Curve Parameters Research Articles

An empirical model of carbon-ion relative biological effectiveness based on the linear correlation between radiosensitivity to photons and carbon ions

Abstract Objective. To develop an empirical model to predict carbon ion (C-ion) relative biological effectiveness (RBE). 

Approach. We used published cell survival data comprising 360 cell line/energy combinations to characterize the linear energy transfer (LET) dependence of cell radiosensitivity parameters describing the dose required to achieve a given survival level, e.g. 5% (D5%), which are linearly correlated between photon and C-ion radiations. Based on the LET response of the metrics D5% and D37%, we constructed a model containing four free parameters that predicts cells’ linear quadratic model (LQM) survival curve parameters for C-ions, αC and βC, from the reference LQM parameters for photons, αX and βX, for a given C-ion LET value. We fit our model’s free parameters to the training dataset and assessed its accuracy via leave-one out cross-validation. We further compared our model to the local effect model (LEM) and the microdosimetric kinetic model (MKM) by comparing its predictions against published predictions made with those models for clinically relevant LET values in the range of 23-107 keV/μm. 

Main Results. Our model predicted C-ion RBE within ±7%–15% depending on cell line and dose which was comparable to LEM and MKM for the same conditions. 

Significance. Our model offers comparable accuracy to the LEM or MKM but requires fewer input parameters and is less computationally expensive and whose implementation is so simple we provide it coded into a spreadsheet. Thus, our model can serve as a pragmatic alternative to these mechanistic models in cases where cell-specific input parameters cannot be obtained, the models cannot be implemented, or for which their computational efficiency is paramount.

Validity of Laws for Predicting Lifespan of Welded Points Under Variable Loading

The article examines the validity of the laws for predicting the life of welded points under variable amplitude loading, using three steels (HE360D, XE360D and XES) supplied by ArcelorMittal to Renault for the manufacture of automobile chassis parts. The fatigue tests revealed two modes of failure: cracking of the sheets at medium stresses and shearing of the molten core at high forces. For lifespan prediction, three damage laws were studied: Miner's law, Mesmacque and Amrouche law, and a proposed model. Miner's law, despite its simplicity and wide distribution, shows notable limitations in the presence of variable loads, because it does not take into account the interactions between successive load levels. The law of Mesmacque and Amrouche uses a damage indicator linked to the FN curve of the material, allowing a cycle by cycle estimation of the residual lifespan. However, it remains non-conservative at low effort. The proposed model, based on Chaboche law, overcomes some of its theoretical drawbacks, notably the dependence on the calibration parameters of the SN curve. By fitting this curve within a specific calibration window, the model improves forecast accuracy by taking into account parameter variability. This proposed model provides more reliable lifespan predictions that better correlate with experimental results, especially for low effort levels where Miner's law fails. The validation approach is based on comparing the predictions of the damage laws with the experimental results, by evaluating the deviation from the first bisector of the graphs. The results show that Miner's law is unsuitable for effort levels, while the proposed model turns out to be the most precise and conservative. The law of Mesmacque and Amrouche falls between the two, presenting a certain justice but remaining overall non-conservative. In conclusion, the proposed model offers a better match with experimental data, making its predictions more reliable for industrial applications.

FITTING DYNAMICALLY CONSISTENT FORWARD RATE CURVES: ALGORITHM AND COMPARISON

This paper introduces a new methodology for estimating dynamically consistent forward rate curves, which are essential for obtaining arbitrage-free valuation and risk management models. Using U.S. Treasury data from January 2013 to June 2023, we fit dynamically consistent forward rates curves and test the goodness of fit between theoretical and observed prices. We compare the traditional static forward rate curve parameterizations (including Nelson–Siegel, Svensson, and cubic splines) with our new dynamically consistent methodology. The dynamically consistent forward rate curve estimates generally outperform the traditional static methods in reproducing market prices.

