Continuous stroke volume estimation from aortic pressure using zero dimensional cardiovascular model: proof of concept study from porcine experiments

Efficacy and Safety of Shenfu Injection for Severe Pneumonia in the Elderly: A Systematic Review and Meta-Analysis Based on Western and Eastern Medicine

Background: Although increasing clinical trials studying Shenfu injection (SFI) comprising panaxoside 0.8 mg/ml extracted from Panax ginseng C.A. Mey. and aconitine 0.1 mg/ml extracted from Aconitum carmichaeli Debeaux for elderly patients with severe pneumonia on biomarkers associated with COVID-19 progression are emerging, there is no evidence-based evaluation for the effect of SFI on elderly severe pneumonia.

Objectives: To evaluate the effect of SFI on elderly patients with severe pneumonia providing hints for treating critical COVID-19, we conducted a systematic review and meta-analysis.

Methods: Nine databases, namely, PubMed, EMBASE, Web of Science, Science Direct, Google Scholar, Wanfang, Chongqing VIP Database, CNKI, and SinoMed were used to search clinical trials reporting the effect of SFI as an adjuvant for elderly severe pneumonia on outcomes of interest. Primary outcomes were total effective rate, Acute Physiology and Chronic Health Evaluation (APACHE) II score, mortality, and safety. Secondary outcomes were predictors associated with COVID-19 progression. Duplicated or irrelevant articles with unavailable data were excluded. Cochrane Collaboration’s tool was used to evaluate the risk of bias by two reviewers independently. All data were analyzed by Rev Man 5.4. Continuous variables were shown as weighted mean difference (WMD) or standard mean difference (SMD) with 95% confidence intervals (95% CI), whereas dichotomous data were calculated as the risk ratio (RR) with 95% CI.

Results: We included 20 studies with 1, 909 participants, and the pooled data showed that compared with standard control, SFI could improve the total effective rate (RR = 1.25, 95% CI = 1.14–1.37, and n = 689), APACHE II score (WMD = −2.95, 95% CI = −3.35, −2.56, and n = 809), and predictors associated with COVID-19 progression (brain natriuretic peptide, creatine kinase, stroke volume, cardiac output, left ventricular ejection fraction, cardiac index, sE-selectin, von Willebrand factor, activated partial thromboplastin time, platelet counts, D-Dimer, procalcitonin, and WBC count). SFI may reduce mortality (RR = 0.52, 95% CI = 0.37–0.73, and n = 429) and safety concerns (RR = 0.29, 95% CI = 0.17–0.51, and n = 150) for elderly severe pneumonia.

Conclusion: SFI as an adjuvant may improve the total effective rate, APACHE II score, gas exchange, and predictors associated with COVID-19 progression, reducing mortality and safety concerns for elderly patients with severe pneumonia.

Excess pressure as an analogue of blood flow velocity

INTRODUCTION

Derivation of blood flow velocity from a blood pressure waveform is a novel technique, which could have potential clinical importance. Excess pressure, calculated from the blood pressure waveform via the reservoir-excess pressure model, is purported to be an analogue of blood flow velocity but this has never been examined in detail, which was the aim of this study.

METHODS

Intra-arterial blood pressure was measured sequentially at the brachial and radial arteries via fluid-filled catheter simultaneously with blood flow velocity waveforms recorded via Doppler ultrasound on the contralateral arm (n = 98, aged 61 ± 10 years, 72% men). Excess pressure was derived from intra-arterial blood pressure waveforms using pressure-only reservoir-excess pressure analysis.

RESULTS

Brachial and radial blood flow velocity waveform morphology were closely approximated by excess pressure derived from their respective sites of measurement (median cross-correlation coefficient r = 0.96 and r = 0.95 for brachial and radial comparisons, respectively). In frequency analyses, coherence between blood flow velocity and excess pressure was similar for brachial and radial artery comparisons (brachial and radial median coherence = 0.93 and 0.92, respectively). Brachial and radial blood flow velocity pulse heights were correlated with their respective excess pressure pulse heights (r = 0.53, P < 0.001 and r = 0.43, P < 0.001, respectively).

CONCLUSION

Excess pressure is an analogue of blood flow velocity, thus affording the opportunity to derive potentially important information related to arterial blood flow using only the blood pressure waveform.

