Postoperative delirium after transcatheter aortic valve replacement: An updated systematic review and meta-analysis.

AIMS: To perform an updated systematic review and meta-analysis of postoperative delirium (POD) after transcatheter aortic valve replacement (TAVR). METHODS: We conducted a systematic literature search of PubMed, Embase, and Cochrane Library databases from the time of the first human TAVR procedure in 2002 until December 24, 2021, which was supplemented by manual searches of bibliographies. Data were collected on incidence rates, risk factors, and/or associated mortality of POD after TAVR. Pooled analyses were conducted using random effects models to yield mean differences, odds ratios, hazard ratios, and risk ratios, with 95% confidence intervals. RESULTS: A total of 70 articles (69 studies) comprising 413,389 patients were included. The study heterogeneity was substantial. The pooled mean incidence of POD after TAVR in all included studies was 9.8% (95% CI: 8.7%-11.0%), whereas that in studies using validated tools to assess for delirium at least once a day for at least 2 consecutive days after TAVR was 20.7% (95% CI: 17.8%-23.7%). According to the level of evidence and results of meta-analysis, independent preoperative risk factors with a high level of evidence included increased age, male sex, prior stroke or transient ischemic attack, atrial fibrillation/flutter, weight loss, electrolyte abnormality, and impaired Instrumental Activities of Daily Living; intraoperative risk factors included non-transfemoral access and general anesthesia; and acute kidney injury was a postoperative risk factor. POD after TAVR was associated with significantly increased mortality (pooled unadjusted RR: 2.20, 95% CI: 1.79-2.71; pooled adjusted RR: 1.62, 95% CI: 1.25-2.10), particularly long-term mortality (pooled unadjusted HR: 2.84, 95% CI: 1.91-4.23; pooled adjusted HR: 1.88, 95% CI: 1.30-2.73). CONCLUSIONS: POD after TAVR is common and is associated with an increased risk of mortality. Accurate identification of risk factors for POD after TAVR and implementation of preventive measures are critical to improve prognosis.

Valvuloplasty of fetal pulmonary atresia with intact ventricular septum and hypoplastic right heart: Mid-term follow-up results.

Objective: This study aimed to analyze and evaluate the results of mid-term follow-up after fetal pulmonary valvuloplasty (FPV) in fetuses with pulmonary atresia with intact ventricular septum (PA/IVS). Methods: From August 31, 2018, to May 31, 2019, seven fetuses with PA/IVS and hypoplastic right heart were included in this study. All underwent echocardiography by the same specialist and were operated on by the same team. Intervention and echocardiography data were collected, and changes in the associated indices noted during follow-up were analyzed. Results: All seven fetuses successfully underwent FPV. The median gestational age at FPV was 27.54 weeks. The average FPV procedural time was 6 â€‹min. Persistent bradycardia requiring treatment occurred in 4/7 procedures. Finally, five pregnancies were successfully delivered, and the other two were aborted. Compared to data before fetal cardiac interventions (FCI), tricuspid valve annulus diameter/mitral valve annulus diameter (TV/MV) and right ventricle diameter/left ventricle diameter (RV/LV) of all fetuses had progressively improved. The maximum tricuspid regurgitation velocity decreased from 4.60 â€‹m/s to 3.64 â€‹m/s. The average follow-up time was 30.40 â€‹± â€‹2.05 months. During the follow-up period, the diameter of the tricuspid valve ring in five children continued to improve, and the development rate of the tricuspid valve was relatively obvious from 6 months to 1 year after birth. However, the development of the right ventricle after birth was relatively slow. It was discovered that there were individual variations in the development of the right ventricle during follow-up. Conclusion: The findings support the potential for the development of the right ventricle and tricuspid valve in fetuses with PA/IVS who underwent FCI. Development of the right ventricle and tricuspid valve does not occur synchronously during pregnancy. The right ventricle develops rapidly in utero, but the development of tricuspid valve is more apparent after birth than in utero.

Predicting grain protein content of field-grown winter wheat with satellite images and partial least square algorithm.

