Residual Mean Research Articles

P1362 Echocardiographic findings and BNP levels in patients with valve-in-valve implantation in small failed mitroflow aortic prosthesis

Abstract BACKGROUND Early structural valve deterioration (SVD) frequently occurs in Mitroflow aortic bioprosthesis, especially for small sizes (19-mm and 21-mm), and it is associated with reduced overall survival. Treatment by percutaneous valve-in-valve (ViV) implantation is considered a challenging procedure. This is mainly due to an elevated risk of coronary ostia obstruction and high residual post-procedural mean gradients (mG), particularly when severe pre-existing patient-prosthesis mismatch (PPM) is present. PURPOSE Aim of our study was to assess the feasibility of transfemoral ViV in small Mitroflow aortic valves using supra-annular self-expanding valves, named CoreValve and Evolut R and Acurate neo and report the midterm clinical results by comparing serum natriuretic peptide type B levels (BNP) before the procedure and at a mean follow-up of 2 years. METHODS This is an observational study including 11 patients with stenotic-type SVD of small Mitroflow aortic valves, considered at high/prohibitive risk for surgical reoperation, who underwent ViV implantation between July 2012 and March 2018. We performed echocardiographic assessment of valve hemodynamics (according to VARC-2 definitions) before and after the procedure and during the follow-up. We used the BNP ratio (the ratio between measured serum BNP/NT-proBNP level and maximal normal level) to compare BNP results before the procedure and at follow-up. All-cause mortality during the hospitalization and follow-up was also reported. RESULTS The Mitroflow size was 19-mm in 4 patients and 21-mm in 7 patients. Pre-existing severe PPM was present in 4 patients and moderate PPM in 7. CoreValve 26 was implanted in 2 patients, EvolutR 23 in 5 and Acurate neo S in 4 patients. We reported no coronary obstruction, deaths or other major events during the hospitalization. At a mean follow-up of 2 years one patient died. The baseline aortic mG of 56 ± 19 mmHg has significantly reduced after the procedure to 16,6 ± 8 mmHg (p < 0.0001) and follow-up 29,6 ± 16 mmHg (p = 0.008). A post-procedural mG≥20, but <40 mmHg, was observed in 3 patients. BNP ratio at baseline was 14,6 ± 12; only one patient had a BNP ratio <3. At follow-up (n = 7 patients), BNP ratio was significantly lowered to 1,5 ± 1,08 (p = 0.01) with only one patient with a BNP ratio >3. Patients with mPG ≥20 mmHg did not show differences in terms of mortality and reduction of serum BNP levels. CONCLUSIONS In our experience the ViV procedure on small degenerated aortic Mitroflow bioprosthesis appears to be technically feasible and provides good midterm clinical results with a net reduction in serum BNP levels, although an increase in mG was observed. Even though a post-procedural mG ≥20 mmHg is considered indicative of suboptimal aortic valve hemodynamics (according to VARC-2 criteria), its correlation with worse outcomes remains unclear and deserves further investigations.

P1489 Late cardiovascular manifestations of Gregg syndrome: Importance of multimodal imaging approach to optimize diagnosis

