Mahalanobis Distance Research Articles

Prediction of IGBT Gate Oxide Layer's Performance Degradation Based on MultiScaleFormer Network.

Insulated gate bipolar transistors (IGBTs) are widely used in power electronic devices, and their health prediction problems have attracted much attention in the field of power electronic equipment health management. The performance degradation of IGBT gate oxide is one of the most important failure modes. In order to analyze this failure mechanism and the ease of implementation of a monitoring circuit, the gate leakage current of IGBTs was selected as the fault precursor parameter for the degradation of their gate oxide performance, and feature selection and fusion were carried out by using time domain characteristic analysis, grayscale correlation, Mahalanobis distance, Kalman filter, and other methods. Thus, a health indicator was obtained to characterize the degradation of IGBT performance, which was used to indicate the degree of aging of the IGBT gate oxide layer. In this paper, we propose an improved degradation prediction model called MultiScaleFormer, inspired by advanced design ideas of the iTransformer network architecture, combined with the health parameters of IGBTs to construct a degradation prediction model for the IGBT gate oxide layer. MultiScaleFormer showed the highest fitting accuracy compared with the Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Support Vector Regression (SVR), Gaussian Process Regression (GPR), CNN-LSTM, and Transformer models in our experiment. The mean absolute error (MAE) of the MultiScaleFormer prediction was as low as 0.0087. Extraction of the health indicator and the construction and verification of the degradation prediction model were carried out on the dataset released by the NASA-Ames Laboratory. These results demonstrate the feasibility of the gate leakage current as a fault precursor parameter for IGBT gate oxide failure, and the feasibility and accuracy of the MultiScaleFormer prediction model for IGBT performance degradation.

Population affinities in pre-colonial West Africa: The case of the burial cave Iroungou (Gabon, 14th-15th century CE).

Our knowledge of the populations of sub-Saharan Africa in the periods before European colonization is limited. Few archeological sites containing human remains have been identified, and written sources for these periods are rare. The discovery in 2018 of the Iroungou sepulchral cave (Gabon), whose use predates the arrival of the Portuguese (14th-15th centuries CE), is an exceptional source of information: at least 28 individuals associated with numerous metal artifacts were found there. The anthropobiological remains were left in situ, but the eight best preserved crania were digitized. This study focuses on the population affinities of these crania, whose morphology was described using 237 landmarks. Geometric morphometric analyses were used to compare the eight Iroungou specimens with 154 individuals representing 12 well-defined African populations. After alignment (Generalized Procrustes Analysis), morphological affinity was assessed using Euclidean and Mahalanobis distances, and posterior probabilities of population membership (discriminant analysis). Results indicate that the eight Iroungou crania have, on average, more affinity with Bayaka Pygmy, followed by Central African Bantu. Nevertheless, individually, the Iroungou specimens show an important morphological variation and the eight crania can be separated into different affinity groups: Bayaka and Central African Bantu, KhoeSan, and East-African Bantu. Finally, one individual presents strong affinity with Somalis. This phenetic mapping of the Iroungou sample raises questions about the profile of the individuals deposited in the cave in a geographical area known for the Loango pre-colonial kingdom, which ruling class seemed to have had privileged relationships with the Pygmy populations.

The application of the analysis framework for compound extreme event dependencies in China

AbstractThe escalation of compound extreme events has resulted in noteworthy economic and property losses. Recognizing the intricate interconnections among these events has become imperative. To tackle this challenge, we have formulated a comprehensive framework for the systematic analysis of their dependencies. This framework consists of three steps. (1) Define extreme events using Mahalanobis distance thresholds. (2) Represent dependencies among multiple extreme events through a point process‐based method. (3) Verify dependencies with residual tail coefficients, determining the final dependency structure. Applying this framework to assess the extreme dependence of precipitation on wind speed and temperature in China, revealed four distinct dependency structures. In northern, Jianghuai, and southern China, precipitation heavily relies on wind speed, while temperatures maintain relative independence. In northeastern and northwestern China, precipitation exhibits relative independence, yet a notable dependence exists between temperatures and wind speed. In southwestern China, precipitation strongly depends on temperature, while wind speed remains relatively independent. The Qinghai–Tibet Plateau region displays a significant dependence relationship among precipitation, wind speed, and temperature, with weaker dependence between extreme wind speed and temperature. This framework is instrumental for analyzing dependencies among extreme values in compound events.

