Modeling patient-specific desaturation patterns in sleep apnea

Modeling Patient-Specific Apnea-Bradycardia Patterns in Preterm Newborn

OBJECTIVE

Preterm infants are particularly exposed to severe cardio-respiratory events, associating apnea with bradycardia and oxygen desaturation. A patient-specific and event-specific model-based approach is proposed in this work to analyze the acute heart rate response to apnea-bradycardia events in preterm newborn.

METHODS

A novel model integrating the main cardio-respiratory interactions which are specific to the neonatal period is proposed. An evolutionary algorithm is applied to estimate patient-specific model parameters from a database of 37 apnea-bradycardia episodes acquired from 10 preterm newborns. Unsupervised clustering (K-means) was applied to the identified parameters to define phenogroups of cardio-respiratory responses to apnea.

RESULTS

A significant correspondence was observed between simulated and experimental heart rate series in all identifications (median RMSE = 8.85 bpm). Three clusters of parameters were found and were associated to three different pathophysiological dynamics related to apnea-bradycardia.

CONCLUSION AND SIGNIFICANCE

The proposed method, based on patient and event-specific model parameter identification, provides a novel approach to characterize bradycardia dynamics in response to apnea, opening the way to the proposal of new personalized diagnosis and treatment possibilities in this particularly sensitive population.

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.

Parametric analysis of an integrated cardio-respiratory model in preterm newborns during apnea

The analysis of the complex interactions involved in the acute physiological response to apnea-bradycardia events in preterm newborns remains a challenging task. This paper presents a novel integrated model of cardio-respiratory interactions, adapted to preterm newborns. A sensitivity analysis, based Morris' screening method, was applied to study the effects of physiological parameters on heart rate and desaturation, during the simulation of a 15-seconds apnea-bradycardia episode. The most sensitive parameters are associated with fundamental, integrative physiological mechanisms involving: (i) respiratory mechanics (intermediate airways and lung compliance), (ii) fraction of inspired oxygen, (iii) metabolic rates (oxygen consumption rate), (iv) heart rate regulation and (v) chemoreflex (gain). Results highlight the relevant influence of physiological variables, involved in preterm apnea-bradycardia events.

A Dynamic Fitting Strategy for Physiological Models: A Case Study of a Cardiorespiratory Model for the Simulation of Incremental Aerobic Exercise

Using mathematical models of physiological systems in medicine has allowed for the development of diagnostic, treatment, and medical educational tools. However, their complexity restricts, in most cases, their application for predictive, preventive, and personalized purposes. Although there are strategies that reduce the complexity of applying models based on fitting techniques, most of them are focused on a single instant of time, neglecting the effect of the system's temporal evolution. The objective of this research was to introduce a dynamic fitting strategy for physiological models with an extensive array of parameters and a constrained amount of experimental data. The proposed strategy focused on obtaining better predictions based on the temporal trends in the system's parameters and being capable of predicting future states. The study utilized a cardiorespiratory model as a case study. Experimental data from a longitudinal study of healthy adult subjects undergoing aerobic exercise were used for fitting and validation. The model predictions obtained in a steady state using the proposed strategy and the traditional single-fit approach were compared. The most successful outcomes were primarily linked to the proposed strategy, exhibiting better overall results regarding accuracy and behavior than the traditional population fitting approach at a single instant in time. The results evidenced the usefulness of the dynamic fitting strategy, highlighting its use for predictive, preventive, and personalized applications.

Desynchronization Strain Patterns and Contractility in Left Bundle Branch Block through Computer Model Simulation

Left bundle branch block (LBBB) is associated with specific septal-to-lateral wall activation patterns which are strongly influenced by the intrinsic left ventricular (LV) contractility and myocardial scar localization. The objective of this study was to propose a computational-model-based interpretation of the different patterns of LV contraction observed in the case of LBBB and preserved contractility or myocardial scarring. Two-dimensional transthoracic echocardiography was used to obtain LV volumes and deformation patterns in three patients with LBBB: (1) a patient with non-ischemic dilated cardiomyopathy, (2) a patient with antero-septal myocardial scar, and (3) a patient with lateral myocardial scar. Scar was confirmed by the distribution of late gadolinium enhancement with cardiac magnetic resonance imaging (cMRI). Model parameters were evaluated manually to reproduce patient-derived data such as strain curves obtained from echocardiographic apical views. The model was able to reproduce the specific strain patterns observed in patients. A typical septal flash with pre-ejection shortening, rebound stretch, and delayed lateral wall activation was observed in the case of non-ischemic cardiomyopathy. In the case of lateral scar, the contractility of the lateral wall was significantly impaired and septal flash was absent. In the case of septal scar, septal flash and rebound stretch were also present as previously described in the literature. Interestingly, the model was also able to simulate the specific contractile properties of the myocardium, providing an excellent localization of LV scar in ischemic patients. The model was able to simulate the electromechanical delay and specific contractility patterns observed in patients with LBBB of ischemic and non-ischemic etiology. With further improvement and validation, this technique might be a useful tool for the diagnosis and treatment planning of heart failure patients needing CRT.