Abstract Background Congenital rubella syndrome, also known as Gregg syndrome (after Dr. Norman Gregg’s first description in 1941) is a variable constellation of multisystemic manifestations caused by rubella intrauterine infection. In this case, a patient known to have Gregg syndrome underwent trans-thoracic echocardiogram (TTE) and cardiac magnetic resonance (CMR) for optimizing the characterization and determining the status of her disease. Case report A 31-year-old female attends routine cardiology outpatient clinic prior initiation of pregnancy due to prior history of Gregg syndrome. Extent of disease includes congenital cataracts, neurosensorial bilateral hearing loss, supra-valvular aortic stenosis (treated percutaneously at age 4 with a subsequent aortic root reconstruction with homograft at age 6) and pulmonary artery stenosis (status-post surgical correction followed by angioplasty at age 9). The patient was asymptomatic with functional class NYHA I and was taking no medication. A TTE was requested which showed a left ventricle with low-normal systolic function. At the pulmonary valve, she had a residual peak velocity and mean gradient of 2.6 m/sec and 15 mmHg, respectively, with moderate regurgitation; the right ventricle was dilated with mild systolic dysfunction. Doppler evaluation of the ascending aorta revealed a peak velocity of 3,5 m/sec and a mean gradient of 30 mmHg (Figure 1a). Anatomical evaluation was limited because of the acoustic window but due to the cited hemo-dynamics, a CMR was requested for further characterization of her disease prior to pregnancy. CMR showed a normal sized left ventricle with an ejection fraction of 62%, without late gadolinium enhancement. The right pulmonary artery lumen was diminished, probably corresponding to an artifact due to previous stent angioplasty. In the ascending aorta there was residual supra-valvular aortic stenosis 9 mm from the sino-tubular junction with an adjacent, non-mobile, 33 x 39 x 20 mm mass, anterior to the aorta and immediately posterior to the sternum, consistent with aortic pseudoaneurysm (Figure 1b). Due to this finding pregnancy was discouraged, and she was referred to the Cardiovascular Surgery for aortic pseudoaneurysm correction. Conclusion Although infrequent because of successful worldwide vaccination policies, sporadic cases of Gregg syndrome can still be seen, especially in neglected populations with poor access to health services or in unvaccinated patients. Long-term follow-up of all patients undergoing repair procedures is warranted for evaluation of late occurring complications. This case underscores the importance of multimodality imaging for a complete anatomical and functional diagnosis of complex cardiovascular conditions. Abstract P1489 Figure 1

Generation of models from existing models composition: An application to agrarian sciences.

Mathematical models that describe gas production are widely used to estimate the rumen degradation digestibility and kinetics. The present study presents a method to generate models by combining existing models and to propose the von Bertalanffy-Gompertz two-compartment model based on this method. The proposed model was compared with the logistic two-compartment one to indicate which best describes the kinetic curve of gas production through the semi-automated in vitro technique from different pinto peanut cultivars. The data came from an experiment grown and harvested at the Far South Animal Sciences station (Essul) in Itabela, BA, Brazil and gas production was read at 2, 4, 6, 8, 10, 12, 14, 17, 20, 24, 28, 32, 48, 72, and 96 h after the start of the in vitro fermentation process. The parameters were estimated by the least squares method using the iterative Gauss-Newton process in the software R version 3.4.1. The best model to describe gas accumulation was based on the adjusted coefficient of determination, residual mean squares, mean absolute deviation, Akaike information criterion, and Bayesian information criterion. The von Bertalanffy-Gompertz two-compartment model had the best fit to describe the cumulative gas production over time according to the methodology and conditions of the present study.

A Two-Parameter Pranav Distribution with Properties and Its Application.

Introduction: Lifetime distribution has drawn so much attention in recent research, and this has lead to the development of new lifetime distribution. Addition of parameters to the existing distribution makes the distribution more flexible and reliable and applicable model has become the focus of the recent search. This paper proposed a two-parameter Pranav distribution which has its base from a one-parameter Pranav and Ishita distribution. Methods Two parameter Pranav distribution was proposed. Mathematical and statistical properties of the distribution which includes; moments, coefficient of variation, skewness, kurtosis, index of dispersion, hazard rate function, mean residual life function, stochastic ordering, mean deviation, Bonferroni and Lorenz curves were developed. Other lifetime distributions such as Ishita, Akash, Sujatha, Shanker, Lindley, and Exponential distributions were considered in the study. Results: This new distribution was compared with two-parameter Akash, Lindley, one parameter Pranav, Ishita, Akash, Sujatha, Shanker, Lindley, and Exponential distributions to determine the efficiency of the new model. The estimation of parameters has been X-rayed using the method of moments and maximum likelihood. Also, AIC and BIC were used to test for the goodness of fit of the model which was applied to a real-life data of hypertensive patients. The results show that the new two-parameter Pranav distribution has the lowest value of AIC and BIC Conclusion: Based on the AIC and BIC values we can conclude that the two-parameter Pranav is more efficient than the other distribution for modeling survival of hypertensive patients. Hence two-parameter Pranav can be seen as an important distribution in modeling lifetime data.

