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Accurate assessment of the left ventricular (LV) systolic function is indispensable in the clinic. However, estimation of a precise index of cardiac contractility, i.e., the end-systolic elastance (Ees), is invasive and cannot be established as clinical routine. The aim of this work was to present and validate a methodology that allows for the estimation of Ees from simple and readily available noninvasive measurements. The method is based on a validated model of the cardiovascular system and noninvasive data from arm-cuff pressure and routine echocardiography to render the model patient-specific. Briefly, the algorithm first uses the measured aortic flow as model input and optimizes the properties of the arterial system model to achieve correct prediction of the patient's peripheral pressure. In a second step, the personalized arterial system is coupled with the cardiac model (time-varying elastance model) and the LV systolic properties, including Ees, are tuned to predict accurately the aortic flow waveform. The algorithm was validated against invasive measurements of Ees (multiple pressure-volume loop analysis) taken from n = 10 patients with heart failure with preserved ejection fraction and n = 9 patients without heart failure. Invasive measurements of Ees (median = 2.4 mmHg/mL, range = [1.0, 5.0] mmHg/mL) agreed well with method predictions (normalized root mean square error = 9%, r = 0.89, bias = -0.1 mmHg/mL, and limits of agreement = [-0.9, 0.6] mmHg/mL). This is a promising first step toward the development of a valuable tool that can be used by clinicians to assess systolic performance of the LV in the critically ill. [ABSTRACT FROM AUTHOR]

The lifetime prevalence of 8.6% of asthma in Germany reflects the high medical and socioeconomic impact of the disease. Asthma treatment goals have changed during the last decades: from symptom control to symptom prevention, with highly effective, disease-modifying anti-asthmatic drugs (DMAADs) aiming at asthma remission. In order to achieve this goal, phenotyping of patients (including an evaluation of allergies and type 2 biomarkers) is crucial for personalized treatment. The identification and effective treatment of concomitant diseases, such as allergic rhinitis or chronic rhinosinusitis with nasal polyps (CRSwNP), plays a major role for successful treatment. This underlines the importance of interdisciplinary collaboration of otolaryngologists and respiratory physicians in the management of patients with asthma. This CME article informs the reader about current guidelines on the diagnosis and treatment of asthma, focusing on clinically relevant recommendations for ENT physicians., Competing Interests: Erklärung zu finanziellen Interessen Forschungsförderung erhalten: ja, von einer anderen Institution (Pharma- oder Medizintechnikfirma usw.); Honorar/geldwerten Vorteil für Referententätigkeit erhalten: ja, von einer anderen Institution (Pharma- oder Medizintechnikfirma usw.); Bezahlter Berater/interner Schulungsreferent/Gehaltsempfänger: ja, von einer anderen Institution (Pharma- oder Medizintechnikfirma usw.); Patent/Geschäftsanteile/Aktien (Autor/Partner, Ehepartner, Kinder) an im Bereich der Medizin aktiven Firma: nein; Patent/Geschäftsanteile/Aktien (Autor/Partner, Ehepartner, Kinder) an zu Sponsoren dieser Fortbildung bzw. durch die Fortbildung in ihren Geschäftsinteressen berührten Firma: nein Erklärung zu nichtfinanziellen Interessen Oliver Pfaar gibt Studiengelder und/oder Honorare an von und/oder Reisekostenunterstützung an von ALK-Abelló, Allergopharma, Stallergenes Greer, HAL Allergy Holding B.V./HAL Allergie GmbH, Bencard Allergie GmbH/Allergy Therapeutics, Lofarma, ASIT Biotech Tools S.A., Laboratorios LETI/LETI Pharma, GlaxoSmithKline, ROXALL Medizin, Novartis, Sanofi-Aventis und Sanofi-Genzyme, Med Update Europe GmbH, streamedup! GmbH, Pohl-Boskamp, Inmunotek S.L., John Wiley and Sons, AS, Paul-Martini-Stiftung (PMS), Regeneron Pharmaceuticals Inc., RG Aerztefortbildung, Institut für Disease Management, Springer GmbH, AstraZeneca, IQVIA Commercial, Ingress Health, Wort&Bild Verlag, Verlag ME, Procter&Gamble, ALTAMIRA, Meinhardt Congress GmbH, Deutsche Forschungsgemeinschaft, Thieme, Deutsche AllergieLiga e.V., AeDA, Alfried-Krupp Krankenhaus, Red Maple Trials Inc., Königlich Dänisches Generalkonsulat, Medizinische Hochschule Hannover, ECM Expro&Conference Management, Technische Universität Dresden, Lilly, Paul Ehrlich Institut (PEI), Japanese Society of Allergy, Forum für Medizinische Fortbildung, alles innerhalb der letzten 36 Moante; zudem ist er Mitglied der EAACI Excom, Mitglied des erweiterten Vorstandes der DGAKI, Koordinator, Haupt- oder Ko-Koordinator von verschiedenen Leitlinien und Positionspapieren in der Rhinologie, Allergologie und Allergen-Immuntherapie (AIT). Boris A. Stuck hat finanzielle Unterstützung für Forschungsarbeiten, Berater- und Referentenhonorare von folgenden Firmen erhalten: Neuwirth Medical Products, XM Consult, Itamar Medical, Inspire Medical, G. Pohl-Boskamp GmbH&Co.KG, GlaxoSmithKline GmbH&Co KG, Bristol Myers Squibb, Merck, UV smart und Sanofi. Er hat darüber hinaus finanzielle Unterstützung (Sponsoring) für Veranstaltungen der Klinik für Hals-, Nasen- und Ohrenheilkunde, Kopf- und Hals-Chirurgie Marburg erhalten von: Epithetik Sauer, Bajohr, GHD, Bristol Myers Squibb, Merck, Storz, Fahl, Fresenius Kabi, Atos, Neuwirth Medical Products, Shire, Zeiss, Inspire Medical Systems, Advanced Bionics, ALK, Löwenstein Medical, Cochlear, Spiggle&Theis, MedEl, Bess, Sanofi, Pohl-Boskamp, MSD, Otopront, Novartis and Takeda. Er ist als Berater für die Firmen Itamar Medical und UV smart tätig. Prof. Dr. Marek Lommatzsch hat Forschungsunterstützung von DFG, GSK und Astra Zeneca, sowie Honorare für Vortrags- und/oder Beratertätigkeiten erhalten von ALK, Allergopharma, Astra Zeneca, Bencard, Berlin-Chemie, Boehringer-Ingelheim, Bosch, Chiesi, Circassia, GSK, HAL Allergy, Janssen-Cilag, Leti, MSD, Mundipharma, Novartis, Nycomed/Takeda, Sanofi, Stallergenes, TEVA und UCB., (Thieme. All rights reserved.)