Deep drilling in the time domain with DECam II: characterizing the light curves of candidates in the extragalactic fields

Abstract In this second paper on the DECam deep drilling field (DDF) program we release 2,020 optical gri-band light curves for transients and variables in the extragalactic COSMOS and ELAIS fields based on time series observations with a 3-day cadence from semester 2021A through 2023A. In order to demonstrate the wide variety of time domain events detected by the program and encourage others to use the data set, we characterize the sample by presenting a brief analysis of the light curve parameters such as time span, amplitude, and peak brightness. We also present preliminary light curve categorizations, and identify potential stellar variables, active galactic nuclei, tidal disruption events, supernovae (such as Type Ia, Type IIP, superluminous, and gravitationally lensed supernovae), and fast transients. Where relevant, the number of identified transients is compared to the predictions of the original proposal. We also discuss the challenges of analyzing DDF data in the context of the upcoming Vera C. Rubin Observatory and its Legacy Survey of Space and Time, which will include DDFs. Images from the DECam DDF program are available without proprietary period and the light curves presented in this work are publicly available for analysis.

Analysis of stress-strain curve of POM fiber reinforced concrete

ABSTRACT As a new type of synthetic fiber, polyoxymethylene (POM) fiber has the advantages of high tensile strength, good dispersibility, and high bonding strength with cement interface. It has broad application prospects. However, there is limited research on POM fiber reinforced concrete. In order to provide scientific basis for promoting the application of POM fiber, this paper adopts a combination of indoor experiments and numerical simulations to obtain the axial stress-strain complete curves of POM fiber reinforced concrete with different volume dosages and studies the uniaxial compressive properties of POM fiber reinforced concrete. Finally, the constitutive equation parameters of POM fiber reinforced concrete were fitted using the Guo Zhenhai model, and the optimal fiber content was determined. The results indicate that at a POM fiber content of 1.5 kg/m3, the load-displacement curve exhibits optimal shape with the highest residual strength. With increasing fiber content, compressive stress-strain curve parameters initially increase and then decrease, peaking at 1.5 kg/m3, optimizing the mechanical properties of concrete. POM fiber has minimal impact on initial elastic modulus. Thus, optimal mechanical property enhancement is achieved with 1.5 kg/m3 POM fiber content.

Enhancing the diagnostic capacity of [18F]PSMA-1007 PET/MRI in primary prostate cancer staging with artificial intelligence and semi-quantitative DCE: an exploratory study

BackgroundTo investigate the ability of artificial intelligence (AI)-based and semi-quantitative dynamic contrast enhanced (DCE) multiparametric MRI (mpMRI), performed within [18F]-PSMA-1007 PET/MRI, in differentiating benign from malignant prostate tissues in patients with primary prostate cancer (PC).ResultsA total of seven patients underwent whole-body [18F]-PSMA-1007 PET/MRI examinations including a pelvic mpMRI protocol with T2w, diffusion weighted imaging (DWI) and DCE image series. Conventional analysis included visual reading of PET/MRI images and Prostate Imaging Reporting & Data System (PI-RADS) scoring of the prostate. On the prostate level, we performed manual segmentations for time-intensity curve parameter formation and semi-quantitative analysis based on DCE segmentation data of PC-suspicious lesions. Moreover, we applied a recently introduced deep learning (DL) pipeline previously trained on 1010 independent MRI examinations with systematic biopsy-enhanced histopathological targeted biopsy lesion ground truth in order to perform AI-based lesion detection, prostate segmentation and derivation of a deep learning PI-RADS score. DICE coefficients between manual and automatic DL-acquired segmentations were compared. On patient-based analysis, PET/MRI revealed PC-suspicious lesions in the prostate gland in 6/7 patients (Gleason Score-GS ≥ 7b) that were histologically confirmed. Four of these patients also showed lymph node metastases, while two of them had bone metastases. One patient with GS 6 showed no PC-suspicious lesions. Based on DCE segmentations, a distinction between PC-suspicious and normal appearing tissue was feasible with the parameters fitted maximum contrast ratio (FMCR) and wash-in-slope. DICE coefficients (manual vs. deep learning) were comparable with literature values at a mean of 0.44. Further, the DL pipeline could identify the intraprostatic PC-suspicious lesions in all six patients with clinically significant PC.ConclusionFirstly, semi-quantitative DCE analysis based on manual segmentations of time-intensity curves was able to distinguish benign from malignant tissues. Moreover, DL analysis of the MRI data could detect clinically significant PC in all cases, demonstrating the feasibility of AI-supported approaches in increasing diagnostic certainty of PSMA-radioligand PET/MRI.