Development and Evaluation of Species-Specific Biomass Models for Most Common Timber and Fuelwood Species of Bangladesh

Allometric biomass models are efficient tools to estimate biomass of trees and forest stands in a non-destructive way. Development of species-specific allometric biomass models requires extensive fieldwork and time. Our study aimed to generate species-specific allometric biomass models for the most common fuelwood and timber species of Bangladesh. We also wanted to evaluate the performances of our models relative to the performances of regional and commonly used pan-tropical biomass models. We used semi-destructive method that incorporates tree-level volume, species-specific biomass expansion factor (BEF), and wood density. We considered four base models, 1) Ln (biomass) = a + bLn (D); 2) Ln (biomass) = a + bLn (H); 3) Ln (Biomass) = a + bLn (D^2H); 4) Ln (Biomass) = a + bLn (D) + cLn (H) to develop species-specific best-fitted models for Total Above-Ground Biomass (TAGB) and stem biomass. The best-fitted model for each species was selected by the lowest value of Akaike Information Criterion (AIC), Residual Standard Error (RSE) and Root Mean Square Error (RMSE). The derived best-fitted models were then evaluated with respect to regional and pan-tropical models using a separate set of observed data. This evaluation was conducted by computing ME (Model Efficiency) and MPE (Model Prediction Error). The best-fitted allometric biomass models have shown higher model efficiency (0.85 to 0.99 at scale 1) and the lowest model prediction error (-8.94% to 5.27%) compared to the regional and pan-tropical models. All the examined regional and pan-tropical biomass models showed different magnitude of ME and MPE. Some models showed higher level (>0.90 at scale 1) of ME compared to the best-fitted specific species biomass model.

Coseismic Slip Distribution of the 2019 Mw 7.5 New Ireland Earthquake from the Integration of Multiple Remote Sensing Techniques

The 2019 Mw 7.5 New Ireland earthquake occurred at an equatorial area where the dense vegetation prevents remote sensing techniques such as C- or X-band interferometric synthetic aperture radar (SAR) from acquiring coherent phase measurements. Therefore, in this paper, multiple remote sensing techniques including the L-band interferometric SAR, the range and azimuth offset tracking of SAR intensities, and the offset tracking of optical images were employed to map its co-seismic deformation field and to determine the slip distribution. The surface rupture was clearly and consistently captured by all offset observations, with the ground fault trace striking at an angel of 315° and extending over 10 km. An iterative weighting strategy based on the residual root mean square of inversions using individual datasets was developed to determine the relative weight of each dataset, allowing for the joint inversion of the fault geometry, the refinement of the dip angle, and the determination of the best fitting slip distribution. The resultant model indicates a nearly left-lateral strike-slip motion on the Weitin fault that ruptured to the surface with a maximum slip of 6.10 m, occurring at a depth of ~10 km, and a geodetic moment release of 1.03 × 1020 Nm, corresponding to a magnitude of Mw 7.31. The distribution of aftershocks shows about 70% of aftershocks were located in the area with increased Coulomb failure stress and few aftershocks in the subduction zone to the south of the Weitin fault were triggered by this event.

Vascular Hyperintensity on Fluid-Attenuated Inversion Recovery Indicates the Severity of Hypoperfusion in Acute Stroke

Background and AimAlthough fluid-attenuated inversion recovery vascular hyperintensities may be frequently seen in acute large-artery ischemic stroke, reports on their prognostic utility had been conflicting due to lack of quantitative evaluation of the perfusion status based on the signal intensity. We hypothesized that greater hyperintensity represents more severe hypoperfusion. MethodsOverall, 27 patients with acute occlusion of the proximal middle cerebral artery were divided into 2 groups, based on their signal intensity in the insular segment of middle cerebral artery on the affected side, relative to that of the insular cortex: the low signal intensity group (hypo- or isointense signals, n = 12) and the high signal intensity group (hyperintense signals, n = 15). Using dynamic susceptibility contrast magnetic resonance imaging, we assessed the time of the maximum value of the residue function and mean transit time, in the entire middle cerebral artery cortical area and diffusion-weighted imaging-Alberta Stroke Program Early Computed Tomography Score regions, including the corona radiata. ResultsThe high signal intensity group had significantly longer time of the maximum value of the residue function in all the diffusion-weighted imaging-Alberta Stroke Program Early Computed Tomography Score regions, except the M3 and M6 regions, and significantly longer mean transit time in the M1 and M4 regions. ConclusionsQuantitative analysis of the perfusion parameters revealed more severely compromised and widely disturbed perfusion status in the high signal intensity group than in the low signal intensity group.