Incipient fault detection for dynamic processes with canonical variate residual statistics analysis

In modern complex industrial operations, timely fault detection is imperative. While statistical process monitoring is widely used in practice, conventional approaches are usually insensitive to incipient faults (IFs) whose magnitudes are not obvious. To this end, an innovative approach is presented for IF detection in dynamic processes. To begin with, canonical variate residuals (CVRs) are generated by using the canonical variate dissimilarity analysis (CVDA) algorithm. The next step involves calculating statistics for the CVRs and arranging a corresponding statistic matrix. Afterward, the Mahalanobis distance index is constructed for fault detection purpose. The main reasons that this developed approach possesses high sensitivity to IFs in dynamic processes lie in the utilization of CVDA and the idea of monitoring extracted statistics rather than original residuals. Finally, its effectiveness and merits are demonstrated via a numerical example and a benchmark process.

Towards characterising rocky worlds: Trends in chemical make-ups of M dwarfs versus GK dwarfs

Elemental abundances of Sun-like stars have been shown to be crucial for understanding the detailed properties of planets surrounding them. However, accurately measuring elemental abundances of M stars, the most abundant class of stars in the solar neighbourhood, is challenging due to their faintness and pervasive molecular features in optical photospheric spectra. As a result, elemental abundances of Sun-like stars have been proposed to constrain those of M stars, particularly by scaling X/H given measured Fe/H This work aims to test the robustness of this convenient practice based on two selected sets of M- and GK-dwarf stellar abundances and a set of rigorous statistical methods. We compiled the elemental abundances of a sample of up to 43 M dwarfs for ten major rock-forming elements (Fe, C, O, Mg, Si, Al, Ca, Na, Ni, and Ti) from high-resolution near-infrared stellar surveys including APOGEE, CARMENES, and Subaru. We carried out bootstrap-based linear regressions on the selected sample of M dwarfs to constrain the statistical trends of X/H versus Fe/H and then compare them with those of GK dwarfs (sampled from the GALAH database). We then applied a two-sample, multivariate Mahalanobis Distance test to assess the significance of the differences in the X/H Fe/H trends for individual elemental pairs between M and GK dwarfs. We find that the null hypothesis -- that is, no significant difference in the chemical trends of X/H versus Fe/H between M and GK dwarfs -- is strongly rejected for all elements except for Si, for which the rejection is marginal ($p$-value close to 0.05), and Na and Ni, for which the results are inconclusive. This finding suggests that assuming no difference may result in biased results, and thus inaccurate constraints on characterising rocky planets around M dwarfs by scaling the (unmeasured) chemical abundances of planet-hosting M dwarfs from the chemical trends of X/H Fe/H determined by GK dwarfs. It is therefore crucial for both the stellar and exoplanet communities to be aware of these observed differences. To better understand these differences, we advocate for dedicated modelling techniques for M-dwarf atmospheres and an increasing set of benchmark, homogeneous abundance analyses. Intermediately, our statistically constrained trends of X/H Fe/H for M dwarfs provide a new constraint on estimating M-dwarf elemental abundances given measured Fe/H and further on characterising the detailed properties of M-dwarf-hosted rocky worlds.