The modified arterial reservoir: An update with consideration of asymptotic pressure (P∞) and zero-flow pressure (Pzf)

This article describes the modified arterial reservoir in detail. The modified arterial reservoir makes explicit the wave nature of both reservoir (P_res) and excess pressure (P_xs). The mathematical derivation and methods for estimating P_res in the absence of flow velocity data are described. There is also discussion of zero-flow pressure (P_zf), the pressure at which flow through the circulation ceases; its relationship to asymptotic pressure (P_∞) estimated by the reservoir model; and the physiological interpretation of P_zf . A systematic review and meta-analysis provides evidence that P_zf differs from mean circulatory filling pressure.

Clinically applicable model-based method, for physiologically accurate flow waveform and stroke volume estimation

BACKGROUND AND OBJECTIVES

Cardiovascular dysfunction can be more effectively monitored and treated, with accurate, continuous, stroke volume (SV) and/or cardiac output (CO) measurements. Since direct measurements of SV/CO are highly invasive, clinical measures are often discrete, or if continuous, can require recalibration with a discrete SV measurement after hemodynamic instability. This study presents a clinically applicable, non-additionally invasive, physiological model-based, SV and CO measurement method, which does not require recalibration during or after hemodynamic instability.

METHODS AND RESULTS

The model's ability to predict flow profiles and SV is assessed in an animal trial, using endotoxin to induce sepsis in 5 pigs. Mean percentage error between beat-to-beat SV measured from an aortic flow probe and estimated by the model was -2%, while 90% of estimations fell within -24.2% and +27.9% error. Error between estimated and measured changes in mean SV following interventions was less than 30% for 4 out of the 5 pigs. Correlations between model estimated and probe measured flow, for each pig and hemodynamic interventions, was r² = 0.58 - 0.96, with 21 of the 25 pig intervention stages having r²  >  0.80.

CONCLUSION

The results demonstrate the model accurately estimates and tracks changes in flow profiles and resulting SV, without requiring model recalibration.

Accurate end systole detection in dicrotic notch-less arterial pressure waveforms

Identification of end systole is often necessary when studying events specific to systole or diastole, for example, models that estimate cardiac function and systolic time intervals like left ventricular ejection duration. In proximal arterial pressure waveforms, such as from the aorta, the dicrotic notch marks this transition from systole to diastole. However, distal arterial pressure measures are more common in a clinical setting, typically containing no dicrotic notch. This study defines a new end systole detection algorithm, for dicrotic notch-less arterial waveforms. The new algorithm utilises the beta distribution probability density function as a weighting function, which is adaptive based on previous heartbeats end systole locations. Its accuracy is compared with an existing end systole estimation method, on dicrotic notch-less distal pressure waveforms. Because there are no dicrotic notches defining end systole, validating which method performed better is more difficult. Thus, a validation method is developed using dicrotic notch locations from simultaneously measured aortic pressure, forward projected by pulse transit time (PTT) to the more distal pressure signal. Systolic durations, estimated by each of the end systole estimates, are then compared to the validation systolic duration provided by the PTT based end systole point. Data comes from ten pigs, across two protocols testing the algorithms under different hemodynamic states. The resulting mean difference ± limits of agreement between measured and estimated systolic duration, of [Formula: see text] versus [Formula: see text], for the new and existing algorithms respectively, indicate the new algorithms superiority.

Determining the Temporal Profile of Intracranial Pressure Changes Following Transient Stroke in an Ovine Model

BACKGROUND AND PURPOSE

Cerebral edema and elevated intracranial pressure (ICP) are the leading cause of death in the first week following stroke. Despite this, current treatments are limited and fail to address the underlying mechanisms of swelling, highlighting the need for targeted treatments. When screening promising novel agents, it is essential to use clinically relevant large animal models to increase the likelihood of successful clinical translation. As such, we sought to develop a survival model of transient middle cerebral artery occlusion (tMCAO) in the sheep and subsequently characterize the temporal profile of cerebral edema and elevated ICP following stroke in this novel, clinically relevant model.