Solving groundwater problems fraught with uncertain recharge: An application to Central Limpopo, South Africa.

Groundwater is a valued resource of limited extent both in quantity and in space. In order to ensure its sustainable use, appropriate evaluation of its availability is required. This study aimed at solving groundwater problem fraught with uncertainty by considering recharge as uncertain input parameter. The developed groundwater flow model was later calibrated using automatic calibration software PEST within MODFLOW environment. Head values from groundwater level measurement data were used as calibration targets. The calibration resulted in normalised residual mean square (NRMS) value of 1.828%, mean error (ME) of 0.999 m, mean absolute error (MAE) of 6.701 m, Root Mean Square Error (RMSE) of 8.499 m and correlation coefficient (Cor) of 0.997. The field monitoring borehole dataset of 2011 was used to validate the model. The validated model resulted in a Root Mean Square Error (RMSE) of 8.601 m and NRMS value of 1.85% which compares very well with the NRMS value of the calibrated model whose value was 1.828%. All set criteria were within acceptable limits for both calibrated and validated model. Thereafter, stochastic uncertainty analyses were performed by randomly sampling recharge realization and running a series of Monte Carlo (MC) simulations ranging from 50 to a maximum of 1000 realization of recharge fields. Stochastic model results were compared to those of deterministic model. Groundwater potential results were compared for both deterministic and stochastic scenarios. Deterministic scenario resulted in groundwater potential value of 1122.92 Mm3/year while stochastic scenario resulted in a value of 1137.61 Mm3/year, an increase of 14.7 Mm3/year. This implies that there is potential for more groundwater exploitation in the study area. Thus, the developed stochastic model can be used as a decision support system (DSS) for water resource management.

Developing Children's Oral Health Assessment Toolkits Using Machine Learning Algorithm.

Evaluating children's oral health status and treatment needs is challenging. We aim to build oral health assessment toolkits to predict Children's Oral Health Status Index (COHSI) score and referral for treatment needs (RFTN) of oral health. Parent and Child toolkits consist of short-form survey items (12 for children and 8 for parents) with and without children's demographic information (7 questions) to predict the child's oral health status and need for treatment. Data were collected from 12 dental practices in Los Angeles County from 2015 to 2016. We predicted COHSI score and RFTN using random Bootstrap samples with manually introduced Gaussian noise together with machine learning algorithms, such as Extreme Gradient Boosting and Naive Bayesian algorithms (using R). The toolkits predicted the probability of treatment needs and the COHSI score with percentile (ranking). The performance of the toolkits was evaluated internally and externally by residual mean square error (RMSE), correlation, sensitivity and specificity. The toolkits were developed based on survey responses from 545 families with children aged 2 to 17 y. The sensitivity and specificity for predicting RFTN were 93% and 49% respectively with the external data. The correlation(s) between predicted and clinically determined COHSI was 0.88 (and 0.91 for its percentile). The RMSEs of the COHSI toolkit were 4.2 for COHSI (and 1.3 for its percentile). Survey responses from children and their parents/guardians are predictive for clinical outcomes. The toolkits can be used by oral health programs at baseline among school populations. The toolkits can also be used to quantify differences between pre- and post-dental care program implementation. The toolkits' predicted oral health scores can be used to stratify samples in oral health research. This study creates the oral health toolkits that combine self- and proxy- reported short forms with children's demographic characteristics to predict children's oral health and treatment needs using Machine Learning algorithms. The toolkits can be used by oral health programs at baseline among school populations to quantify differences between pre and post dental care program implementation. The toolkits can also be used to stratify samples according to the treatment needs and oral health status.

