Search

BackgroundCoronary obstruction (CO) is a rare but devasting complication during transcatheter aortic valve replacement (TAVR).ObjectivesWe aim to demonstrate that the predicted distance between the coronary ostia and the closest structure derived with patient-specific computer simulation is associated with CO risk during TAVR.MethodsWe retrospectively analysed 14 aortic stenosis patients who underwent TAVR through finite element simulation. The frame deformation predicted with patient-specific computer simulation was qualitatively and quantitatively compared to the post-operative device deformation. The minimum distance between each coronary ostium and the closest structure was calculated and compared in patients who developed CO, at high risk of CO, and at no risk of CO.ResultsFour patients experienced CO during TAVR, 5 patients were at high risk of CO, and the remaining 5 patients had no risk of CO. A high coefficient of determination was obtained for all measurements extracted from the simulated device and the post-operative device (≥0.95). Simulations predicted shorter distance between the coronary ostium and the closest structure in patients who experienced CO, compared to patients at high risk of CO or who did not experience this complication (right coronary: 5.9 vs. 6.8 vs. 8.8 mm, left coronary: 3.0 vs. 3.3 vs. 6.5 mm respectively).ConclusionsThe distance between the coronary ostium and the closest structure was lower in patients who experienced CO during TAVR through patient-specific computational simulation. This technology enables coronary obstruction analysis before TAVR in the future.

BackgroundThere are only limited reports on the trends of NT-proBNP after transcatheter aortic valve replacement (TAVR) in aortic stenosis (AS) and even fewer report on the prognostic value of the NT-proBNP trajectory following TAVR.ObjectivesThis study aims to investigate short-term NT-proBNP trajectory following TAVR and explore its association with clinical outcomes in TAVR recipients.MethodsAortic stenosis patients undergoing TAVR were included if they had NT-proBNP levels recorded at baseline, prior to discharge, and within 30 days after TAVR. We used latent class trajectory models to identify NT-proBNP trajectories based on their trends over time.ResultsThree distinct NT-proBNP trajectories were identified from 798 TAVR recipients, which were named class 1 (N = 661), class 2 (N = 102), and class 3 (N = 35). Compared to those with trajectory class 1, patients with trajectory class 2 had a more than 2.3-fold risk of 5-year all-cause death and 3.4-fold risk of cardiac death, while patients with trajectory class 3 had a more than 6.6-fold risk of all-cause death and 8.8-fold risk of cardiac death. By contrast, the groups had no differences in 5-year hospitalization rates. In multivariable analyses, the risk of 5-year all-cause mortality was significantly higher in patients with trajectory class 2 (HR 1.90, 95% CI 1.03–3.52, P = 0.04) and class 3 (HR 5.70, 95% CI 2.45–13.23, P < 0.01).ConclusionOur findings implied different short-term evolution of NT-proBNP levels in TAVR recipients and its prognostic value for AS patients following TAVR. NT-proBNP trajectory may have further prognostic value, in addition to its baseline level. This may aid clinicians with regards to patient selection and risk prediction in TAVR recipients.

Background and purposeFutile recanalization occurs when the endovascular thrombectomy (EVT) is a technical success but fails to achieve a favorable outcome. This study aimed to use machine learning (ML) algorithms to develop a pre-EVT model and a post-EVT model to predict the risk of futile recanalization and to provide meaningful insights to assess the prognostic factors associated with futile recanalization.MethodsConsecutive acute ischemic stroke patients with large vessel occlusion (LVO) undergoing EVT at the National Advanced Stroke Center of Nanjing First Hospital (China) between April 2017 and May 2021 were analyzed. The baseline characteristics and peri-interventional characteristics were assessed using four ML algorithms. The predictive performance was evaluated by the area under curve (AUC) of receiver operating characteristic and calibration curve. In addition, the SHapley Additive exPlanations (SHAP) approach and partial dependence plot were introduced to understand the relative importance and the influence of a single feature.ResultsA total of 312 patients were included in this study. Of the four ML models that include baseline characteristics, the “Early” XGBoost had a better performance {AUC, 0.790 [95% confidence intervals (CI), 0.677–0.903]; Brier, 0.191}. Subsequent inclusion of peri-interventional characteristics into the “Early” XGBoost showed that the “Late” XGBoost performed better [AUC, 0.910 (95% CI, 0.837–0.984); Brier, 0.123]. NIHSS after 24 h, age, groin to recanalization, and the number of passages were the critical prognostic factors associated with futile recanalization, and the SHAP approach shows that NIHSS after 24 h ranks first in relative importance.ConclusionsThe “Early” XGBoost and the “Late” XGBoost allowed us to predict futile recanalization before and after EVT accurately. Our study suggests that including peri-interventional characteristics may lead to superior predictive performance compared to a model based on baseline characteristics only. In addition, NIHSS after 24 h was the most important prognostic factor for futile recanalization.