METHODS

Merino-sheep (27M;31F) were anesthetized and subject to 2 h tMCAO with reperfusion or sham surgery. Following surgery, animals were allowed to recover and returned to their home pens. At preselected times points ranging from 1 to 7 days post-stroke, animals were re-anesthetized, ICP measured for 4 h, followed by imaging with MRI to determine cerebral edema, midline shift and infarct volume (FLAIR, T2 and DWI). Animals were subsequently euthanized and their brain removed for immunohistochemical analysis. Serum and cerebrospinal fluid samples were also collected and analyzed for substance P (SP) using ELISA.

RESULTS

Intracranial pressure and MRI scans were normal in sham animals. Following stroke, ICP rose gradually over time and by 5 days was significantly (p < 0.0001) elevated above sham levels. Profound cerebral edema was observed as early as 2 days post-stroke and continued to evolve out to 6 days, resulting in significant midline shift which was most prominent at 5 days post-stroke (p < 0.01), in keeping with increasing ICP. Serum SP levels were significantly elevated (p < 0.01) by 7 days post-tMCAO.

CONCLUSION

We have successfully developed a survival model of ovine tMCAO and characterized the temporal profile of ICP. Peak ICP elevation, cerebral edema and midline shift occurred at days 5-6 following stroke, accompanied by an elevation in serum SP. Our findings suggest that novel therapeutic agents screened in this model targeting cerebral edema and elevated ICP would most likely be effective when administered prior to 5 days, or as early as possible following stroke onset.

Stroke Volume estimation using aortic pressure measurements and aortic cross sectional area: Proof of concept

Accurate Stroke Volume (SV) monitoring is essential for patient with cardiovascular dysfunction patients. However, direct SV measurements are not clinically feasible due to the highly invasive nature of measurement devices. Current devices for indirect monitoring of SV are shown to be inaccurate during sudden hemodynamic changes. This paper presents a novel SV estimation using readily available aortic pressure measurements and aortic cross sectional area, using data from a porcine experiment where medical interventions such as fluid replacement, dobutamine infusions, and recruitment maneuvers induced SV changes in a pig with circulatory shock. Measurement of left ventricular volume, proximal aortic pressure, and descending aortic pressure waveforms were made simultaneously during the experiment. From measured data, proximal aortic pressure was separated into reservoir and excess pressures. Beat-to-beat aortic characteristic impedance values were calculated using both aortic pressure measurements and an estimate of the aortic cross sectional area. SV was estimated using the calculated aortic characteristic impedance and excess component of the proximal aorta. The median difference between directly measured SV and estimated SV was -1.4ml with 95% limit of agreement +/- 6.6ml. This method demonstrates that SV can be accurately captured beat-to-beat during sudden changes in hemodynamic state. This novel SV estimation could enable improved cardiac and circulatory treatment in the critical care environment by titrating treatment to the effect on SV.

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.

Beat-by-Beat Estimation of the Left Ventricular Pressure-Volume Loop Under Clinical Conditions

This paper develops a method for the minimally invasive, beat-by-beat estimation of the left ventricular pressure-volume loop. This method estimates the left ventricular pressure and volume waveforms that make up the pressure-volume loop using clinically available inputs supported by a short, baseline echocardiography reading. Validation was performed across 142,169 heartbeats of data from 11 Piétrain pigs subject to two distinct protocols encompassing sepsis, dobutamine administration and clinical interventions. The method effectively located pressure-volume loops, with low overall median errors in end-diastolic volume of 8.6%, end-systolic volume of 17.3%, systolic pressure of 19.4% and diastolic pressure of 6.5%. The method further demonstrated a low overall mean error of 23.2% predicting resulting stroke work, and high correlation coefficients along with a high percentage of trend compass 'in band' performance tracking changes in stroke work as patient condition varied. This set of results forms a body of evidence for the potential clinical utility of the method. While further validation in humans is required, the method has the potential to aid in clinical decision making across a range of clinical interventions and disease state disturbances by providing real-time, beat-to-beat, patient specific information at the intensive care unit bedside without requiring additional invasive instrumentation.

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 (V_d), an important, but difficult to measure physiological parameter. V_d 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 V_d. 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 (V_d) for each subject (no more than 7.8% variation). Method validity was further supported by the physiologically reasonable V_d 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 V_d 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 V_d in a clinical environment.

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.

Electronic supplementary material

The online version of this article (doi:10.1186/s12938-017-0338-7) contains supplementary material, which is available to authorized users.