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Positive end-expiratory pressure results in a sustained positive intrathoracic pressure, which exerts pressure on intrathoracic vessels, resulting in cardiopulmonary interactions. This sustained positive intrathoracic pressure is known to decrease cardiac preload, and thus, decrease venous return, ultimately reducing both the stroke volume and stressed blood volume of the cardiovascular system. Currently, cardiovascular and pulmonary care are provided independently of one another. That positive end-expiratory pressure alters both stroke volume and stressed blood volume suggests both the pulmonary and cardiovascular state should be conjointly optimised. Optimising these systems in isolation may benefit one system, but have highly detrimental effects on the other. A combined cardiopulmonary model has the potential to provide a better understanding of patient specific pulmonary and cardiovascular state, as well as resulting cardiopulmonary interactions. This would enable simultaneous optimisation of all cardiovascular and pulmonary parameters. Cardiopulmonary interactions are highly patient specific and unpredictable, making accurate modelling of these interactions challenging. A previously validated cardiopulmonary model was found to have increasing errors at high positive end-expiratory pressures. A new iteration, the alpha model, was introduced to resolve this issue. This paper aims to review the alpha model against its predecessors, the previous cardiopulmonary model, and the original three chamber cardiovascular system model. All models are used to identify cardiovascular system parameters from measurements of 4 pigs during a preload reduction manoeuvre. Outputs and parameter estimations from models are compared to assess the relative performance of the alpha model against its predecessors. The novel alpha model was able to reduce model errors under high positive end-expiratory pressure, resulting in more accurate model outputs. At high positive end-expiratory pressures (20cmH2O), the alpha model had an average error of 11.24%, while the original cardiopulmonary model had a much higher error of 52.21%. Furthermore, identified outputs of the alpha model more closely matched those of the 3 chamber model than the previous cardiopulmonary model. On average, at high positive end-expiratory levels, identified model parameters from the alpha model showed a 6.21% difference to those of the 3 chamber model, while the cardiopulmonary model displayed a 39.43% difference. The alpha model proved to be more stable than the original cardiopulmonary model, making it a good candidate for model based care. However, it produced similar parameter outputs to the simpler three chamber cardiovascular model, bringing into question whether the additional complexity is justified, especially considering the low availability of clinical data in the ICU. There is a critical need for model based care to guide important procedures in ICU, such as fluid therapy. Candidate models should be continuously reviewed in order to guarantee the best possible care.

Objectives Currently, there are no data on sex differences in the power profiles in sprint track cycling. This cross-section study analyses retrospective data of female and male track sprint cyclists for sex differences. We hypothesized that women would exhibit lower peak power to weight than men, as well as demonstrate a different distribution of power durations related to sprint cycling performance. Design We used training, testing, and racing data from a publicly available online depository (www.strava.com), for 29 track sprint cyclists (eight women providing 18 datasets, and 21 men providing 54 datasets) to create sex-specific profiles. R2 was used to describe model quality, and regression indices are used to compare watts per kilogram (W/kg) for each duration for both sexes against a 1:1 relationship expected for 15-s:15-s W/kg. Results We confirmed our sample were sprint cyclists, displaying higher peak and competition power than track endurance cyclists. All power profiles showed a high model quality (R2 ≥ 0.77). Regression indices for both sexes were similar for all durations, suggesting similar peak power and similar relationship between peak power and endurance level for both men and women (rejecting our hypothesis). The value of R2 for the female sprinters showed greater variation suggesting greater differences within female sprint cyclists. Conclusion The main finding shows female sprint cyclists in this study have very similar relationships between peak power and endurance power as men. Higher variation in W/kg for women in this study than men, within these strong relationships, indicates women in this study, had greater inter-athlete variability, and may thus require more personalised training. Future work needs to be performed with larger samples, and at different levels to optimize these recommendations.

Current convention place peak power as the main determinant of sprint cycling performance. This study challenges that notion and compares two common durations of sprint cycling performance with not only peak power, but power out to 20-min. There is also a belief where maximal efforts of longer durations will be detrimental to sprint cycling performance. 56 data sets from 27 cyclists (21 male, 6 female) provided maximal power for durations from 1-s to 20-min. Peak power values are compared to assess the strength of correlation (R2), and any relationship (slope) across every level. R2 between 15-s- 30-s power and durations from 1-s to 20-min remained high (R2 ≥ 0.83). Despite current assumptions around 1-s power, our data shows this relationship is stronger around competition durations, and 1-s power also still shared strong relationships with longer durations out to 20-min. Slopes for relationships at shorter durations were closer to a 1:1 relationship than longer durations, but closer to long-duration slopes than to a 1:1 line. The present analyses contradicts both well-accepted hypotheses that peak power is the main driver of sprint cycling performance and that maximal efforts of longer durations out to 20-min will hinder sprint cycling. This study shows the importance and potential of training durations from 1-s to 20-min over a preparation period to improve competition sprint cycling performance.

