Evaluating Objective Metrics of habit strength for taking medications.

Habit strength for taking medication is associated with medication adherence. However, habit strength is typically measured via self-reports, which have limitations. Objective sensors may provide advantages to self-reports. To evaluate whether habit-strength metrics derived from objective sensor data (MEMS® Caps; AARDEX Group) are associated with self-reported habit strength and adherence (objective and self-reported) and whether objective and self-reported habit strength are independently associated with adherence. Patients (N = 79) on oral medications for type 2 diabetes completed self-reports of habit strength and medication adherence and used MEMS® Caps to take their prescribed medication for one month. MEMS® Caps data were used to create five objective metrics of habit strength (e.g., individual-level variance in pill timing) and quantify medication adherence (% days correct dosing). Consistency in behavior from week to week (versus across each day) had the greatest association with self-reported habit strength (r(78) = 0.29, p = 0.01), self-reported adherence (r(78) = 0.32, p = 0.005), and objective adherence (r(78) = 0.61, p < 0.001). Objective and self-reported habit strength were independently associated with adherence. Weekly pill-timing consistency may be more useful than daily pill-timing consistency for predicting adherence and understanding patients' medication-taking habits. Self-reports and objective metrics of habit strength may be measuring different constructs, warranting further research.

Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them.

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

Improved pressure contour analysis for estimating cardiac stroke volume using pulse wave velocity measurement.

BACKGROUND: Pressure contour analysis is commonly used to estimate cardiac performance for patients suffering from cardiovascular dysfunction in the intensive care unit. However, the existing techniques for continuous estimation of stroke volume (SV) from pressure measurement can be unreliable during hemodynamic instability, which is inevitable for patients requiring significant treatment. For this reason, pressure contour methods must be improved to capture changes in vascular properties and thus provide accurate conversion from pressure to flow. METHODS: This paper presents a novel pressure contour method utilizing pulse wave velocity (PWV) measurement to capture vascular properties. A three-element Windkessel model combined with the reservoir-wave concept are used to decompose the pressure contour into components related to storage and flow. The model parameters are identified beat-to-beat from the water-hammer equation using measured PWV, wave component of the pressure, and an estimate of subject-specific aortic dimension. SV is then calculated by converting pressure to flow using identified model parameters. The accuracy of this novel method is investigated using data from porcine experiments (N = 4 Pietrain pigs, 20-24.5 kg), where hemodynamic properties were significantly altered using dobutamine, fluid administration, and mechanical ventilation. In the experiment, left ventricular volume was measured using admittance catheter, and aortic pressure waveforms were measured at two locations, the aortic arch and abdominal aorta. RESULTS: Bland-Altman analysis comparing gold-standard SV measured by the admittance catheter and estimated SV from the novel method showed average limits of agreement of ±26% across significant hemodynamic alterations. This result shows the method is capable of estimating clinically acceptable absolute SV values according to Critchely and Critchely. CONCLUSION: The novel pressure contour method presented can accurately estimate and track SV even when hemodynamic properties are significantly altered. Integrating PWV measurements into pressure contour analysis improves identification of beat-to-beat changes in Windkessel model parameters, and thus, provides accurate estimate of blood flow from measured pressure contour. The method has great potential for overcoming weaknesses associated with current pressure contour methods for estimating SV.

Minimally invasive, patient specific, beat-by-beat estimation of left ventricular time varying elastance.

BACKGROUND: The aim of this paper was to establish a minimally invasive method for deriving the left ventricular time varying elastance (TVE) curve beat-by-beat, the monitoring of which's inter-beat evolution could add significant new data and insight to improve diagnosis and treatment. The method developed uses the clinically available inputs of aortic pressure, heart rate and baseline end-systolic volume (via echocardiography) to determine the outputs of left ventricular pressure, volume and dead space volume, and thus the TVE curve. This approach avoids directly assuming the shape of the TVE curve, allowing more effective capture of intra- and inter-patient variability. RESULTS: The resulting TVE curve was experimentally validated against the TVE curve as derived from experimentally measured left ventricular pressure and volume in animal models, a data set encompassing 46,318 heartbeats across 5 Piétrain pigs. This simulated TVE curve was able to effectively approximate the measured TVE curve, with an overall median absolute error of 11.4% and overall median signed error of -2.5%. CONCLUSIONS: The use of clinically available inputs means there is potential for real-time implementation of the method at the patient bedside. Thus the method could be used to provide additional, patient specific information on intra- and inter-beat variation in heart function.