A Multicriteria Decision Analytic Approach to Systems Resilience

AbstractThis article develops a novel decision-oriented framework that strategically deconstructs systems resilience in a way that focuses on systems’ design, capabilities, and management. The framework helps evaluate and compare how system design choices impact system resilience. First, we propose a resilience score based on a piecewise linear approximation to a resilience curve. Using multicriteria decision analysis principles, we score system design alternatives in terms of system-specific capabilities. We estimate the relevance of these capabilities to resilience curve parameters associated with resilience phases. Finally, we interpret the derivatives of resilience with respect to the curve parameter values as the leverage of these parameters. Using multiple levels of weighted sums of the scores, we calculate the first order impact of system design choices first on a proxy for the generic resilience parameters and then on resilience, which allows situational characteristics to be incorporated in their natural terminology while mapping their impact on resilience with a traceable logic. We illustrate the approach by using existing materials to develop an example comparing engineered designs for minimizing post-wildfire flood impacts.

Modeling of partially oriented spring-exchange magnetic composites

PurposeThe presentation refers to simulations of magnetization processes of the spring-exchange magnetic composites containing magnetically soft and ultra-high coercive phases. In particular, the aim of this study is to investigate the possibility of reducing expensive rare earth (RE) in the so-called neodymium magnets and improving their efficiency.Design/methodology/approachIn order to model hysteresis loops, a special disorder-based Monte Carlo procedure, suitable for irregular geometry of the composites, was applied. The chosen system parameters were defined in order to model Nd2Fe14B/Fe composites.FindingsThe results suggest potential for optimizing hard magnetic composites. Magnetization curve parameters are sensitive to grain coupling and easy magnetization axis ordering. Strong coupling for a single-phase hysteresis loop is unachievable for grains above a certain size, i.e. found to be a few hundred nanometers. Considering these factors and their interdependencies, it’s possible to enhance the |BH|max parameter or reduce the RE content.Research limitations/implicationsThe research was carried out using computer simulations, which by their nature are only approximations of physical processes. The next stage of research is to produce the described composites and test their actual properties.Practical implicationsThe research enhances permanent magnets, boosting efficiency in technologies like wind turbines and electric motors, indirectly benefiting the environment. It also reduces RE elements in magnets for environmental, economic and political gains.Originality/valueThe unique approach is to consider the random orientation of the magnetic anisotropy of the hard magnetic grains, which is close to real powder composites. The results provide valuable guidance for the production process of permanent magnets.

Study on the influence of pipe effect on the vibration characteristics of electro-hydraulic exciter

In this paper, an electro-hydraulic exciter controlled by an alternating current distribution valve is proposed, and a systematic analysis of the influence of pipe effect on the vibration characteristics of the electro-hydraulic exciter is investigated by simulation and experiments. The vibration characteristics curves for different pipe parameters are obtained, and the relative errors between simulation and experiment are evaluated. Moreover, a significant regression model of pipe parameters and vibration characteristics of the electro-hydraulic exciter is established by using the quadratic regression analysis method and the Box-Behnken experiment design method. Significant differences in the influence of pipe parameters on vibration characteristics are obtained by ANOVA (Analysis of Variance), and the influence of pipe parameter interaction on vibration characteristics is discussed. The results show that the relative importance of pipe parameters on the vibration characteristics of electro-hydraulic exciter, from high to low, is as follows: pipe diameter, elastic modulus, and pipe length. Notably, there is also an interaction between the pipe parameters, and the significance of these interactions is ranked from high to low as the interaction between the layers of steel wire and diameter, the interaction between diameter and length, and the interaction between the layers of steel wire and length.