Background and purposeFutile recanalization occurs in a significant proportion of patients with basilar artery occlusion (BAO) after endovascular thrombectomy (EVT). Therefore, our goal was to develop a visualized nomogram model to early identify patients with BAO who would be at high risk of futile recanalization, more importantly, to aid neurologists in selecting the most appropriate candidates for EVT.MethodsPatients with BAO with EVT and the Thrombolysis in Cerebral Infarction score of ≥2b were included in the National Advanced Stroke Center of Nanjing First Hospital (China) from October 2016 to June 2021. The exclusion criteria were lacking the 3-month Modified Rankin Scale (mRS), age 2, and unavailable baseline CT imaging. Potential predictors were selected for the construction of the nomogram model and the predictive and calibration capabilities of the model were assessed.ResultsA total of 84 patients with BAO were finally enrolled in this study, and patients with futile recanalization accounted for 50.0% (42). The area under the curve (AUC) of the nomogram model was 0.866 (95% CI, 0.786–0.946). The mean squared error, an indicator of the calibration ability of our prediction model, was 0.025. A web-based nomogram model for broader and easier access by clinicians is available online at https://trend.shinyapps.io/DynNomapp/.ConclusionWe constructed a visualized nomogram model to accurately and online predict the risk of futile recanalization for patients with BAO, as well as assist in the selection of appropriate candidates for EVT.

Background and Purpose: Treatment for mild stroke remains an open question. We aim to develop a decision support tool based on machine learning (ML) algorithms, called DAMS (Disability After Mild Stroke), to identify mild stroke patients who would be at high risk of post-stroke disability (PSD) if they only received medical therapy and, more importantly, to aid neurologists in making individual clinical decisions in emergency contexts.Methods: Ischemic stroke patients were prospectively recorded in the National Advanced Stroke Center of Nanjing First Hospital (China) between July 2016 and September 2020. The exclusion criteria were patients who received thrombolytic therapy, age 5. The primary outcome was PSD, corresponding to 3-month mRS ≥ 2. We developed five ML models and assessed the area under curve (AUC) of receiver operating characteristic, calibration curve, and decision curve analysis. The optimal ML model was selected to be DAMS. In addition, SHapley Additive exPlanations (SHAP) approach was introduced to rank the feature importance. Finally, rapid-DAMS (R-DAMS) was constructed for a more urgent situation based on DAMS.Results: A total of 1,905 mild stroke patients were enrolled in this study, and patients with PSD accounted for 23.4% (447). There was no difference in AUCs between the five models (ranged from 0.691 to 0.823). Although there was similar discriminative performance between ML models, the support vector machine model exhibited higher net benefit and better calibration (Brier score, 0.159, calibration slope, 0.935, calibration intercept, 0.035). Therefore, this model was selected for DAMS. In addition, SHAP approach showed that the most crucial feature was NIHSS on admission. Finally, R-DAMS was constructed and there was similar discriminative performance between R-DAMS and DAMS, but the former performed worse on calibration.Conclusions: DAMS and R-DAMS, as prediction-driven decision support tools, were designed to aid clinical decision-making for mild stroke patients in emergency contexts. In addition, even within a narrow range of baseline scores, NIHSS on admission is the strongest feature that contributed to the prediction.

Background: Patient-specific computer simulation of transcatheter aortic valve replacement (TAVR) can provide unique insights in device-patient interaction.Aims: This study was to compare transcatheter aortic valve sealing behavior in patients with bicuspid aortic valves (BAV) and tricuspid aortic valves (TAV) through patient-specific computational modeling.Methods: Patient-specific computer simulation was retrospectively performed with FEops HEARTguide for TAVR patients. Simulation output was compared with postprocedural computed tomography and echocardiography to validate the accuracy. Skirt malapposition was defined by a distance larger than 1 mm based on the predicted device-patient interaction by quantifying the distance between the transcatheter heart valve (THV) skirt and the surrounding anatomical regions.Results: In total, 43 patients were included in the study. Predicted and observed THV frame deformation showed good correlation (R2 ≥ 0.90) for all analyzed measurements (maximum diameter, minimum diameter, area, and perimeter). The amount of predicted THV skirt malapposition was strongly linked with the echocardiographic grading of paravalvular leakage (PVL). More THV skirt malapposition was observed for BAV cases when compared to TAV cases (22.7 vs. 15.5%, p < 0.05). A detailed analysis of skirt malapposition showed a higher degree of malapposition in the interleaflet triangles section for BAV cases as compared to TAV patients (11.1 vs. 5.8%, p < 0.05).Conclusions: Patient-specific computer simulation of TAVR can accurately predict the behavior of the Venus A-valve. BAV patients are associated with more malapposition of the THV skirt as compared to TAV patients, and this is mainly driven by more malapposition in the interleaflet triangle region.