The Importance of Assessing Drug Exposure and Medication Adherence in Evaluating Investigational Medications: Ensuring Validity and Reliability of Clinical Trial Results.

The objective of this current opinion paper is to draw global attention to medication adherence, emphasizing its crucial role in drug trials. Frequently, trialists lean on traditional approaches to assess medication adherence, which, while comfortable, may only reveal what trialists desire rather than offering the essential insights needed for informed decision making in drug development. Understanding drug exposure and medication adherence is paramount when evaluating the effectiveness and safety of investigational medications. Without a comprehensive understanding of how patients adhere to their prescribed treatment regimens, the integrity and dependability of clinical trial results can be compromised. This paper emphasizes the need for measures that accurately and reliably assess medication intake behaviors, enabling the differentiation between minor dosing errors and significant deviations that may impact the drug's efficacy and safety. Accurate knowledge of drug exposure empowers researchers to make informed decisions, identify potential confounding factors, and appropriately interpret study outcomes, ultimately ensuring the validity and reliability of the research findings. By prioritizing drug exposure assessment and medication adherence measurement, clinical trials can enhance their scientific rigor, contribute to more accurate evaluations of investigational medications, and ultimately speed up the development process.

A multi-scale cardiovascular system model can account for the load-dependence of the end-systolic pressure-volume relationship.

BACKGROUND: The end-systolic pressure-volume relationship is often considered as a load-independent property of the heart and, for this reason, is widely used as an index of ventricular contractility. However, many criticisms have been expressed against this index and the underlying time-varying elastance theory: first, it does not consider the phenomena underlying contraction and second, the end-systolic pressure volume relationship has been experimentally shown to be load-dependent. METHODS: In place of the time-varying elastance theory, a microscopic model of sarcomere contraction is used to infer the pressure generated by the contraction of the left ventricle, considered as a spherical assembling of sarcomere units. The left ventricle model is inserted into a closed-loop model of the cardiovascular system. Finally, parameters of the modified cardiovascular system model are identified to reproduce the hemodynamics of a normal dog. RESULTS: Experiments that have proven the limitations of the time-varying elastance theory are reproduced with our model: (1) preload reductions, (2) afterload increases, (3) the same experiments with increased ventricular contractility, (4) isovolumic contractions and (5) flow-clamps. All experiments simulated with the model generate different end-systolic pressure-volume relationships, showing that this relationship is actually load-dependent. Furthermore, we show that the results of our simulations are in good agreement with experiments. CONCLUSIONS: We implemented a multi-scale model of the cardiovascular system, in which ventricular contraction is described by a detailed sarcomere model. Using this model, we successfully reproduced a number of experiments that have shown the failing points of the time-varying elastance theory. In particular, the developed multi-scale model of the cardiovascular system can capture the load-dependence of the end-systolic pressure-volume relationship.

Electronically monitored medication adherence in idiopathic pulmonary fibrosis: prevalence, predictors and outcomes.

Medication adherence studies in idiopathic pulmonary fibrosis (IPF) are limited, use cross-sectional designs and report discontinuation rates. We prospectively investigated adherence to pirfenidone in IPF patients using electronic monitoring, which provides insights on whether and when the medication was taken on a day-by-day basis. We investigated the impact of nonadherence on lung function and selected predictors for nonadherence based on the COM-B behavioural model. The longitudinal statistical analyses included generalised estimation equations and linear mixed effects models. 55 patients initiating pirfenidone were followed-up for 2 years after diagnosis (76.4% men, mean age 71.1 years (range 50-87 years), mean forced vital capacity (FVC) 88% predicted (sd 18.3), mean diffusing capacity of the lung for carbon monoxide (D LCO) 58.1% predicted (sd 14.7)). Our data showed an association (p=0.03) between the proportion of days with three pirfenidone intakes (i.e. dosing adherence) and FVC % predicted, whereby a high dosing adherence seemed necessary to maintain stable or improving FVC % predicted values. 58.2% of the participants were able to implement at least 90% correct dosing days, yet adherence significantly decreased over time. Too short dosing intervals had negative effects on lung function outcomes. Knowledge on IPF and self-reported adherence were significantly associated with electronically measured adherence. In conclusion, nonadherence is prevalent and might negatively affect lung function. Further research is needed on the impact of nonadherence on outcomes and its predictors, so that tailored interventions can be developed. Meanwhile, a self-report questionnaire could be used to identify adherence issues and teams should equip patients with knowledge about their treatment and how to take it.

Machine Learning-Based Extraction of Breast Cancer Receptor Status From Bilingual Free-Text Pathology Reports.