Stacking Ensemble Technique Using Optimized Machine Learning Models with Boruta–XGBoost Feature Selection for Landslide Susceptibility Mapping: A Case of Kermanshah Province, Iran

Landslides cause significant human and financial losses in different regions of the world. A high-accuracy landslide susceptibility map (LSM) is required to reduce the adverse effects of landslides. Machine learning (ML) is a robust tool for LSM creation. ML models require large amounts of data to predict landslides accurately. This study has developed a stacking ensemble technique based on ML and optimization to enhance the accuracy of an LSM while considering small datasets. The Boruta–XGBoost feature selection was used to determine the optimal combination of features. Then, an intelligent and accurate analysis was performed to prepare the LSM using a dynamic and hybrid approach based on the Adaptive Fuzzy Inference System (ANFIS), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and new optimization algorithms (Ladybug Beetle Optimization [LBO] and Electric Eel Foraging Optimization [EEFO]). After model optimization, a stacking ensemble learning technique was used to weight the models and combine the model outputs to increase the accuracy and reliability of the LSM. The weight combinations of the models were optimized using LBO and EEFO. The Root Mean Square Error (RMSE) and Area Under the Receiver Operating Characteristic Curve (AUC-ROC) parameters were used to assess the performance of these models. A landslide dataset from Kermanshah province, Iran, and 17 influencing factors were used to evaluate the proposed approach. Landslide inventory was 116 points, and the combined Voronoi and entropy method was applied for non-landslide point sampling. The results showed higher accuracy from the stacking ensemble technique with EEFO and LBO algorithms with AUC-ROC values of 94.81% and 94.84% and RMSE values of 0.3146 and 0.3142, respectively. The proposed approach can help managers and planners prepare accurate and reliable LSMs and, as a result, reduce the human and financial losses associated with landslide events.

Static histomorphometry parameters can identify bone turnover status in children and adults with chronic kidney disease

IntroductionTetracycline labeling for bone biopsy facilitates quantification of the pace of new bone production. As tetracycline labeling needs to be done prior to biopsy, it cannot be used to assess bone turnover in patients presenting with fractures, yet knowing turnover rate in patients experiencing fractures - especially in those with chronic kidney disease (CKD) - may guide appropriate medical therapy after surgical repair. Therefore, we sought to determine the diagnostic accuracy of static markers of bone turnover relative to tetracycline labeling in a pediatric and adult cohort of patients with chronic kidney disease (CKD) undergoing iliac crest biopsy with histomorphometry. MethodsWe evaluated two cohorts, one of 147 children and young adults ages 18±10 and another of 151 adults ages 49±13 who had undergone iliac crest biopsy with tetracycline labeling for clinical indications of CKD-mineral and bone disorders. We used bone formation rate relative to bone surface (BFR/BS) based on double tetracycline labeling as our gold standard marker of bone turnover. A blinded investigator used light microscopy without fluorescence to measure static bone turnover parameters. We compared the area under the ROC curve (AUC), sensitivity, and specificity of each static parameter with low and high bone turnover based on BFR/BS. ResultsIn the pediatric and adult cohorts, 35 (24 %) and 70 (46 %) had low bone turnover, respectively, and 18 (12 %) and 30 (20 %) had high bone turnover, respectively. The static parameters with the greatest AUCs for low and high turnover were osteoblasts surface/bone surface (Ob.S/BS), osteoclasts surface/bone surface (Oc.S/BS), eroded surface/bone surface (ES/BS), osteoid surface/bone surface (OS/BS), osteoid volume/bone volume (OV/BV), and osteoid thickness (O.Th.) in both cohorts. Ob.S/BS had the highest AUC for low and high turnover in the pediatric cohort (0.8204 and 0.8678, respectively) whereas Oc.S/BS had the highest AUC for low turnover (0.8325) and ES/BS had the highest AUC for high turnover (0.7360) in the adult cohort. DiscussionStatic measures of histomorphometry that do not rely on tetracycline bone labeling can identify low and high bone turnover in children and adults with CKD with moderate to high accuracy. This approach may allow assessment of bone turnover in the setting of clinical fractures where clinicians may have access to bone tissue but where tetracycline labeling is not possible.