Remote sensing has been used as an important means of modern crop production monitoring, especially for wheat quality prediction in the middle and late growth period. In order to further improve the accuracy of estimating grain protein content (GPC) through remote sensing, this study analyzed the quantitative relationship between 14 remote sensing variables obtained from images of environment and disaster monitoring and forecasting small satellite constellation system equipped with wide-band CCD sensors (abbreviated as HJ-CCD) and field-grown winter wheat GPC. The 14 remote sensing variables were normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), optimized soil-adjusted vegetation index (OSAVI), nitrogen reflectance index (NRI), green normalized difference vegetation index (GNDVI), structure intensive pigment index (SIPI), plant senescence reflectance index (PSRI), enhanced vegetation index (EVI), difference vegetation index (DVI), ratio vegetation index (RVI), Rblue (reflectance at blue band), Rgreen (reflectance at green band), Rred (reflectance at red band) and Rnir (reflectance at near infrared band). The partial least square (PLS) algorithm was used to construct and validate the multivariate remote sensing model of predicting wheat GPC. The research showed a close relationship between wheat GPC and 12 remote sensing variables other than Rblue and Rgreen of the spectral reflectance bands. Among them, except PSRI and Rblue, Rgreen and Rred, other remote sensing vegetation indexes had significant multiple correlations. The optimal principal components of PLS model used to predict wheat GPC were: NDVI, SIPI, PSRI and EVI. All these were sensitive variables to predict wheat GPC. Through modeling set and verification set evaluation, GPC prediction models' coefficients of determination (R2) were 0.84 and 0.8, respectively. The root mean square errors (RMSE) were 0.43% and 0.54%, respectively. It indicated that the PLS algorithm model predicted wheat GPC better than models for linear regression (LR) and principal components analysis (PCA) algorithms. The PLS algorithm model's prediction accuracies were above 90%. The improvement was by more than 20% than the model for LR algorithm and more than 15% higher than the model for PCA algorithm. The results could provide an effective way to improve the accuracy of remotely predicting winter wheat GPC through satellite images, and was conducive to large-area application and promotion.

Rapid Recognition of Field-Grown Wheat Spikes Based on a Superpixel Segmentation Algorithm Using Digital Images.

Wheat spike number, which could be rapidly and accurately estimated by the image processing technology, serves as the basis for crop growth monitoring and yield prediction. In this research, simple linear iterative clustering (SLIC) was performed for superpixel segmentation of the digital images of field-grown wheat. Firstly, certain characteristic color parameters were extracted and analyzed from the digital images, and the classifiers with the highest accuracy were chosen for subsequent image classification. Next, the main body of wheat spike was extracted through a series of morphological transformation and estimate was performed for each region. Backbone of the head was extracted, and the number of inflection points of backbone was detected. Then the wheat spike number was determined by combining the estimate of inflection points of backbone and the estimate for each region. Finally, the wheat spike number estimate was verified under four nitrogen fertilizer levels. The results were as follows: (1) Super green value (Eg) and normalized red green index (Dgr) were used as classification features to recognize wheat spikes, soil and leaves; (2) compared with pixel-based image processing, wheat spike recognition effect was much better after superpixel segmentation, as the main body of wheat spike extracted was more clear and morphology more intact; and (3) wheat plants had better growth under high nitrogen fertilizer level, and the accuracy of wheat spike number estimation was also the highest, which was 94.01%. The growth status was the worst under no nitrogen fertilizer application, and the accuracy of wheat spikes number estimation was also the lowest, which was only 80.8%. After excluding the no nitrogen condition, the accuracy of wheat spikes number estimation among mixed samples with more uniform growth status was up to 93.8%, which was an increase by 10.1% than before the exclusion. Wheat spikes number estimate based on superpixel segmentation and color features was a rapid and accurate method that was applicable to the field environment. However, this method was not recommended for use when the growth status of wheat was poor or of high heterogeneity. The findings provided reference for field-grown wheat yield estimate.

Analysis of Different Hyperspectral Variables for Diagnosing Leaf Nitrogen Accumulation in Wheat.