Incidence of Metaphorical Virtual Classrooms and Interactive Learning Objects in the Interaction of Online Students: An Ecuadorian Case Study

This study explored the incidence of metaphorical virtual classrooms and interactive learning objects in the interaction of students in online mode. The main objective was to analyze how these digital tools, driven by a set of strategies to promote their use, affect the interaction of students in the virtual classroom system and their derived effects. To this end, the latest version of Moodle was implemented in conjunction with gamification plugins and interactive tools in the higher education institution used as a case study. The methodology consisted of data collection through ordinal instruments applied to the teachers and student performance metrics gathered using a plugin developed to extract accurate metrics of each student’s usage and performance through direct queries to the Moodle database and its processing through a neural network. This facilitated the collection of standardized data on the actual metrics of each virtual classroom at the end of the teaching of each subject from both the previous LMS and the newly implemented one. This data was then analyzed using advanced statistical techniques, including Mahalanobis distances, confirmatory factor analysis, and the Wilcoxon signed-rank test. These methods provided a compelling comparison between the old and new systems, revealing significant improvements in the metrics and factors evaluated. The results showed a significant improvement in teachers’ perceptions of the usability of the virtual classroom system and an increase in students’ academic performance, interaction, progress, and time spent learning in virtual contexts. These results provide solid empirical evidence of the added value of these educational tools as effective strategies for improving student interaction, performance, and motivation in online education.

MVDR Algorithm Based on Loading Factor and Sample Selection Strategy

The MVDR (Minimum Variance Distortionless Response) algorithm is a classic Wiener filtering method used for beamforming in array signal processing. In a one-dimensional linear array high-frequency ground wave radar system, it can be employed to suppress various types of ionospheric clutter. A key step in suppression is the use of maximum likelihood estimation (MLE) to estimate the ionospheric clutter covariance matrix. However, MLE typically assumes that samples are independently and identically distributed (i.i.d.). Traditional MVDR algorithms estimate the clutter covariance matrix using all samples, which may not satisfy the i.i.d. condition. Therefore, this paper proposes a new sample selection strategy that utilizes the Mahalanobis distance method to select samples. Meanwhile, due to the very small numerical values of the ionospheric clutter covariance matrix, even if the matrix is full-rank, numerical instability may occur during inversion. To address this issue, the paper introduces 4 different forms of regularization factors. Empirical data demonstrate that the proposed new method offers better suppression of ionospheric clutter compared to the traditional MVDR algorithm.

An Optimal Spatio-Temporal Hybrid Model Based on Wavelet Transform for Early Fault Detection.

An optimal spatio-temporal hybrid model (STHM) based on wavelet transform (WT) is proposed to improve the sensitivity and accuracy of detecting slowly evolving faults that occur in the early stage and easily submerge with noise in complex industrial production systems. Specifically, a WT is performed to denoise the original data, thus reducing the influence of background noise. Then, a principal component analysis (PCA) and the sliding window algorithm are used to acquire the nearest neighbors in both spatial and time dimensions. Subsequently, the cumulative sum (CUSUM) and the mahalanobis distance (MD) are used to reconstruct the hybrid statistic with spatial and temporal sequences. It helps to enhance the correlation between high-frequency temporal dynamics and space and improves fault detection precision. Moreover, the kernel density estimation (KDE) method is used to estimate the upper threshold of the hybrid statistic so as to optimize the fault detection process. Finally, simulations are conducted by applying the WT-based optimal STHM in the early fault detection of the Tennessee Eastman (TE) process, with the aim of proving that the fault detection method proposed has a high fault detection rate (FDR) and a low false alarm rate (FAR), and it can improve both production safety and product quality.

Multi-Site Wind Speed Prediction Based on Graph Embedding and Cyclic Graph Isomorphism Network (GIN-GRU)

Accurate and reliable wind speed prediction is conducive to improving the power generation efficiency of electrical systems. Due to the lack of adequate consideration of spatial feature extraction, the existing wind speed prediction models have certain limitations in capturing the rich neighborhood information of multiple sites. To address the previously mentioned constraints, our study introduces a graph isomorphism-based gated recurrent unit (GIN-GRU). Initially, the model utilizes a hybrid mechanism of random forest and principal component analysis (PCA-RF) to discuss the feature data from different sites. This process not only preserves the primary features but also extracts critical information by performing dimensionality reduction on the residual features. Subsequently, the model constructs graph networks by integrating graph embedding techniques with the Mahalanobis distance metric to synthesize the correlation information among features from multiple sites. This approach effectively consolidates the interrelated feature data and captures the complex interactions across multiple sites. Ultimately, the graph isomorphism network (GIN) delves into the intrinsic relationships within the graph networks and the gated recurrent unit (GRU) integrates these relationships with temporal correlations to address the challenges of wind speed prediction effectively. The experiments conducted on wind farm datasets for offshore California in 2019 have demonstrated that the proposed model has higher prediction accuracy compared to the comparative model such as CNN-LSTM and GAT-LSTM. Specifically, by modifying the network layers, we achieved higher precision, with the mean square error (MSE) and root mean square error (RMSE) of wind speed at a height of 10 m being 0.8457 m/s and 0.9196 m/s, respectively.