Toward Improved Probabilistic Predictions for Flood Forecasts Generated Using Deterministic Models

AbstractUncertainties in flood forecasts are inevitable, and the key issue is to develop probabilistic predictions so that the predictive uncertainty (PU) bounds can be estimated. We develop and test a general method for probabilistic forecasting and PU estimation that is based on a theoretical and practical analysis of the actual nature of the model residuals, which reveals that the residual mean, standard deviation, and distributional form can all vary with time. Our approach is to condition the nature of the residual distribution on the magnitude of the corresponding streamflow value, but other kinds of conditioning are also possible. Using real data, we illustrate seven progressively more realistic sets of assumptions regarding the model residuals, ranging from homogenous Gaussian to fully heterogeneous non‐Gaussian. Our results show that the estimated probabilistic predictions become progressively better as the assumptions better conform to the actual properties of the residuals. As benchmarks, we compare against results from the state‐of‐the‐art power transformation approach. Our method is generally applicable to any situation where a deterministic model is used to generate predictions, and where empirical probabilistic predictions are required without developing a stochastic version of that model.

Ground diameter-height models of naturally regenerated seedlings and saplings under broad-leaved mixed forest in Maoershan Mountains.

Based on the investigation data of seedlings and saplings from 48 plots in natural broad-leaved forest of Maoershan Experimental Forest Farm of Northeast Forestry University in Heilongjiang Province, the optimum model of ground diameter (D0) - height (H) was selected from eight alternative models as the basic model for the main regeneration tree species, and then the stand factors were parameterized, and the mixed effect model of sampling plot level was developed. The basic model and the mixed effect model were tested by independent samples. The results showed that there was a significant positive correlation between ground diameter and tree height of seedlings and saplings and that power function or model containing power function could better fit the relationship. The introduction of stand factors [dominant height of forest (HT), average diameter at breast height (Dg), basal area of forest (BA)] could improve the fitting effect of the model, with the residual root mean square error (RMSE) of each tree species decreasing by 1.3%-7.4% (average 3.8%), adjusted coefficient of determination (Ra2) only increasing by 0.1%-1.1% (average 0.6%) and Akaike info criterion (AIC) decreasing by 3.2%-35.2% (average 11.4%). Mixed effect models were developed for 10 tree species, such as Ulmus propinqua, Tilia and Fraxinus mandshurica. The Ra2 of mixed effect models was larger than that of the basic model, with an enhancement of 0.5%-3.5% (average 2.2%). RMSE and AIC decreased by 3.9%-20.3% (ave-rage 13.9%) and 4.0%-44.4% (average 22.3%) than that of the basic model. Model test results showed that, compared with the basic model, the average absolute error (MAE) of mixed effect model was reduced by 0.0001-0.46 m, with an average reduction of 0.08 m, and the average prediction error percentage (MPSE) decreased by 0.1%-6.2%, with an average reduction of 2.0%. The mixed effect model could improve the fitting effect and prediction ability of the model. The ground diameter-height model of seedlings and samplings of main regeneration species in broad-leaved mixed forest was developed in this study, which provides a reference for structure analysis and stand growth prediction of natural broad-leaved forest.

Equatorial Pacific 1,000‐dbar Velocity and Isotherm Displacements From Argo Data: Beyond the Mean and Seasonal Cycle

AbstractEquatorial Pacific zonal velocity at 1,000 dbar and vertical isotherm displacements from 0–2,000 dbar are analyzed using Argo data through February 2018. In agreement with previous studies, the mean 1,000‐dbar zonal velocity is characterized by alternating eastward and westward jets of amplitude 5–10 cm/s. Similarly, the seasonal cycle is dominated by an annual Rossby wave that is apparent in annual harmonic fits to both the isotherm displacement and velocity fields. Residual (mean, annual, and semiannual harmonics removed) zonal velocity and isotherm displacements are analyzed using Complex Empirical Orthogonal Functions (CEOFs) and their Principal Components. For the analysis at 1,000 dbar, the first velocity CEOF accounts for 28% of the variance and the first isotherm displacement CEOF 24%. Spatial patterns of the lead CEOFs are broadly consistent with Rossby waves, but only isotherm displacement CEOF1 can be clearly linked to known physical processes in the equatorial Pacific. This is true of the isotherm displacement CEOF1s across pressure surfaces, despite being estimated independently on each one. The Principal Component of isotherm displacement CEOF1 at 1,000 dbar is well correlated (r = –0.65) with the Niño3.4 index at a 12‐month lag, suggesting that El Niño signals propagate deep into the equatorial Pacific Ocean. The 3‐D structure of the isotherm displacement CEOF1s suggests that the deep isotherm displacement signals are consistent with a vertically propagating Rossby wave of vertical mode 3–4.