As part of its core business of gathering population-based information on new cancer diagnoses, the Belgian Cancer Registry receives free-text pathology reports, describing results of (pre-)malignant specimens. These reports are provided by 82 laboratories and written in 2 national languages, Dutch or French. For breast cancer, the reports characterize the status of estrogen receptor, progesterone receptor, and Erb-b2 receptor tyrosine kinase 2. These biomarkers are related with tumor growth and prognosis and are essential to define therapeutic management. The availability of population-scale information about their status in breast cancer patients can therefore be considered crucial to enrich real-world scientific studies and to guide public health policies regarding personalized medicine. The main objective of this study is to expand the data available at the Belgian Cancer Registry by automatically extracting the status of these biomarkers from the pathology reports. Various types of numeric features are computed from over 1,300 manually annotated reports linked to breast tumors diagnosed in 2014. A range of popular machine learning classifiers, such as support vector machines, random forests and logistic regressions, are trained on this data and compared using their F 1 scores on a separate validation set. On a held-out test set, the best performing classifiers achieve F 1 scores ranging from 0.89 to 0.92 for the four classification tasks. The extraction is thus reliable and allows to significantly increase the availability of this valuable information on breast cancer receptor status at a population level.

Practical identifiability analysis of a minimal cardiovascular system model.

BACKGROUND AND OBJECTIVE: Parameters of mathematical models of the cardiovascular system can be used to monitor cardiovascular state, such as total stressed blood volume status, vessel elastance and resistance. To do so, the model parameters have to be estimated from data collected at the patient's bedside. This work considers a seven-parameter model of the cardiovascular system and investigates whether these parameters can be uniquely determined using indices derived from measurements of arterial and venous pressures, and stroke volume. METHODS: An error vector defined the residuals between the simulated and reference values of the seven clinically available haemodynamic indices. The sensitivity of this error vector to each model parameter was analysed, as well as the collinearity between parameters. To assess practical identifiability of the model parameters, profile-likelihood curves were constructed for each parameter. RESULTS: Four of the seven model parameters were found to be practically identifiable from the selected data. The remaining three parameters were practically non-identifiable. Among these non-identifiable parameters, one could be decreased as much as possible. The other two non-identifiable parameters were inversely correlated, which prevented their precise estimation. CONCLUSIONS: This work presented the practical identifiability analysis of a seven-parameter cardiovascular system model, from limited clinical data. The analysis showed that three of the seven parameters were practically non-identifiable, thus limiting the use of the model as a monitoring tool. Slight changes in the time-varying function modeling cardiac contraction and use of larger values for the reference range of venous pressure made the model fully practically identifiable.

Parameter estimation in a minimal model of cardio-pulmonary interactions.

Mechanical ventilation is a widely used breathing support for patients in intensive care. Its effects on the respiratory and cardiovascular systems are complex and difficult to predict. This work first presents a minimal mathematical model representing the mechanics of both systems and their interaction, in terms of flows, pressures and volumes. The aim of this model is to get insight on the two systems' status when mechanical ventilation settings, such as positive end-expiratory pressure, are changing. The parameters of the model represent cardiac elastances and vessel compliances and resistances. As a second step, these parameters are estimated from 16 experimental datasets. The data come from three pig experiments reproducing intensive care conditions, where a large range of positive end-expiratory pressures was imposed by the mechanical ventilator. The data used for parameter estimation is limited to information available in the intensive care unit, such as stroke volume, central venous pressure and systemic arterial pressure. The model is able to satisfactorily reproduce this experimental data, with mean relative errors ranging from 1 to 26%. The model also reproduces the dynamics of the cardio-vascular and respiratory systems, and their interaction. By looking at the estimated parameter values, one can quantitatively track how the two coupled systems mechanically react to changes in external conditions imposed by the ventilator. This work thus allows real-time, model-based management of ventilator settings.

Pre-ejection period, the reason why the electrocardiogram Q-wave is an unreliable indicator of pulse wave initialization.