Craniometric Relationships of Migrant Victims of the April 18, 2015 Shipwreck Off the Coast of Libya

One of the most tragic events involving African migrants’ attempts to get to Europe was the 2015 shipwreck off the coast of Libya. More than 300 crania were recovered and are currently in the Laboratory of Forensic Anthropology and Odontology, Milan, Italy, where attempts are being made to identify them. This paper analyzes the cranial morphometrics in relation to what is known of African cranial variation. It also addresses questions of population subdivision on the ship as well as secular changes that may be reflected in cranial morphology.Crania were digitized using the 3skull software, which also computes Howells measurements from the coordinates. Migrant crania were compared to African reference samples consisting of both 19th-century sub-Saharan West Africans and East Africans. Statistical procedures were discriminant and canonical variate analysis and Mahalanobis distances. K-means unsupervised clustering was also used.Results showed that the migrant samples differed from the 19th-century samples systemically; the differences consisted mainly of lower facial projections and higher cranial vaults and bases. Position on the ship, whether on the deck or below in the holds, showed subdivision. Holds had a higher proportion of West Africans, and the deck had a higher proportion of East Africans. K-means clustering also found groups contrasting between the deck and the holds. Comparing migrant cranial morphology to 19th-century Africans using variables that respond to secular change showed that migrants reflect changes that have occurred in Africa over the past 200 years.We conclude that morphometric analysis can provide useful information concerning the composition of unidentified victims of tragic events such as the 2015 shipwreck.

VARIABILITY OF SOMATOTYPES ACCORDING TO HEATH-CARTER IN ADULT CHUVASH PEOPLE

Introduction. In the range of topical problems of modern human morphology, the doctrine of bodily constitution occupies an important place, but there is a shortage of data on population variability of somatotypes in the literature. The purpose of this cross-sectional study was to study age and sex variations in body composition components using the Heath-Carter method in the Chuvash rural population examined in several villages in the Mariinsky-Posad, Morgaushsky and Yadrinsky districts of Chuvashia and Chuvash villages in the Aurgazinsky and Bizhbulyaksky districts of Bashkiria. The tasks included the determination and analysis of somatotypes according to Heath-Carter of men and women in age subgroups of adult Chuvash and assessment of age-related variability of sexual dimorphism of somatotype scores. Materials and methods. The study sample consists of 802 men aged 18–89 years (M = 46.9) and 738 women aged 18–90 years (M = 48.6) examined in 1994, 1999 and 2002. Age and sex differences were assessed using one-way analysis of variance. Mahalanobis distances were used to rate the sexual dimorphism of somatotype components. Results and discussion. Sex differences in the assessment of somatotype variations in the Chuvash population turn out to be most significant for the characteristics of endomorphy, with higher values in women. The values of endomorphy in men after 30 years practically did not change, but in women it continued to increase until the 6th decade, and then decreased with age. There were practically no differences in the mesomorphy; very small differences were found in ectomorphy between men and women aged 18-30 years. Mesomorphy increases from 18-30 years to 50 in both sexes; the ectomorphy shows opposite age trends. A leveling of sex differences in all body composition components was recorded in the older part of the combined sample (70 – 80 years old). Conclusions. Local patterns of age-related variability in body composition components for ethnic Chuvash are shown. For the first time, using representative material, indicators of sexual dimorphism have been determined for the complex of components of endomorphy, mesomorphy and ectomorphy in the Chuvash, showing the maximum intersexual differences in the age range of 41-60 years. A predominance of endomesomorphic body types in both sexes was revealed, with a tendency towards balanced mesomorphy in men (the youngest male subgroup is characterized by an ectomesomorphic somatotype).