In a progressively aging population, it is of utmost importance to develop reliable, noninvasive, and cost-effective tools to estimate biomarkers that can be indicative of cardiovascular risk. Various pathophysiological conditions are associated to changes in the total arterial compliance (C T ), and thus, its estimation via an accurate and simple method is valuable. Direct noninvasive measurement of C T is not feasible in the clinical practice. Previous methods exist for indirect estimation of C T , which, however, require noninvasive, yet complex and expensive, recordings of the central pressure and flow. Here, we introduce a novel, noninvasive method for estimating C T from a single carotid waveform measurement using regression analysis. Features were extracted from the carotid wave and were combined with demographic data. A prediction pipeline was adopted for estimating C T using, first, a feature-based regression analysis and, second, the raw carotid pulse wave. The proposed methodology was appraised using the large human cohort ( N = 2,256) of the Asklepios study. Accurate estimates of C T were yielded for both prediction schemes, namely, r = 0.83 and normalized root mean square error (nRMSE) = 9.58% for the feature-based model, and r = 0.83 and nRSME = 9.67% for the model that used the raw signal. The major advantage of this method pertains to the simplification of the technique offering easily applicable and convenient C T monitoring. Such an approach could offer promising applications, ranging from fast and cost-efficient hemodynamical monitoring by the physician to integration in wearable technologies. NEW & NOTEWORTHY This article introduces a novel artificial intelligence method to estimate total arterial compliance (C T ) via exploiting the information provided by an uncalibrated carotid blood pressure waveform as well as typical clinical variables. The major finding of this study is that C T , which is usually acquired using both pressure and flow waveforms, can be accurately derived by the use of the pressure wave alone. This method could potentially facilitate easily applicable and convenient monitoring of C T .

Accurate assessment of the left ventricular (LV) systolic function is indispensable in the clinic. However, estimation of a precise index of cardiac contractility, i.e., the end-systolic elastance ( E es ), is invasive and cannot be established as clinical routine. The aim of this work was to present and validate a methodology that allows for the estimation of E es from simple and readily available noninvasive measurements. The method is based on a validated model of the cardiovascular system and noninvasive data from arm-cuff pressure and routine echocardiography to render the model patient-specific. Briefly, the algorithm first uses the measured aortic flow as model input and optimizes the properties of the arterial system model to achieve correct prediction of the patient's peripheral pressure. In a second step, the personalized arterial system is coupled with the cardiac model (time-varying elastance model) and the LV systolic properties, including E es , are tuned to predict accurately the aortic flow waveform. The algorithm was validated against invasive measurements of E es (multiple pressure-volume loop analysis) taken from n = 10 patients with heart failure with preserved ejection fraction and n = 9 patients without heart failure. Invasive measurements of E es (median = 2.4 mmHg/mL, range = [1.0, 5.0] mmHg/mL) agreed well with method predictions (normalized root mean square error = 9%, ρ = 0.89, bias = -0.1 mmHg/mL, and limits of agreement = [-0.9, 0.6] mmHg/mL). This is a promising first step toward the development of a valuable tool that can be used by clinicians to assess systolic performance of the LV in the critically ill. NEW & NOTEWORTHY In this study, we present a novel model-based method to estimate the left ventricular (LV) end-systolic elastance ( E es ) according to measurement of the patient's arm-cuff pressure and a routine echocardiography examination. The proposed method was validated in vivo against invasive multiple-loop measurements of E es , achieving high correlation and low bias. This tool could be most valuable for clinicians to assess the cardiovascular health of critically ill patients.