Introduction Infants with severe or recurrent transitional hypoglycaemia continue to have high rates of adverse neurological outcomes and new treatment approaches are needed that target the underlying pathophysiology. Diazoxide is one such treatment that acts on the pancreatic β-cell in a dose-dependent manner to decrease insulin secretion.Methods and analysis Phase IIB, double-blind, two-arm, parallel, randomised trial of diazoxide versus placebo in neonates ≥35 weeks’ gestation for treatment of severe (blood glucose concentration (BGC)

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.

BACKGROUND:Respiratory mechanics models can aid in optimising patient-specific mechanical ventilation (MV), but the applications are limited to fully sedated MV patients who have little or no spontaneously breathing efforts. This research presents a time-varying elastance (E(drs)) model that can be used in spontaneously breathing patients to determine their respiratory mechanics. METHODS:A time-varying respiratory elastance model is developed with a negative elastic component (E(demand)), to describe the driving pressure generated during a patient initiated breathing cycle. Data from 22 patients who are partially mechanically ventilated using Pressure Support (PS) and Neurally Adjusted Ventilatory Assist (NAVA) are used to investigate the physiology relevance of the time-varying elastance model and its clinical potential. E(drs) of every breathing cycle for each patient at different ventilation modes are presented for comparison. RESULTS:At the start of every breathing cycle initiated by patient, E(drs) is < 0. This negativity is attributed from the E(demand) due to a positive lung volume intake at through negative pressure in the lung compartment. The mapping of E(drs) trajectories was able to give unique information to patients' breathing variability under different ventilation modes. The area under the curve of E(drs) (AUCE(drs)) for most patients is > 25 cmH2Os/l and thus can be used as an acute respiratory distress syndrome (ARDS) severity indicator. CONCLUSION:The E(drs) model captures unique dynamic respiratory mechanics for spontaneously breathing patients with respiratory failure. The model is fully general and is applicable to both fully controlled and partially assisted MV modes.

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.

Accurate, continuous, left ventricular stroke volume (SV) measurements can convey large amounts of information about patient hemodynamic status and response to therapy. However, direct measurements are highly invasive in clinical practice, and current procedures for estimating SV require specialized devices and significant approximation.This study investigates the accuracy of a three element Windkessel model combined with an aortic pressure waveform to estimate SV. Aortic pressure is separated into two components capturing; 1) resistance and compliance, 2) characteristic impedance. This separation provides model-element relationships enabling SV to be estimated while requiring only one of the three element values to be known or estimated. Beat-to-beat SV estimation was performed using population-representative optimal values for each model element. This method was validated using measured SV data from porcine experiments (N = 3 female Pietrain pigs, 29-37 kg) in which both ventricular volume and aortic pressure waveforms were measured simultaneously.The median difference between measured SV from left ventricle (LV) output and estimated SV was 0.6 ml with a 90% range (5th-95th percentile) -12.4 ml-14.3 ml. During periods when changes in SV were induced, cross correlations in between estimated and measured SV were above R = 0.65 for all cases.The method presented demonstrates that the magnitude and trends of SV can be accurately estimated from pressure waveforms alone, without the need for identification of complex physiological metrics where strength of correlations may vary significantly from patient to patient.

IntroductionThis study examines the likelihood and evolution of overall and hypoglycemia-inducing variability of insulin sensitivity in ICU patients based on diagnosis and day of stay.Materials and methodsAn analysis of model-based insulin sensitivity for n=390 patients in a medical ICU (Christchurch, New Zealand). Two metrics are defined to measure the variability of a patient's insulin sensitivity relative to predictions of a stochastic model created from the same data for all patients over all days of stay. The first selectively captures large increases related to the risk of hypoglycemia. The second captures overall variability. Distributions of per-patient variability scores were evaluated over different ICU days of stay and for different diagnosis groups based on APACHE III: operative and non-operative cardiac, gastric, all other. Linear and generalized linear mixed effects models assess the statistical significance of differences between groups and over days.ResultsVariability defined by the two metrics was not substantially different. Variability was highest on day 1, and decreased over time (pConclusionsThis study characterizes the variability and evolution of insulin sensitivity in critically ill patients, and may help inform the clinical management of metabolic dysfunction in critical care.