Nutritional Diagnosis of Potato Crops Using the Multivariate Method

The compositional nutrient diagnosis (CND) method considers the multiple relationships among nutrients and has been proposed to evaluate the nutritional status of plants in place of the univariate and bivariate methods. As it is mathematically based and considers the interactions among all nutrients at the same time, it avoids the errors and trends observed in the calculations of other methods estimating nutritional status, enabling a greater relationship with productivity. The objective of this study was to obtain the CND norms for high-yielding populations of potato crops. For this, 587 samples were used from 21 experimental areas in the state of São Paulo, Brazil to correlate the leaf nutrient contents and the yields of potato crops. Crops with yields higher than 48,993.24 kg ha−1 were considered to have high yields, and the Mahalanobis distance separated the balanced samples from the nutritionally unbalanced ones. Thus, the CND-ilr method generated the norms and classified the 587 samples as nutritionally balanced with a high yield (5% of the total), nutritionally unbalanced with a low yield (92%), nutritionally unbalanced with a high yield (0.3%), or nutritionally balanced with a low yield (2.7%), with accuracy, sensitivity, specificity, NPV, and PPV scores of 96.9, 97.1, 93.6, 64.4, and 99.6%, respectively.

Diagnostics for categorical response models based on quantile residuals and distance measures

Polytomous categorical data are frequent in studies, that can be obtained with an individual or grouped structure. In both structures, the generalized logit model is commonly used to relate the covariates on the response variable. After fitting a model, one of the challenges is the definition of an appropriate residual and choosing diagnostic techniques. Since the polytomous variable is multivariate, raw, Pearson, or deviance residuals are vectors and their asymptotic distribution is generally unknown, which leads to difficulties in graphical visualization and interpretation. Therefore, the definition of appropriate residuals and the choice of the correct analysis in diagnostic tools is important, especially for nominal data, where a restriction of methods is observed. This paper proposes the use of randomized quantile residuals associated with individual and grouped nominal data, as well as Euclidean and Mahalanobis distance measures, as an alternative to reduce the dimension of the residuals. We developed simulation studies with both data structures associated. The half-normal plots with simulation envelopes were used to assess model performance. These studies demonstrated a good performance of the quantile residuals, and the distance measurements allowed a better interpretation of the graphical techniques. We illustrate the proposed procedures with two applications to real data.

Latent Variable Modeling of Social Networks with Directional Relations: An Exploration of Profile Similarity of Latent Factors

Statistical analysis of networks has gained increasing popularity in social and psychological sciences. This study introduces a latent variable model for self-reported directional relations within social networks, specifically focusing on predicting relationships based on actor attributes and latent traits. In a directional relation, the initiator is termed the “sender,” and the recipient is the “receiver.” Traditional symmetric similarity measures, such as Mahalanobis distance, often fall short of capturing the nuanced impact of sender and receiver latent traits in these scenarios. To overcome this limitation, we propose four distinct measures of trait similarity ( p sender , p receiver , m sender , and m receiver ), aiming to discern subtle distinctions between the impact of senders and receivers characteristics on the perceived social relations. We demonstrate the applicability and relevance of these measures in real-world network scenarios by applying our proposed framework to analyze a college friendship network. Among competing models, our findings affirm a potential non-linear trend between personality similarity and perceived friendship. Notably, the quadratic model with m receiver ( Q 8 ) outperformed others, exhibiting both significant linear and quadratic terms. Our results underscore the significance of the projected components of the receiver’s personality along with the sender’s orientation, with larger and smaller components both indicating a higher probability of perceived friendship.