OBJECTIVE: Pulse wave velocity measurements are an indicator of arterial stiffness and possible cardiovascular dysfunction. It is usually calculated by measuring the pulse transit time (PTT) over a known distance through the arteries. In animal studies, reliable PTT measures can be obtained using two pressure catheters. However, such direct, invasive methods are undesirable in clinical settings. A less invasive alternative measure of PTT is pulse arrival time (PAT), the time between the Q-wave of an electrocardiogram (ECG) and the arrival of the foot of the beats pressure waveform at one pressure catheter. Since the Q-wave signifies the start of ventricular contraction, PAT includes the pre-ejection period (PEP), a time where no blood is ejected. Thus, inter- or intra- subject variation in PEP could result in poor correlation between pulse arrival time (PAT) and the desired pulse transit time (PTT). APPROACH: This study looks at the relationship between PAT and PTT, over a range of common critical care therapies and determines the effect of PEP on PAT as a possible surrogate of PTT in a critical care environment. The analysis uses data from five porcine experiments, where ECG, aortic arch and abdominal aortic pressure were measured simultaneously, over a range of induced hemodynamic conditions. RESULTS: The resulting correlations of PAT verse PTT varied within pigs and across interventions (r 2 = 0.32-0.69), and across pigs (r 2 = 0.05-0.60). Variability was due to three main causes. First, the interventions themselves effect PEP and PTT differently, second, pig specific response to the interventions, and third, inter- and intra- pig variability in PEP, independent of PTT. SIGNIFICANCE: The overall analysis shows PAT is an unreliable measure of PTT and a poor surrogate under clinical interventions common in a critical care setting, due to intra- and inter- subject variability in PEP.

Minimally invasive estimation of ventricular dead space volume through use of Frank-Starling curves.

This paper develops a means of more easily and less invasively estimating ventricular dead space volume (Vd), an important, but difficult to measure physiological parameter. Vd represents a subject and condition dependent portion of measured ventricular volume that is not actively participating in ventricular function. It is employed in models based on the time varying elastance concept, which see widespread use in haemodynamic studies, and may have direct diagnostic use. The proposed method involves linear extrapolation of a Frank-Starling curve (stroke volume vs end-diastolic volume) and its end-systolic equivalent (stroke volume vs end-systolic volume), developed across normal clinical procedures such as recruitment manoeuvres, to their point of intersection with the y-axis (where stroke volume is 0) to determine Vd. To demonstrate the broad applicability of the method, it was validated across a cohort of six sedated and anaesthetised male Pietrain pigs, encompassing a variety of cardiac states from healthy baseline behaviour to circulatory failure due to septic shock induced by endotoxin infusion. Linear extrapolation of the curves was supported by strong linear correlation coefficients of R = 0.78 and R = 0.80 average for pre- and post- endotoxin infusion respectively, as well as good agreement between the two linearly extrapolated y-intercepts (Vd) for each subject (no more than 7.8% variation). Method validity was further supported by the physiologically reasonable Vd values produced, equivalent to 44.3-53.1% and 49.3-82.6% of baseline end-systolic volume before and after endotoxin infusion respectively. This method has the potential to allow Vd to be estimated without a particularly demanding, specialised protocol in an experimental environment. Further, due to the common use of both mechanical ventilation and recruitment manoeuvres in intensive care, this method, subject to the availability of multi-beat echocardiography, has the potential to allow for estimation of Vd in a clinical environment.

Structural identifiability analysis of a cardiovascular system model.

The six-chamber cardiovascular system model of Burkhoff and Tyberg has been used in several theoretical and experimental studies. However, this cardiovascular system model (and others derived from it) are not identifiable from any output set. In this work, two such cases of structural non-identifiability are first presented. These cases occur when the model output set only contains a single type of information (pressure or volume). A specific output set is thus chosen, mixing pressure and volume information and containing only a limited number of clinically available measurements. Then, by manipulating the model equations involving these outputs, it is demonstrated that the six-chamber cardiovascular system model is structurally globally identifiable. A further simplification is made, assuming known cardiac valve resistances. Because of the poor practical identifiability of these four parameters, this assumption is usual. Under this hypothesis, the six-chamber cardiovascular system model is structurally identifiable from an even smaller dataset. As a consequence, parameter values computed from limited but well-chosen datasets are theoretically unique. This means that the parameter identification procedure can safely be performed on the model from such a well-chosen dataset. Thus, the model may be considered suitable for use in diagnosis.

Model-based computation of total stressed blood volume from a preload reduction manoeuvre.

Total stressed blood volume is an important parameter for both doctors and engineers. From a medical point of view, it has been associated with the success or failure of fluid therapy, a primary treatment to manage acute circulatory failure. From an engineering point of view, it dictates the cardiovascular system's behavior in changing physiological situations. Current methods to determine this parameter involve repeated phases of circulatory arrests followed by fluid administration. In this work, a more straightforward method is developed using data from a preload reduction manoeuvre. A simple six-chamber cardiovascular system model is used and its parameters are adjusted to pig experimental data. The parameter adjustment process has three steps: (1) compute nominal values for all model parameters; (2) determine the five most sensitive parameters; and (3) adjust only these five parameters. Stressed blood volume was selected by the algorithm, which emphasizes the importance of this parameter. The model was able to track experimental trends with a maximal root mean squared error of 29.2%. Computed stressed blood volume equals 486 ± 117 ml or 15.7 ± 3.6 ml/kg, which matches previous independent experiments on pigs, dogs and humans. The method proposed in this work thus provides a simple way to compute total stressed blood volume from usual hemodynamic data.