Hyperspectral remote sensing is a rapid non-destructive method for diagnosing nitrogen status in wheat crops. In this study, a quantitative correlation was associated with following parameters: leaf nitrogen accumulation (LNA), raw hyperspectral reflectance, first-order differential hyperspectra, and hyperspectral characteristics of wheat. In this study, integrated linear regression of LNA was obtained with raw hyperspectral reflectance (measurement wavelength = 790.4 nm). Furthermore, an exponential regression of LNA was obtained with first-order differential hyperspectra (measurement wavelength = 831.7 nm). Coefficients (R2) were 0.813 and 0.847; root mean squared errors (RMSE) were 2.02 g·m-2 and 1.72 g·m-2; and relative errors (RE) were 25.97% and 20.85%, respectively. Both the techniques were considered as optimal in the diagnoses of wheat LNA. Nevertheless, the better one was the new normalized variable (SDr - SDb)/(SDr + SDb), which was based on vegetation indices of R2 = 0.935, RMSE = 0.98, and RE = 11.25%. In addition, (SDr - SDb)/(SDr + SDb) was reliable in the application of a different cultivar or even wheat grown elsewhere. This indicated a superior fit and better performance for (SDr - SDb)/(SDr + SDb). For diagnosing LNA in wheat, the newly normalized variable (SDr - SDb)/(SDr + SDb) was more effective than the previously reported data of raw hyperspectral reflectance, first-order differential hyperspectra, and red-edge parameters.

Remotely Assessing Fraction of Photosynthetically Active Radiation (FPAR) for Wheat Canopies Based on Hyperspectral Vegetation Indexes.

Fraction of photosynthetically active radiation (FPAR), as an important index for evaluating yields and biomass production, is key to providing the guidance for crop management. However, the shortage of good hyperspectral data can frequently result in the hindrance of accurate and reliable FPAR assessment, especially for wheat. In the present research, aiming at developing a strategy for accurate FPAR assessment, the relationships between wheat canopy FPAR and vegetation indexes derived from concurrent ground-measured hyperspectral data were explored. FPAR revealed the most strongly correlation with normalized difference index (NDI), and scaled difference index (N*). Both NDI and N* revealed the increase as the increase of FPAR; however, NDI value presented the stagnation as FPAR value beyond 0.70. On the other hand, N* showed a decreasing tendency when FPAR value was higher than 0.70. This special relationship between FPAR and vegetation index could be employed to establish a piecewise FPAR assessment model with NDI as a regression variable during FPAR value lower than 0.70, or N* as the regression variable during FPAR value higher than 0.70. The model revealed higher assessment accuracy up to 16% when compared with FPAR assessment models based on a single vegetation index. In summary, it is feasible to apply NDI and N* for accomplishing wheat canopy FPAR assessment, and establish an FPAR assessment model to overcome the limitations from vegetation index saturation under the condition with high FPAR value.

Assessment of Fv/Fm absorbed by wheat canopies employing in-situ hyperspectral vegetation indexes.

Chlorophyll fluorescence parameter of Fv/Fm, as an important index for evaluating crop yields and biomass, is key to guide crop management. However, the shortage of good hyperspectral data can hinder the accurate assessment of wheat Fv/Fm. In this research, the relationships between wheat canopy Fv/Fm and in-situ hyperspectral vegetation indexes were explored to develop a strategy for accurate Fv/Fm assessment. Fv/Fm had the highest coefficients with normalized pigments chlorophyll ratio index (NPCI) and the medium terrestrial chlorophyll index (MTCI). Both NPCI and MTCI were increased with the increase in Fv/Fm. However, NPCI value ceased to increase as Fv/Fm reached 0.61. MTCI had a descending trend when Fv/Fm value was higher than 0.61. A piecewise Fv/Fm assessment model with NPCI and MTCI regression variables was established when Fv/Fm value was ≤0.61 and >0.61, respectively. The model increased the accuracy of assessment by up to 16% as compared with the Fv/Fm assessment model based on a single vegetation index. Our study indicated that it was feasible to apply NPCI and MTCI to assess wheat Fv/Fm and to establish a piecewise Fv/Fm assessment model that can overcome the limitations from vegetation index saturation under high Fv/Fm value.