ASOD: an adaptive stream outlier detection method using online strategy

In the current era of information technology, blockchain is widely used in various fields, and the monitoring of the security and status of the blockchain system is of great concern. Online anomaly detection for the real-time stream data plays vital role in monitoring strategy to find abnormal events and status of blockchain system. However, as the high requirements of real-time and online scenario, online anomaly detection faces many problems such as limited training data, distribution drift, and limited update frequency. In this paper, we propose an adaptive stream outlier detection method (ASOD) to overcome the limitations. It first designs a K-nearest neighbor Gaussian mixture model (KNN-GMM) and utilizes online learning strategy. So, it is suitable for online scenarios and does not rely on large training data. The K-nearest neighbor optimization limits the influence of new data locally rather than globally, thus improving the stability. Then, ASOD applies the mechanism of dynamic maintenance of Gaussian components and the strategy of dynamic context control to achieve self-adaptation to the distribution drift. And finally, ASOD adopts a dimensionless distance metric based on Mahalanobis distance and proposes an automatic threshold method to accomplish anomaly detection. In addition, the KNN-GMM provides the life cycle and the anomaly index for continuous tracking and analysis, which facilities the cause analysis and further interpretation and traceability. From the experimental results, it can be seen that ASOD achieves near-optimal F1 and recall on the NAB dataset with an improvement of 6% and 20.3% over the average, compared to baselines with sufficient training data. ASOD has the lowest F1 variance among the five best methods, indicating that it is effective and stable for online anomaly detection on stream data.

Data-driven cluster analysis method: a novel outliers detection method in multivariate data

Detection of multivariate outliers is crucial in statistical studies. On the other hand, the statistical applications without identifying possible outliers may present incorrect results. This study proposes a new technique for detecting multivariate outliers based on cluster analysis. The method considers information inherent in the data itself. We compare the methodology with three detection methods that are already widespread. The comparative investigation considers detection techniques based on the Mahalanobis distance. Sensitivity, specificity, and accuracy measures are used to assess the quality of the methods, as well as an analysis of the CPU time required to carry out the procedures. The new technique revealed a notorious superiority over others.

PLAN: Variance-Aware Private Mean Estimation

Differentially private mean estimation is an important building block in privacy-preserving algorithms for data analysis and machine learning. Though the trade-off between privacy and utility is well understood in the worst case, many datasets exhibit structure that could potentially be exploited to yield better algorithms. In this paper we present Private Limit Adopted Noise (PLAN), a family of differentially private algorithms for mean estimation in the setting where inputs are independently sampled from a distribution D over R^d, with coordinate-wise standard deviations σ in R^d. Similar to mean estimation under Mahalanobis distance, PLAN tailors the shape of the noise to the shape of the data, but unlike previous algorithms the privacy budget is spent non-uniformly over the coordinates. Under a concentration assumption on D, we show how to exploit skew in the vector σ, obtaining a (zero-concentrated) differentially private mean estimate with l_2 error proportional to ||σ||_1. Previous work has either not taken σ into account, or measured error in Mahalanobis distance --- in both cases resulting in l_2 error proportional to sqrt{d}||σ||_2, which can be up to a factor sqrt{d} larger. To verify the effectiveness of PLAN, we empirically evaluate accuracy on both synthetic and real-world data.

A method to simulate multivariate outliers with known mahalanobis distances for normal and non-normal data

Monte Carlo simulations and theoretical analyses have repeatedly demonstrated the impact of outliers on statistical analysis. Most simulation studies generate outliers using one of two general approaches: by multiplying an arbitrary point by a constant or through a finite mixture. The latter can be extended to multivariate settings by defining the Mahalanobis distance between the centroids of two clusters of points. Nevertheless, when researchers aim to simulate individual data points with population-level Mahalanobis distances, the number of available procedures is very limited. This article generalizes one of the few existing methods to simulate an arbitrary number of outliers in an arbitrary number of dimensions, for both multivariate normal and non-normal data. A small simulation demonstration showcases how this methodology enables new simulation designs that were either unpopular or not possible due to the lack of a data-generating algorithm. A discussion of potential implications highlights the importance of considering multivariate outliers in simulation settings.

Integrating wing morphometrics and mitochondrial DNA analysis to assess the filaria vector Mansonia uniformis (Diptera: Culicidae) populations in Thailand.