Using bearing ratio curves to quantify the surface roughness parameters of fly ash-teff straw ash-based geopolymer mortars

Considering the prominent attributes of surface roughness parameters, the closer their solutions are to real surfaces, the more appropriate they are for civil engineering applications. One of the steps that can be considered for such applications is to incorporate the bearing ratio curve (BRC) parameters in the study of civil engineering materials. In this research, the bearing ratio curves have been used for the first time to quantify the surface roughness attributes of fly ash-teff straw ash-based geopolymer mortars. For various cases, the roughness parameters in the V-group are compared with the roughness parameters in the Rk group, which contributes to the attractiveness of this research. The fly ash-teff straw ash-based geopolymer mortar specimens in this study are dominated by smooth surfaces, as evidenced by the Rvk and Rpk values of less than 0.21 μm for all specimens. The study highlights that if such surfaces are to be used in mortar-to-mortar bonding, imposed surface roughness could be required to enhance the bond strength of the layers. In addition, the results indicate that critical roughness parameters can be identified from the bearing ratio curves when a surface is undergoing roughness transformations. These findings provide a step towards the understanding of BRC parameters for fly ash-teff straw ash-based geopolymer mortar surfaces. Overall, the approach demonstrated in this study could also accelerate and promote the surface roughness characterisation for geopolymer mortars and could be extended to unlock peculiar surface roughness properties for other civil engineering materials.

Changes in spinal curve during dentistry studies measured with a Spinal Mouse device: A five-year prospective study.

Musculoskeletal disorders (MSDs) often arise and develop during dentistry studies. The most affected regions are related to the spine. Possible associations between spinal curve parameters and MSDs have not yet been investigated amongst dentistry students. This longitudinal observational study aimed to determine whether spinal curve changes during dentistry studies, analyse the relationship between objective findings and subjectively declared MSDs and compare spinal curve parameters with those published in the literature. Seventy-three dentistry students answered a questionnaire on MSDs, and were examined using the Spinal Mouse® device at the beginning, in the middle, and at the end of their 5-year study. The spinal curve exhibited a gender diversity in the lumbar lordosis angle, sacrum inclination, and thoracolumbar ratio. From the first to fifth study year, we observed an increase in the range of motions in the sagittal and frontal planes, an increase in the maximal extent of right lateral inclination, and a decrease in maximal left lateral inclination. Whole-spine backward inclination increased only in women, and forward sacral inclination decreased. No statistically significant relationships were found between the objective findings and subjectively declared MSDs. The spinal curve shape differed between men and women and changed during dentistry studies. No objective markers or predictors of MSDs were found amongst the dentistry students. These findings can serve as a benchmark for further studies on the association between MSDs and objective findings.

Climate change is expected to reduce the potential distribution of Ceiba glaziovii in Caatinga, the largest area of dry tropical forest in South America

Ecological niche modeling is a widely used tool to predict species distribution considering current, past, or future climate change scenarios across different geographic areas. Modeling scenarios allow researchers to assess the impacts of climate change on species distribution and identify priority areas for conservation. This study aimed to model the current and future potential distribution of Ceiba glaziovii under different climate change scenarios in Brazil. The MaxEnt algorithm was used to correlate species occurrence points with bioclimatic variables in current and future climate scenarios. Four General Circulation Models (GCMs) from CMIP6 were employed: BCC-CSM2-MR, CNRM-CM6-1, IPSL-CM6A-LR, and MIROC6, considering optimistic and pessimistic projections. The contribution of variables and model accuracy were assessed using the Jackknife statistical test and the Area Under the Curve (AUC) parameter. AUC values for current and future scenarios demonstrated high accuracy. The bioclimatic variables of precipitation and temperature were the main contributors to determining areas with higher habitat suitability. In the future climate scenario, there was a reduction in areas with good climatic suitability for all four GCMs, considering optimistic and pessimistic projections. Among the areas with high habitat suitability, the IPSL-CM6A-1 model in the optimistic projection showed the smallest reduction, while in the pessimistic scenario, all areas with high suitability disappeared. The species' climatic niche is expected to decrease under all tested climate change scenarios. The central areas of the Caatinga and its transition zones exhibit the highest climatic suitability in current and future scenarios and should be prioritized for the species' conservation.

