the estimation of LV pressures: step 1) from invasive, intraventricular measurements and step 2) from non-invasive data. The proposed approach was validated on data obtained from 12 patients with AS. The total relative errors between estimated and measured pressures were on average 11.9% and 12.27% and mean R² were equal to 0.96 and 0.91, respectively for steps 1 and 2 of parameter identification strategy. Using LV pressures obtained from non-invasive measurements (step 2) and patient-specific simulations, Global Constructive (GCW), Wasted (GWW) myocardial Work and Global Work Efficiency (GWE) parameters were calculated. Correlations between measures and model-based estimations were 0.88, 0.80, 0.91 respectively for GCW, GWW and GWE. The main contributions concern the proposal of the parameter identification procedure, applied on an integrated cardiovascular model, able to reproduce LV pressure specifically to each AS patient, by non-invasive procedures, as well as a new method for the non-invasive estimation of constructive, wasted myocardial work and work efficiency in AS.

Influence of nasal CPAP on cardiorespiratory control in healthy neonate

The present study aimed to further unravel the effects of nasal continuous positive airway pressure (nCPAP) on the cardiovascular and respiratory systems in the neonatal period. Six-hour polysomnographic recordings were first performed in seven healthy newborn lambs, aged 2-3 days, without and with nCPAP application at 6 cmH₂O (nCPAP-6), in randomized order. The effects of nCPAP-6 on heart rate variability, respiratory rate variability, and cardiorespiratory interrelations were analyzed using a semiautomatic signal processing approach applied to ECG and respiration recordings. Thereafter, a cardiorespiratory mathematical model was adapted to the experimental conditions to gain further physiological interpretation and to simulate higher nCPAP levels (8 and 10 cmH₂O). Results from the signal processing approach suggest that nCPAP-6 applied in newborns with healthy lungs: 1) increases heart rate and decreases the time and frequency domain indices of heart rate variability, especially those representing parasympathetic activity, while increasing the complexity of the RR-interval time series; 2) prolongs the respiratory cycle and expiration duration and decreases respiratory rate variability; and 3) slightly impairs cardiorespiratory interrelations. Model-based analysis revealed that nCPAP-6 increases the heart rate and decreases respiratory sinus arrhythmia amplitude, in association with a reduced parasympathetic efferent activity. These results were accentuated when simulating an increased CPAP level. Overall, our results provide a further understanding of the effects of nCPAP in neonates, in the absence of lung disease.NEW & NOTEWORTHY Application of nasal continuous positive airway pressure (CPAP) at 6 cmH₂O, a level very frequently used in newborns, alters heart and respiratory rate variability, as well as cardiorespiratory interrelations in a full-term newborn model without lung disease. Moreover, whereas nasal CPAP at 6 cmH₂O decreases parasympathetic efferent activity, there is no change in sympathetic efferent activity.

Recursive model identification for the analysis of the autonomic response to exercise testing in Brugada syndrome

This paper proposes the integration and analysis of a closed-loop model of the baroreflex and cardiovascular systems, focused on a time-varying estimation of the autonomic modulation of heart rate in Brugada syndrome (BS), during exercise and subsequent recovery. Patient-specific models of 44 BS patients at different levels of risk (symptomatic and asymptomatic) were identified through a recursive evolutionary algorithm. After parameter identification, a close match between experimental and simulated signals (mean error = 0.81%) was observed. The model-based estimation of vagal and sympathetic contributions were consistent with physiological knowledge, enabling to observe the expected autonomic changes induced by exercise testing. In particular, symptomatic patients presented a significantly higher parasympathetic activity during exercise, and an autonomic imbalance was observed in these patients at peak effort and during post-exercise recovery. A higher vagal modulation during exercise, as well as an increasing parasympathetic activity at peak effort and a decreasing vagal contribution during post-exercise recovery could be related with symptoms and, thus, with a worse prognosis in BS. This work proposes the first evaluation of the sympathetic and parasympathetic responses to exercise testing in patients suffering from BS, through the recursive identification of computational models; highlighting important trends of clinical relevance that provide new insights into the underlying autonomic mechanisms regulating the cardiovascular system in BS. The joint analysis of the extracted autonomic parameters and classic electrophysiological markers could improve BS risk stratification.

Recursive Model Identification for the Evaluation of Baroreflex Sensitivity

A method for the recursive identification of physiological models of the cardiovascular baroreflex is proposed and applied to the time-varying analysis of vagal and sympathetic activities. The proposed method was evaluated with data from five newborn lambs, which were acquired during injection of vasodilator and vasoconstrictors and the results show a close match between experimental and simulated signals. The model-based estimation of vagal and sympathetic contributions were consistent with physiological knowledge and the obtained estimators of vagal and sympathetic activities were compared to traditional markers associated with baroreflex sensitivity. High correlations were observed between traditional markers and model-based indices.