Mansonia uniformis (Diptera: Culicidae) is recognized as a vector of Brugia malayi and has been reported to transmit Wuchereria bancrofti, both causing lymphatic filariasis in humans. This study employed geometric morphometrics (GM) to investigate wing shape variation and analyzed genetic diversity through cytochrome c oxidase subunit 1 (COI) gene analyses in Ma. uniformis populations across Thailand. Wing GM analyses indicated significant differences in wing shape based on Mahalanobis distances among nearly all population pairs (p < 0.05), with no significant correlation between wing shape and geographic distance (r = 0.210, p > 0.05). Genetic analyses identified 63 haplotypes and 49 polymorphic sites, with the overall population exhibiting a nucleotide diversity of 0.006 (± 0.001) and a haplotype diversity of 0.912 (± 0.017). Deviations from neutrality, as indicated by Tajima's D and Fu's FS tests for the overall Ma. uniformis populations in Thailand, were statistically significant and negative, suggesting population expansion (both p < 0.05). Analysis of molecular variance revealed no significant genetic structure when all populations were categorized based on collection sites and geographic regions. However, significant differences in FST values were observed between some populations. These findings enhance our understanding of the geographical and genetic factors influencing Ma. uniformis populations, which are crucial for developing effective control strategies in Thailand.

Transcatheter Versus Surgical Aortic Valve Replacement in Recipients of Solid Organ Transplants and Liver Cirrhosis: A Propensity-Matched Analysis of National Readmission Data

Transcatheter aortic valve replacement (TAVR) continues to grow in the United States. There are limited data on recipients of solid organ transplant (SOT) and patients with liver cirrhosis who undergo aortic valve replacement (AVR). Our study aims to evaluate outcomes in these populations. Using the national readmission database (2016 to 2020), we identified recipients of SOT and patients with liver cirrhosis without previous liver transplants who were admitted for severe aortic stenosis and underwent either TAVR or surgical AVR (SAVR). We used multivariable regression for adjusted analysis and the propensity score matching model, implementing complete Mahalanobis distance matching within the Propensity Score Caliper (0.2) to match TAVR and SAVR cohorts for outcomes. Of 3,394 hospitalizations for AVR in recipients of SOT, 2,181 underwent TAVR, and 1,213 underwent SAVR. On propensity-matched analysis, SAVR was associated with more adverse events than was TAVR, including in-hospital mortality (5.2% vs 1.1%, adjusted odds ratio [aOR] 4.49, p <0.001), acute kidney injury (43.7% vs 10.2%, p <0.001), cardiogenic shock (9.0% vs 1.6%, p <0.001), sudden cardiac arrest (15.9 vs 6.0%, p <0.001), major adverse cardiac and cerebrovascular events (28% vs 10.4%, p <0.001), and net adverse events (72.8 vs 37.6%, p <0.001). A greater median length of stay (10 vs 2 days, p <0.001) and adjusted cost ($80,842 vs $57,014, p <0.001) were also observed. The readmission rates were the same for both cohorts after a 6-month follow-up. Similarly, in 14,763 hospitalizations for AVR in liver cirrhosis, 7,109 patients underwent TAVR, and 7,654 underwent SAVR. In propensity-matched cohorts (n = 2,341), SAVR was found to be associated with greater adverse events, including in-hospital mortality (19.8% vs 10%), stroke (6.7% vs 2%), acute kidney injury (67.7% vs 30.3%), cardiogenic shock (41.9% vs 19.9%), sudden cardiac arrest (31.8% vs 13.2%, aOR 2.89), major adverse cardiac and cerebrovascular events (66.2% vs 35.7%), and net adverse events (86% vs 59.5%) (p <0.001). A greater median length of stay (16 vs 3 days) and cost ($500,218 vs $263,383) were also observed (p <0.001). However, the rate of readmissions at 30-day (9% vs 11.1%) and 180-day intervals (33.4% vs 39.8%) was lower for the SAVR cohort (p <0.05). In recipients of SOT and patients with liver cirrhosis, SAVR is associated with greater short-term mortality, adverse events, and healthcare burden than is TAVR. TAVR is a relatively safer alternative to SAVR in these patient populations, although further studies are warranted to compare the long-term outcomes.