Parameter analysis in stereoelectroencephalography-guided radiofrequency thermocoagulation: A common basis for objective comparison between protocols

ObjectiveStereoelectroencephalography-guided radiofrequency thermocoagulation (SEEG-guided RF-TC) is an invasive procedure based on stereotactic lesioning of cortical targets in the brain using bipolar current through electrode contacts within the SEEG implant. To date, several RF-TC protocols have been described in the literature; however, a consensus has yet to be reached. This work aims to analyze the electrical parameters during RF-TC processes, offering a method to objectively describe and compare different SEEG-guided RF-TC protocols. MethodsThe study included patients who underwent RF-TC procedures at the IRCCS Istituto delle Scienze Neurologiche di Bologna from February 2022 to May 2023. During each procedure, modifications of the following parameters were measured: voltage, current, impedance, and electric power. An ad-hoc algorithm was implemented to detect abrupt impedance raises, which reflects the occurrence of the thermocoagulation. A two-sample t-test was used to compare parameter curves in RF-TC of different brain structures. ResultsA total of ninety-two RF-TC procedures were performed in eight patients according to a standardized protocol. During each procedure, impedance levels started at about 700Ω and rose up to 1300Ω, displaying an erratic pattern characterized by one or multiple raises. All measured parameters exhibited similar trends until the first peak, after which changes were influenced by the frequency of impedance raises. No significant correlations were observed between parameter modifications in distinct anatomical sites of RF-TC. SignificanceThe systematic analysis of electrical parameters may represent a reliable tool to compare different RF-TC protocols, paving the way for identifying optimal configurations for SEEG-guided RF-TC procedures in the future.

Seismic response prediction of composite plate shear walls- concrete filled (C-PSW/CF) using machine learning methods

In the seismic design of composite plate shear walls filled with concrete, referred to as C-PSW/CF, cyclic backbone curves provide invaluable information for effectively withstanding seismic disturbances. The curves evaluate lateral strength, ductility, and energy dissipation capabilities, and guide performance-based design strategies. This study harnesses machine learning (ML) techniques to predict critical parameters of the cyclic backbone curve of C-PSWs/CF. Various types of existing C-PSWs/CF from the literature were categorized for this analysis. ML models, comprising Random Forests (RF), Support Vector Regression (SVR), eXtreme Gradient Boosting (XGB), and Gaussian Process Regression (GPR), were developed to facilitate the predictive tasks. The findings indicate that all models demonstrate robust performance in predicting maximum strength and ultimate strength, yielding R2 scores close to 1.00 for training and testing, and exceeding 0.90 for cross-validation. Additionally, RF, SVR, and XGB exhibit commendable accuracy in predicting yield strength and its corresponding drift ratio.

Identification of the optimal method for measuring left atrium volume as a marker of undetected atrial fibrillation in patients with cryptogenic stroke and high prevalence of overweight