Transcatheter aortic valve replacement (TAVR) is increasingly used to treat severe aortic stenosis (AS) patients. However, little is known regarding the direct effect of TAVR on the ventricular-aortic interaction. In the present study, we aimed to investigate changes in central hemodynamics after successful TAVR. We retrospectively examined 33 cases of severe AS patients (84 ± 6 yr) who underwent TAVR. Invasive measurements of left ventricular and aortic pressures as well as echocardiographic aortic flow were acquired before and after TAVR (maximum within 5 days). We examined alterations in key features of central pressure and flow waveforms, including the aortic augmentation index (AIx), and performed wave separation analysis. Arterial parameters were determined via parameter-fitting on a two-element Windkessel model. Resolution of AS resulted in direct increase in the aortic systolic pressure and maximal aortic flow (131 ± 22 vs. 157 ± 25 mmHg and 237 ± 49 vs. 302 ± 69 mL/s, P < 0.001 for all), whereas the ejection duration decreased ( P < 0.001). We noted a significant decrease in the AIx (from 42 ± 12 to 19 ± 11%, P < 0.001). Of note, the arterial properties remained unchanged. There was a comparable increase in both forward (61 ± 20 vs. 77 ± 20 mmHg, P < 0.001) and backward (35 ± 14 vs. 42 ± 10 mmHg, P = 0.013) pressure wave amplitudes, while their ratio, i.e., the reflection coefficient, was preserved. Our results highlight the impact of TAVR on the ventricular-aortic interaction by affecting the amplitude, shape, and related attributes of the aortic pressure and flow pulse and challenge the interpretation of AIx as a solely vascular measure in AS patients. NEW & NOTEWORTHY Transcatheter aortic valve replacement (TAVR) is linked with an immediate increase in aortic systolic blood pressure and maximal flow, as well as steeper aortic pressure and flow wave upstrokes. After TAVR, the forward wave pumped by the heart is enhanced. Although the arterial properties remain unchanged, the central augmentation index (AIx) is markedly decreased after TAVR. This challenges the interpretation of AIx as a solely vascular measure in patients with aortic valve stenosis.

Diastolic dysfunction (DD) is a major component of heart failure with preserved ejection fraction (HFpEF). Accordingly, a profound understanding of the underlying biomechanical mechanisms involved in DD is needed to elucidate all aspects of HFpEF. In this study, we have developed a computational model of DD by leveraging the power of an advanced one-dimensional arterial network coupled to a four-chambered zero-dimensional cardiac model. The two main pathologies investigated were linked to the active relaxation of the myocardium and the passive stiffness of the left ventricular wall. These pathologies were quantified through two parameters for the biphasic delay of active relaxation, which simulate the early and late-phase relaxation delay, and one parameter for passive stiffness, which simulates the increased nonlinear stiffness of the ventricular wall. A parameter sensitivity analysis was conducted on each of the three parameters to investigate their effect in isolation. The three parameters were then concurrently adjusted to produce the three main phenotypes of DD. It was found that the impaired relaxation phenotype can be replicated by mainly manipulating the active relaxation, the pseudo-normal phenotype was replicated by manipulating both the active relaxation and passive stiffness, and, finally, the restricted phenotype was replicated by mainly changing the passive stiffness. This article presents a simple model producing a holistic and comprehensive replication of the main DD phenotypes and presents novel biomechanical insights on how key parameters defining the relaxation and stiffness properties of the myocardium affect the development and manifestation of DD. NEW & NOTEWORTHY This study uses a complete and validated computational model of the cardiovascular system to simulate the two main pathologies involved in diastolic dysfunction (DD), i.e., abnormal active relaxation and increased ventricular diastolic stiffness. The three phenotypes of DD were successfully replicated according to literature data. We elucidate the biomechanical effect of the relaxation pathologies involved and how these pathologies interact to create the various phenotypes of DD.