Prediction Of Arrival Volume Based On Improved Markov SCGM(1,1)c Model

Aiming at the fact that the arrival gauges of port ships have the characteristics of increasing trend but abnormal fluctuation, and combined with the prediction methods commonly used for random events, this paper proposed a Markov SCGM(1,1)c model for a single-factor system, and the Markov SCGM(1,1)c model is improved by using the method of equal dimension gray number and sliding transfer probability matrix. The results show that the residual mean of the improved Markov SCGM(1,1)c prediction model is reduced to a certain extent, that is, the fitting degree is improved, the prediction accuracy is improved, and the ability to modify the mutation data is enhanced, which meets the actual requirements and provides theoretical support for port anchorage planning.

Horizontal Residual Mean: Addressing the Limited Spatial Resolution of Ocean Models

AbstractHorizontal fluxes of heat and other scalar quantities in the ocean are due to correlations between the horizontal velocity and tracer fields. However, the limited spatial resolution of ocean models means that these correlations are not fully resolved using the velocity and temperature evaluated on the model grid, due to the limited spatial resolution and the boxcar-averaged nature of the velocity and the scalar field. In this article, a method of estimating the horizontal flux due to unresolved spatial correlations is proposed, based on the depth-integrated horizontal transport from the seafloor to the density surface whose spatially averaged height is the height of the calculation. This depth-integrated horizontal transport takes into account the subgrid velocity and density variations to compensate the standard estimate of horizontal transport based on staircase-like velocity and density. It is not a parameterization of unresolved eddies, since it utilizes data available in ocean models without relying on any presumed parameter such as diffusivity. The method is termed the horizontal residual mean (HRM). The method is capable of estimating the spatial-correlation-induced water transport in a 1/4° global ocean model, using model data smoothed to 3/4°. The HRM extra overturning has a peak in the Southern Ocean of about 1.5 Sv (1 Sv ≡ 106 m3 s−1). This indicates an extra heat transport of 0.015 PW on average in the same area. It is expected that implementing the scheme in a coarse-resolution ocean model will improve its representation of lateral heat fluxes.

Approximating Isoneutral Ocean Transport via the Temporal Residual Mean

Ocean volume and tracer transports are commonly computed on density surfaces because doing so approximates the semi-Lagrangian mean advective transport. The resulting density-averaged transport can be related approximately to Eulerian-averaged quantities via the Temporal Residual Mean (TRM), valid in the limit of small isopycnal height fluctuations. This article builds on a formulation of the TRM for volume fluxes within Neutral Density surfaces, (the “NDTRM”), selected because Neutral Density surfaces are constructed to be as neutral as possible while still forming well-defined surfaces. This article derives a TRM, referred to as the “Neutral TRM” (NTRM), that approximates volume fluxes within surfaces whose vertical fluctuations are defined directly by the neutral relation. The purpose of the NTRM is to more closely approximate the semi-Lagrangian mean transport than the NDTRM, because the latter introduces errors associated with differences between the instantaneous state of the modeled/observed ocean and the reference climatology used to assign the Neutral Density variable. It is shown that the NDTRM collapses to the NTRM in the limiting case of a Neutral Density variable defined with reference to the Eulerian-mean salinity, potential temperature and pressure, rather than an external reference climatology, and therefore that the NTRM approximately advects this density variable. This prediction is verified directly using output from an idealized eddy-resolving numerical model. The NTRM therefore offers an efficient and accurate estimate of modeled semi-Lagrangian mean transports without reference to an external reference climatology, but requires that a Neutral Density variable be computed once from the model’s time-mean state in order to estimate isopycnal and diapycnal components of the transport.