Abstract Introduction Paroxysmal atrial fibrillation (AF) is detected in approximately one-third of patients diagnosed with cryptogenic stroke undergoing prolonged ECG monitoring. Several risk factors for AF have been described, including left atrium (LA) enlargement, LA deformation, age, cardiovascular risk factors, and biomarkers as N-pro-BNP and troponin. Specifically, LA enlargement is a direct marker of atrial cardiopathy, which predisposes to AF. However, the methods to calculate LA enlargement vary. While LA biplane volume indexed to body surface area (BSA) remains the standardized method of choice according to current guidelines, it may underestimate the degree of enlargement in overweight patients. Additionally, overweight produces a systemic inflammatory state which is also associated with AF. Purpose The primary objective of the study was to define the best parameter among LA end systolic volume (LAESV), LAESV indexed to BSA (LAESV/BSA), and LAESV indexed to ideal BSA for identifying patients admitted for cryptogenic stroke with undetected paroxysmal AF. The secondary objective was to assess the accuracy of these parameters in both normal-weight and overweight patients. Methods Consecutive patients over 65 years-old diagnosed with cryptogenic stroke in our tertiary hospital were enrolled. All patients underwent 15-days ambulatory ECG monitoring to screen for undetected paroxysmal AF. Clinical, echocardiographic and laboratory data were prospectively collected. Ideal BSA was calculated using the Devine method. The accuracy of LA volume parameters in classifying patients with undetected paroxysmal AF was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. The analysis was stratified for overweight status (BMI < 25 or BMI ≥ 25). Results A total of 66 patients (Mean age 78.4 ± 7.6 years; 48% male) were included. Of them, 39 (59%) had hypertension, 16 (24%) had diabetes and 39 (59%) were overweight. AF was present in 21 (32%) patients. The mean LAESV, LAESV/BSA, LAESV/ideal BSA were 56.2 ± 23.0 ml, 34.2 ± 12.6 ml/m2, and 34.7 ± 14.1 ml/m2, respectively. Figure 1 illustrates the ROC curves for LA volume parameters in the main cohort and stratified by overweight status. Overall, no relevant differences in AUC were observed among LAESV, LAESV/BSA, LAESV/ideal BSA in each scenario. However, BMI significantly altered the discrimination performance of LA volume measures, which resulted optimal in normal-weight patients (AUC from 0.838 to 0.886) and absent in overweight patients (AUC from 0.838 to 0.521 to 0.564). Conclusions LAESV, LAESV/BSA, LAESV/ideal BSA show similar discrimination performance in identifying undetected AF in patients with cryptogenic stroke. However, the presence of overweight considerably impacts on the predictive value of LA volume parameters in this clinical scenario.ROC curves

Predictive ability and prognostic correlation of global longitudinal strain in patients with negative dipyridamole stress echocardiography by wall motion criteria

Abstract Background Dipyridamole stress echocardiography (DSE) is an established technique to detect coronary artery disease (CAD) and for risk stratification. Speckle Tracking Echocardiography (STE), as an objective and quantitative evaluation of ventricular function, may add incremental value to conventional stress echo analysis. Purpose To assess the prognostic value of rest (Rest-GLS) and peak (Peak-GLS) stress global longitudinal strain (GLS) in patients with suspected CAD and negative DSE according to wall motion index (WMI) criteria. Methods We retrospectively enrolled patients who underwent DSE at our centre for suspected CAD, with normal WMI. Strain Reserve was calculated as Rest GLS – Peak-GLS (Delta-GLS). We also collected data regarding other conventional parameters such as coronary flow reserve (CFR) of left anterior descending (LAD), left ventricle contractile reserve (LVCR), delta left ventricular ejection fraction (Delta-LVEF), heart rate response (HRR). Our primary endpoint was a composite of all-cause death and coronary revascularization. Receiver operating characteristic (ROC) curves were used to compare the predictive ability of the parameters. Adjusted logistic regression analyses were used to assess associations, also modelled as a restricted cubic spline. Results A total of 355 patients were included in the analysis (median age 68.2, IQR 58.2-75.2, 38.0% female). At follow-up, 58 patients met the primary endpoint. Figure 1 shows the ROC curves of different DSE parameters. Delta-GLS and Peak-GLS showed high predictive ability of composite endpoint events (area under the curve [AUC] 0.87, 95% Confidence Interval [CI] 0.82-0.92, AUC, 0.85, 95% CI 0.79-0.91, respectively), which was significantly higher compared with other DSE parameters, included Basal-GLS (P for difference for all < 0.001). After adjustments for age, previous CAD, sex, and diabetes, reducing values of Rest-GLS, Peak-GLS and Delta-GLS were significantly associated with higher odds of experiencing composite endpoint events (Basal-GLS, odds ratio [OR] 1.13, 95% CI 1.04-1.24; Peak-GLS, OR 1.45, 95% CI 1.32-1.63; Delta-GLS, OR 1.73, 95% CI 1.50-2.04). Plotting the results of the logistic regression analysis on a restricted cubic spline, reducing values of Peak-GLS and Delta-GLS showed a linear increase for the risk of adverse events, while Rest-GLS showed a plateau for lower values. Conclusion The use of GLS parameters in DSE can help to better stratify the risk for the patients since they are associated with adverse cardiovascular events.Figure 1Figure 2