Chouguiyu, a Chinese traditional fermented fish, is famous for its uniquely strong odor and desirable taste. However, traditional spontaneous fermentation often resulted in contamination and unstable quality of products. In this study, individual or conjunctive inoculation of two indigenous lactic acid bacteria (LAB), Lactococcus lactis M10 and Weissella cibaria M3, was tested for their effect on improving Chouguiyu’s quality. It was shown that inoculation would not affect the system’s pH, while increased the total bacteria count and lactic acid bacteria amounts. Matrix-assisted laser desorption/ionization time-of-flight mass (MALDI-TOF) analysis results revealed that Lactoc. lactis M10 and W. cibaria M3 could quickly occupy a dominant position in the ecosystem, and Lactoc. lactis M10 played an important role in the control of spoilage bacteria. Volatile basic nitrogen (TVB-N), thiobarbituric acid reactive substances (TBARS), and biogenic amines results also showed that Lactoc. lactis M10 had a positive effect on improving the product’s quality. Co-inoculation of Lactoc. lactis M10 and W. cibaria M3 could promote the formation of flavor according to the E-nose and gas chromatography-mass spectrometer (GC-MS) analyses, especially for the aroma-active and key volatile compounds. PCA plots of E-nose and hierarchical clustering analysis of GC-MS profiles revealed that the co-inoculation sample at the fifth day (LW5) was the most similar to the natural fermentation sample at the seventh day (C7). The overall acceptance of LW5 was also the closest to that of C7 in sensory evaluation. In conclusion, mixed starter culture was shown to have a good effect on improving product quality and enhancing flavor with fermentation time shortened by 29%.

The microbial community during the fermentation of industrial paocai, a lactic acid fermented vegetable food, was investigated via combined denaturing gradient gel electrophoresis (DGGE) and metagenomic sequencing. Firmicutes and Proteobacteria were identified as the dominant phyla during the fermentation. DGGE results of the bacterial community analysis showed that many genera were observed during the fermentation of industrial paocai, but the same predominant genus and species were observed: Lactobacillus and Lactobacillus (L.) alimentarius/L. paralimentarius. The abundance of L. alimentarius/L. paralimentarius increased fast during the initial stage of fermentation and approximately remained constant during the later stage. Metagenomic sequencing was used to finally identify the predominant species and their genetic functions. Metabolism was the primary functions of the microbial community in industrial paocai fermentation, including carbohydrate metabolism (CM), overview (OV), amino acid metabolism (AAM), nucleotide metabolism (NM), energy metabolism (EM), etc. The predominant species L. alimentarius and L. paralimentarius were involved in plenty of pathways in metabolism and played different roles in the metabolism of carbohydrate, amino acid, lipid to form flavor compounds during industrial paocai fermentation. This study provided valuable information about the predominant species in industrial paocai and its functional properties, which could enable us to advance our understanding of the fermentation mechanism during fermentation of industrial paocai. Our results will advance the understanding of the microbial roles in the industrial paocai fermentation and provide a theoretical basis for improving the quality of industrial paocai products.

Background and purpose: Futile recanalization occurs when the endovascular thrombectomy (EVT) is a technical success but fails to achieve a favorable outcome. This study aimed to use machine learning (ML) algorithms to develop a pre-EVT model and a post-EVT model to predict the risk of futile recanalization and to provide meaningful insights to assess the prognostic factors associated with futile recanalization. Methods: Consecutive acute ischemic stroke patients with large vessel occlusion (LVO) undergoing EVT at the National Advanced Stroke Center of Nanjing First Hospital (China) between April 2017 and May 2021 were analyzed. The baseline characteristics and peri-interventional characteristics were assessed using four ML algorithms. The predictive performance was evaluated by the area under curve (AUC) of receiver operating characteristic and calibration curve. In addition, the SHapley Additive exPlanations (SHAP) approach and partial dependence plot were introduced to understand the relative importance and the influence of a single feature. Results: A total of 312 patients were included in this study. Of the four ML models that include baseline characteristics, the "Early" XGBoost had a better performance {AUC, 0.790 [95% confidence intervals (CI), 0.677-0.903]; Brier, 0.191}. Subsequent inclusion of peri-interventional characteristics into the "Early" XGBoost showed that the "Late" XGBoost performed better [AUC, 0.910 (95% CI, 0.837-0.984); Brier, 0.123]. NIHSS after 24 h, age, groin to recanalization, and the number of passages were the critical prognostic factors associated with futile recanalization, and the SHAP approach shows that NIHSS after 24 h ranks first in relative importance. Conclusions: The "Early" XGBoost and the "Late" XGBoost allowed us to predict futile recanalization before and after EVT accurately. Our study suggests that including peri-interventional characteristics may lead to superior predictive performance compared to a model based on baseline characteristics only. In addition, NIHSS after 24 h was the most important prognostic factor for futile recanalization. [ABSTRACT FROM AUTHOR]