Modelling of the nonlinear end-systolic pressure-volume relation and volume-at-zero-pressure in porcine experiments.

The End-Systolic Pressure-Volume Relation (ESPVR) is generally modelled as a linear relationship between P and V as cardiac reflexes, such as the baroreflex, are typically suppressed in experiments. However, ESPVR has been observed to behave in a curvilinear fashion when cardiac reflexes are not suppressed, suggesting the curvilinear function may be more clinically appropriate. Data was gathered from 41 vena cava occlusion manoeuvres performed experimentally at a variety of PEEPs across 6 porcine specimens, and ESPVR determined for each pig. An exponential model of ESPVR was found to provide a higher correlation coefficient than a linear model in 6 out of 7 cases, and a lower Akaike Information Criterion (AIC) value in all cases. Further, the exponential ESPVR provided positive V0 values in a physiological range in 6 out of 7 cases analysed, while the linear ESPVR produced positive V0 values in only 3 out of 7 cases, suggesting linear extrapolation of ESPVR to determine V0 may be flawed.

Early detection of abnormal left ventricular relaxation in acute myocardial ischemia with a quadratic model.

AIMS: The time constant of left ventricular (LV) relaxation derived from a monoexponential model is widely used as an index of LV relaxation rate, although this model does not reflect the non-uniformity of ventricular relaxation. This study investigates whether the relaxation curve can be better fitted with a "quadratic" model than with the "conventional" monoexponential model and if changes in the LV relaxation waveform due to acute myocardial ischemia could be better detected with the quadratic model. METHODS AND RESULTS: Isovolumic relaxation was assessed with quadratic and conventional models during acute myocardial ischemia performed in 6 anesthetized pigs. Mathematical development indicates that one parameter (Tq) of the quadratic model reflects the rate of LV relaxation, while the second parameter (K) modifies the shape of the relaxation curve. Analysis of experimental data obtained in anesthetized pigs showed that the shape of LV relaxation consistently deviates from the conventional monoexponential decay. During the early phase of acute myocardial ischemia, the rate and non-uniformity of LV relaxation, assessed with the quadratic function, were significantly enhanced. Tq increased by 16% (p<0.001) and K increased by 12% (p<0.001) within 30 and 60 min, respectively, after left anterior descending (LAD) coronary artery occlusion. However, no significant changes were observed with the conventional monoexponential decay within 60 min of ischemia. CONCLUSIONS: The quadratic model better fits LV isovolumic relaxation than the monoexponential model and can detect early changes in relaxation due to acute myocardial ischemia that are not detectable with conventional methods.

Simulation of left atrial function using a multi-scale model of the cardiovascular system.

During a full cardiac cycle, the left atrium successively behaves as a reservoir, a conduit and a pump. This complex behavior makes it unrealistic to apply the time-varying elastance theory to characterize the left atrium, first, because this theory has known limitations, and second, because it is still uncertain whether the load independence hypothesis holds. In this study, we aim to bypass this uncertainty by relying on another kind of mathematical model of the cardiac chambers. In the present work, we describe both the left atrium and the left ventricle with a multi-scale model. The multi-scale property of this model comes from the fact that pressure inside a cardiac chamber is derived from a model of the sarcomere behavior. Macroscopic model parameters are identified from reference dog hemodynamic data. The multi-scale model of the cardiovascular system including the left atrium is then simulated to show that the physiological roles of the left atrium are correctly reproduced. This include a biphasic pressure wave and an eight-shaped pressure-volume loop. We also test the validity of our model in non basal conditions by reproducing a preload reduction experiment by inferior vena cava occlusion with the model. We compute the variation of eight indices before and after this experiment and obtain the same variation as experimentally observed for seven out of the eight indices. In summary, the multi-scale mathematical model presented in this work is able to correctly account for the three roles of the left atrium and also exhibits a realistic left atrial pressure-volume loop. Furthermore, the model has been previously presented and validated for the left ventricle. This makes it a proper alternative to the time-varying elastance theory if the focus is set on precisely representing the left atrial and left ventricular behaviors.