Model selection for ventricular mechanics: a sensitivity analysis approach

Predictive Modeling of Secondary Pulmonary Hypertension in Left Ventricular Diastolic Dysfunction

Diastolic dysfunction is a common pathology occurring in about one third of patients affected by heart failure. This condition may not be associated with a marked decrease in cardiac output or systemic pressure and therefore is more difficult to diagnose than its systolic counterpart. Compromised relaxation or increased stiffness of the left ventricle induces an increase in the upstream pulmonary pressures, and is classified as secondary or group II pulmonary hypertension (2018 Nice classification). This may result in an increase in the right ventricular afterload leading to right ventricular failure. Elevated pulmonary pressures are therefore an important clinical indicator of diastolic heart failure (sometimes referred to as heart failure with preserved ejection fraction, HFpEF), showing significant correlation with associated mortality. However, accurate measurements of this quantity are typically obtained through invasive catheterization and after the onset of symptoms. In this study, we use the hemodynamic consistency of a differential-algebraic circulation model to predict pulmonary pressures in adult patients from other, possibly non-invasive, clinical data. We investigate several aspects of the problem, including the ability of model outputs to represent a sufficiently wide pathologic spectrum, the identifiability of the model's parameters, and the accuracy of the predicted pulmonary pressures. We also find that a classifier using the assimilated model parameters as features is free from the problem of missing data and is able to detect pulmonary hypertension with sufficiently high accuracy. For a cohort of 82 patients suffering from various degrees of heart failure severity, we show that systolic, diastolic, and wedge pulmonary pressures can be estimated on average within 8, 6, and 6 mmHg, respectively. We also show that, in general, increased data availability leads to improved predictions.

Reperfusion Treatment and Stroke Outcomes in Hospitals With Telestroke Capacity

Importance

Telestroke is increasingly used in hospital emergency departments, but there has been limited research on its impact on treatment and outcomes.

Objective

To describe differences in care patterns and outcomes among patients with acute ischemic stroke who present to hospitals with and without telestroke capacity.

Design, Setting, and Participants

Patients with acute ischemic stroke who first presented to hospitals with telestroke capacity were matched with patients who presented to control hospitals without telestroke capacity. All traditional Medicare beneficiaries with a primary diagnosis of acute ischemic stroke (approximately 2.5 million) who presented to a hospital between January 2008 and June 2017 were considered. Matching was based on sociodemographic and clinical characteristics, hospital characteristics, and month and year of admission. Hospitals included short-term acute care and critical access hospitals in the US without local stroke expertise. In 643 hospitals with telestroke capacity, there were 76 636 patients with stroke who were matched 1:1 to patients at similar hospitals without telestroke capacity. Data were analyzed in July 2020.

Main Outcomes and Measures

Receipt of reperfusion treatment through thrombolysis with alteplase or thrombectomy, mortality at 30 days from admission, spending through 90 days from admission, and functional status as measured by days spent living in the community after discharge.

Results

In the final sample of 153 272 patients, 88 386 (57.7%) were female, and the mean (SD) age was 78.8 (10.4) years. Patients cared for at telestroke hospitals had higher rates of reperfusion treatment compared with those cared for at control hospitals (6.8% vs 6.0%; difference, 0.78 percentage points; 95% CI, 0.54-1.03; P < .001) and lower 30-day mortality (13.1% vs 13.6%; difference, 0.50 percentage points; 95% CI, 0.17-0.83, P = .003). There were no differences in days spent living in the community following discharge or in spending. Increases in reperfusion treatment were largest in the lowest-volume hospitals, among rural residents, and among patients 85 years and older.

Conclusions and Relevance

Patients with ischemic stroke treated at hospitals with telestroke capacity were more likely to receive reperfusion treatment and have lower 30-day mortality.

Patient-specific parameter estimation in single-ventricle lumped circulation models under uncertainty

Computational models of cardiovascular physiology can inform clinical decision-making, providing a physically consistent framework to assess vascular pressures and flow distributions, and aiding in treatment planning. In particular, lumped parameter network (LPN) models that make an analogy to electrical circuits offer a fast and surprisingly realistic method to reproduce the circulatory physiology. The complexity of LPN models can vary significantly to account, for example, for cardiac and valve function, respiration, autoregulation, and time-dependent hemodynamics. More complex models provide insight into detailed physiological mechanisms, but their utility is maximized if one can quickly identify patient specific parameters. The clinical utility of LPN models with many parameters will be greatly enhanced by automated parameter identification, particularly if parameter tuning can match non-invasively obtained clinical data. We present a framework for automated tuning of 0D lumped model parameters to match clinical data. We demonstrate the utility of this framework through application to single ventricle pediatric patients with Norwood physiology. Through a combination of local identifiability, Bayesian estimation and maximum a posteriori simplex optimization, we show the ability to automatically determine physiologically consistent point estimates of the parameters and to quantify uncertainty induced by errors and assumptions in the collected clinical data. We show that multi-level estimation, that is, updating the parameter prior information through sub-model analysis, can lead to a significant reduction in the parameter marginal posterior variance. We first consider virtual patient conditions, with clinical targets generated through model solutions, and second application to a cohort of four single-ventricle patients with Norwood physiology. Copyright © 2016 John Wiley & Sons, Ltd.

Temporal relationship between left ventricular and arterial system elastances

Arterial compliance is an important component of ventricular afterload. Although its pressure dependence has been recognized, its temporal relationship to ventricular elastance (Elv(t)) has not been established. We investigated this in five open chest anesthetized dogs where simultaneous aortic pressure and flow and left ventricular pressure were measured. Elv(t) was derived using an elastance-resistance model of the left ventricle assuming an ejection fraction of 0.50 and a dead volume (Vd) of 3.0 mL. The nonlinear pressure-dependent compliance (C(P)) of the arterial system was incorporated in a three-element Windkessel model and determined by accurate prediction of aortic pressure from aortic flow. The resulting arterial elastance (Eas(t)) was computed as Eas(t) = 1/C(P). Results show that Eas(t) reaches a minimum value at or near the start of ventricular ejection and attains its peak value at or near the same time maximum LV elastance (Emax) is reached, at end-systole. Finally, numerical simulation of the model demonstrates its ability to adequately reproduce measured pressure and flow. Thus, the arterial system, in terms of elastance, is dynamically and temporally coupled to the left-ventricle during ejection.