Prognostic value of multiparametric cardiac magnetic resonance after revascularized STEMI

Abstract Despite early revascularization of STEMI through angioplasty, the prognosis following STEMI remains poor for a non-negligible percentage of patients (1). In this setting, multiple imaging parameters have demonstrated their prognostic value. The aim of the present study is to evaluate which of these parameters are the most potent predictors of events following revascularized STEMI. Methods A prospective observational study was conducted, including a cohort of revascularized STEMI patients and who underwent cardiac MRI during the first week. The protocol included cardiac functional analysis and with b-SSFP sequences for evaluation of volumes, mass, ejection fraction and also circumferential and radial strain with feature tracking. STIR sequences were used to assess edema and salvaged myocardium, IR-FGE sequences were employed for evaluation of necrosis size and microvascular obstruction. Intra-infarct hemorrhage assessment was performed using T2* mapping sequences as previosly described (2,3,4) . A semi-automatic analysis was conducted, considering as microvascular obstruction segments with hypointensity in the infarct core and as segments with hemorrhage those with a T2* less than 20 ms. The association of imaging parameters with cardiovascular events in the first year following STEMI was studied using ROC curves. Variables with significant association were analyzed using a multivariate logistic regression model. Results Ninety-four patients with a mean age of 63 (SD 13) years were included. Nine of them had cardiovascular events in the first year and two of them died. The relative extent of necrosis and microvascular obstruction, minimal radial strain, left ventricular ejection fraction (LVEF) and indexed left ventricular end- systolic volume (ILVESV) showed significant association with cardiovascular events after STEMI. Variables with significant association were entered into a multivariate predictive model, revealing that the extent of microvascular obstruction (OR: 1.9, p=0.02) and the left ventricular end-systolic volume (OR: 1.04, p<0.01) are the only independent predictors of cardiovascular events following STEMI. Conclusion Although multiple imaging parameters have shown prognostic value after STEMI, the ILVESV and the extent of microvascular obstruction are the only independent predictors of events following STEMI.Multiparametric cardiac MR in STEMIAREA UNDER ROC CURVE OF CMR PARAMETERS

Radiomics-based multimodality prediction of left atrial appendage thrombus using coronary computed tomography angiography

Abstract Background Timely detection of thrombus in the left atrial appendage (LAA) may help guide decision-making. However, existing models have challenges in extracting comprehensive image information. It has been shown that radiomics-based prediction models can be used for accurate identification. This study investigates potential radiomic markers and constructs a highly efficient multimodality prediction model for LAA thrombus in patients undergoing coronary computed tomography angiography (CCTA). Methods We retrospectively enrolled patients with nonvalvular atrial fibrillation (NVAF) who underwent CCTA and transesophageal echocardiography (TEE) from May 2015 to May 2020. These patients were classified into thrombus or non-thrombus groups based on TEE findings. The data were balanced by resampling and assigned to training or test sets. Radiomics was conducted on the extracted features, and a random forest algorithm was employed for feature selection and importance ranking. We constructed different multimodality thrombus prediction models and compared their performance using the area under the receiver operating characteristic curve (AUC) and other efficacy parameters. Results Among 670 patients (60.17±10.98 years, 70.1% male), 5.2% (n=35) had LAA thrombi. A total of 1232 radiomics features were extracted, with 25 features selected using a random forest for modeling. Two radiomics features (Ibp_3D_m1_firstorder Skewness and original_shape_Flatness) ranked the highest. The radiomics-based multimodality prediction model achieved an AUC of 0.964 (95% confidence interval [CI]: 0.947-0.982), an accuracy of 0.926 (95% CI: 0.925-0.926), and an F1 score of 0.924, outperforming other traditional prediction models. Decision curve analysis also indicated that this model provided the best net clinical benefit. Conclusions Radiomics facilitates a comprehensive exploration of predictors associated with LAA thrombi. A radiomics-based multimodality prediction model can significantly enhance the efficiency of non-invasively detecting thrombi.Receiver operating characteristic curveDecision curve analysis