P4724Elevated mitral valve pressure gradient is predictive for long-term outcome after percutaneous edge-to-edge mitral valve repair (PMVR) in patients with degenerative MR, but not in functional MR

Abstract Background To analyze the effects of residual mitral regurgitation (MR) and mean mitral valve pressure gradient (MVPG) after percutaneous edge-to-edge mitral valve repair (PMVR) using the MitraClip-system on long term outcome. Methods and results Two hundred fifty-five patients who underwent PMVR were analyzed. Kaplan-Meier and Cox regression analyses were performed to evaluate the impact of residual MR and MVPG on clinical outcome. A combined clinical endpoint (all-cause mortality, MV surgery, redo procedure, implantation of a left ventricular assist device) was used. After PMVR, mean MVPG increased from 1.6±1.0 mmHg to 3.1±1.5 mmHg (p<0.001). Reduction of MR severity to ≤2+ postintervention was achieved in 98.4% of all patients. In the overall patient cohort, residual MR was predictive for the combined endpoint while elevated MVPG >4.4 mmHg was not according to Kaplan-Meier and Cox regression analyses. We then analyzed the cohort with degenerative and that with functional MR separately to account for these different entities.In the cohort with degenerative MR, elevated MVPG was associated with increased occurrence of the primary endpoint, whereas this was not observed in the cohort with functional MR. Conclusions MVPG >4.4 mmHg after MitraClip-implantation was predictive for clinical outcome in the patient cohort with degenerative MR. In the patient cohort with functional MR, MVPG >4.4 mmHg was not associated with increased clinical events. Acknowledgement/Funding This study was supported by grants from the German Research Foundation (KFO 274), the Volkswagen Foundation (Lichtenberg Program) and the German Heart

380 High Resolution Manometry Findings in Patients With Absent Contractility

INTRODUCTION: High resolution manometry (HRM) is a widely utilized diagnostic tool for identifying esophageal motility disorders. The Chicago Classification V3.0 (CC3) applies a hierarchical stepwise approach to the analysis of HRM parameters, allowing for characterization and diagnosis of different disorders. While upper esophageal sphincter (UES) parameters are not utilized in CC3, studies have shown that these values could potentially help in differentiating between different disorders. The aim of this study was to evaluate for trends in esophageal sphincter parameters in patients with absent contractility (AC). METHODS: Reports for esophageal HRM performed at a single referral-based motility center between 2013 and 2018 were retrospectively analyzed. We selected patients diagnosed with either AC (study group) or no CC3 abnormality (control group), and divided them by age group. The primary outcome included differences in parameters related to both the lower esophageal sphincter (LES) and UES. Multivariate regression analysis was used to adjust for age group and sex. RESULTS: A total of 455 patients were included in the study (Figure 1), 52 with AC (mean age 51.4, 50.0% female) and 403 without CC3 abnormality (mean age 52.8, 69.5% female). The primary indication for HRM was more likely to be dysphagia in AC patients (73.1% vs 30.5%, P < 0.01), whereas GERD was more likely to be reported in controls (67.2% vs 25.0%, P < 0.01). AC diagnosis was associated with a lower LES mean basal pressure (14.9 vs 27.0 mmHg, P < 0.01) and LES mean residual pressure (5.9 vs 7.9 mmHg, P < 0.01). However, UES mean residual pressure (2.1 vs 0.1 mmHg, P = 0.02) and UES mean relaxation time to nadir (296.4 vs 228.5 ms, P = 0.04) were significantly higher compared to control. No significant difference in UES mean basal pressure was identified between the study group and control. CONCLUSION: AC affects both the LES and UES, causing decreased LES basal and residual pressures, along with increased UES residual pressures and UES relaxation time to nadir. Our data suggest that AC affects smooth muscle in both the esophageal body and LES, where it causes decreased LES tone. The increased UES residual pressures and relaxation time to nadir might be compensatory adaptation to poor esophageal bolus clearance to prevent esophago-pharyngeal reflux. Utilization of UES parameters in future versions of the Chicago Classification may help users better diagnose and manage AC and related disorders.

Performance characteristic evaluation of a bismuth germanate-based high-sensitivity 5-ring discovery image quality positron emission tomography/computed tomography system as per National Electrical Manufacturers Association NU 2-2012.

National Electrical Manufacturers Association (NEMA) provides guidelines to assess the performance of Positron Emission Tomography (PET). A PET/CT scanner, Discovery IQ, GE Medical systems, Milwaukee, USA was installed in our department which has high a sensitivity PET component. We have performed the NEMA NU-2 2012 quality control tests to evaluate this system on site before clinical use. Performance measurements of the PET scanner were made using the NEMA NU2-2012 procedures for spatial resolution, scatter fraction, sensitivity, count rate loss and random coincidence estimation, Noise Equivalent Count Rate (NECR) and image quality. As per NU2 2012, spatial resolution was measured at 1 cm, 10 cm and 20 cm vertically from the centre and at each of these points resolution was measured at tangential, radial and axial directions. Sensitivity was measured at centre and 10 cm off center vertically from the center. The system sensitivity is reported as an average of the two measured values. Scatter fraction and NECR measurements, Image quality test was also performed. The tangential, radial and axial FWHM were 4.99 mm, 4.20 mm and 4.79 mm at 1 cm off centre, 5.49 mm, 4.69 mm and 4.81 mm at 10 cm off centre and 7.99 mm, 5.07 mm and 4.95 mm at 20 cm off centre respectively. The absolute sensitivity of this scanner was found to be 20.1 cps/kBq. The scatter fraction calculated from the decay method was 37.94% and NECR was 125 kcps. The peak NECR was achieved at activity concentration of 8.7 KBq/ml and the count loss below the peak NECR was found to be 0.68%. Image quality test for, contrast recovery, background variability and lung error residual mean met all specifications. Overall PET performance of Discovery IQ whole-body scanner was satisfactory and the scanner met all the performance specifications required by NEMA 2012.

Predicting tick-borne encephalitis using Google Trends

Data generated through public Internet searching offers a promising alternative source of information for monitoring and forecasting of infectious disease. Here future cases of tick-borne encephalitis (TBE) were predicted using traditional weekly case reports, both with and without Google Trends data (GTD). Data on the weekly number of acute, confirmed TBE cases in Germany were obtained from the Robert Koch Institute. Data relating to the volume of Internet searching on TBE was downloaded from the Google Trends website. Data were split into training and validation parts. A SARIMA (0,1,1) (1,1,1) [52] model was used to describe the weekly TBE case number time series. Google Trends Data was used as an external regressor in a second, as optimal identified SARIMA (4,1,1) (1,1,1) [52] model. Predictions for the number of future cases were made with both models and compared with the validation dataset. GTD showed a significant correlation with reported weekly case numbers of TBE (p < 0.0001). A comparison of forecasted values with reported ones resulted in an RMSE (residual mean squared error) of 0.71 for the model without Google search values, and an RMSE of 0.70 for the Google Trends values enhanced model. However, difference between predictive performances was not significant (Diebold Mariano test, p-value = 0.14).

Performance of NGA Models in Predicting Ground Motion Parameters of The Strong Earthquake

Next Generation Attenuation (NGA) West 1 and 2 models are employed to predict the ground motion parameters of strong earthquake during the 6.9 Mw Kobe Earthquake in 1995. This study is initiated by collecting the data of ground motion parameters of the earthquake. Furthermore, the ground motion prediction is performed by using the NGA models. There are three ground motion parameters observed, i.e. peak ground acceleration (PGA), spectral acceleration (SA) at 0.2 second and SA at 1 second. The performances of the models are evaluated by using the Residual Values and Root Mean Square (RMS) Error. The results show that the NGA models could predict the ground motion parameters quite appropriately. It can be seen from the correlation values of the observed and the predicted values, which is relatively consistent each other, especially for peak ground acceleration. In general, this study could recommend the procedure in selecting the attenuation model for strong earthquakes. The study framework could be implemented to predict the ground motion in other regions.