Failure Time Research Articles

Treatment Effect Heterogeneity in Acute Kidney Injury Incidence Following Intravenous Antihypertensive Administration for Severe Blood Pressure Elevation During Hospitalization

Severe hypertension (sHTN) that develops after hospital admission is prevalent in 10% of patients admitted for reasons other than HTN. sHTN is commonly treated with intravenous (IV) antihypertensives and is associated with a greater risk of acute kidney injury (AKI). Our goal was to explore whether there is heterogeneity in IV antihypertensives' effect on AKI incidence among patients who develop sHTN during hospitalization. Heterogeneity of treatment effect analysis. Patients who developed sHTN, systolic blood pressure (BP) >180, or diastolic BP >110 mmHg during hospitalization and did not have kidney failure. Treatment with IV antihypertensives within 3 hours of BP elevation. Time to develop AKI. An accelerated failure time Bayesian Additive Regression Trees (BART) model to capture the association between the time to develop AKI and predictors. Individual treatment effects estimated for each participant using a counterfactual outcome framework and these estimates were used to identify patient characteristics associated with treatment effect heterogeneity in response to IV antihypertensives. We included 11,951 patients who developed sHTN, 741 were treated with IV antihypertensives, and 11,210 were not, of which 18% and 13% developed AKI, respectively. Most patients would have been harmed from IV antihypertensive treatment except for a small subset of 317 patients who were White, had an SBP on admission ≥156 mmHg, an eGFR ≥70.7 ml/min/1.73m2, and a serum bicarbonate <21.7 mmol/L. Data-driven, hypothesis-generating approach. Findings were not validated with external data sources. These exploratory findings suggest that most patients who develop sHTN will not benefit from IV antihypertensive treatment. Future studies should assess for heterogeneity when identifying treatment options, if any are needed, for sHTN.

Predicting long-term survival among patients with HCC.

Prognosticating survival among patients with HCC and cirrhosis must account for both the tumor burden/stage, as well as the severity of the underlying liver disease. Although there are many staging systems used to guide therapy, they have not been widely adopted to predict patient-level survival after the diagnosis of HCC. We sought to develop a score to predict long-term survival among patients with early- to intermediate-stage HCC using purely objective criteria. Retrospective cohort study among patients with HCC confined to the liver, without major medical comorbidities within the Veterans Health Administration from 2014 to 2023. Tumor data were manually abstracted and combined with clinical and laboratory data to predict 5-year survival from HCC diagnosis using accelerated failure time models. The data were randomly split using a 75:25 ratio for training and validation. Model discrimination and calibration were assessed and compared to other HCC staging systems. The cohort included 1325 patients with confirmed HCC. A risk score using baseline clinical, laboratory, and HCC-related survival had excellent discrimination (integrated AUC: 0.71 in the validation set) and calibration (based on calibration plots and Brier scores). Models had superior performance to the BCLC and ALBI scores and similar performance to the combined BCLC-ALBI score. We developed a risk score using purely objective data to accurately predict long-term survival for patients with HCC. This score, if validated, can be used to prognosticate survival for patients with HCC, and, in the setting of liver transplantation, can be incorporated to consider the net survival benefit of liver transplantation versus other curative options.

Penalized Likelihood Estimation for Current Status Data with Informative Censoring

Current status data occurs when failure time of subjects in a survival study is only known to be either less or greater than the censoring time. Thus, the failure time is either left – or right – censored. Analyzing data of this structure under the Cox Proportional Hazards model with dependent censoring assumption can be challenging. To address this, a Penalized Maximum Likelihood Estimation (PMLE) approach was proposed. The unknown baseline cumulative hazard functions for both the failure time and the censoring time were estimated using splines. The advantage of penalized approach over unpenalized method is that that the desired smoothness level of the functions are controlled by their respective penalty terms. The possible dependence between the failure and censoring times was accounted for using the gamma-frailty model. An easy to implement hybrid computational algorithm is proposed to estimate the PMLEs and the Bayesian technique was employed for the estimation of the variances of the parameters. Extensive simulation studies were conducted to assess the statistical properties of the PMLEs. It was observed that the realized estimators were not only consistent, asymptotically normal and efficient, but also, were robust to the number of knots chosen, the proportion of dependent censoring used and the frailty distribution employed. The proposed PMLE method was further applied to real data obtained from tumorigenicity experiment.

Selection processes, transportability, and failure time analysis in life history studies.

In life history analysis of data from cohort studies, it is important to address the process by which participants are identified and selected. Many health studies select or enrol individuals based on whether they have experienced certain health related events, for example, disease diagnosis or some complication from disease. Standard methods of analysis rely on assumptions concerning the independence of selection and a person's prospective life history process, given their prior history. Violations of such assumptions are common, however, and can bias estimation of process features. This has implications for the internal and external validity of cohort studies, and for the transportabilty of results to a population. In this paper, we study failure time analysis by proposing a joint model for the cohort selection process and the failure process of interest. This allows us to address both independence assumptions and the transportability of study results. It is shown that transportability cannot be guaranteed in the absence of auxiliary information on the population. Conditions that produce dependent selection and types of auxiliary data are discussed and illustrated in numerical studies. The proposed framework is applied to a study of the risk of psoriatic arthritis in persons with psoriasis.

Relative burden of cancer and noncancer mortality among long-term survivors of differentiated thyroid cancer in the US.

Limited information is available regarding the relative risks of cancer-specific mortality and noncancer-specific mortality among long-term survivors with differentiated thyroid cancer (DTC). In this retrospective study, nationwide data from the Surveillance, Epidemiology, and End Results database (1992-2020) were utilized. The Accelerated Failure Time Model was applied to calculate Survival Time Ratios (TR), with the primary focus on mortality resulting from DTC. The competing risks model was employed to investigate the relative risks of various outcomes in DTC patients with a survival duration of 5 years or more. In our cohort, 279 patients succumbed to DTC, while 748 died from other diseases. Notably, in DTC cohorts, noncancer-specific mortality rates were consistently higher than DTC-specific mortality rates across different age groups and genders. The risk of DTC and noncancer-specific mortality varied based on the TNM stage. With more advanced disease stages, the risk of DTC and other cancer-specific mortality gradually increased. The cumulative mortality from other cancer-specific causes was consistently the lowest. In long-term surviving DTC patients, noncancer-specific mortality outweighed DTC-specific mortality irrespective of age and gender. For stage I and II patients, increased attention should be directed toward noncancer-specific mortality in postoperative follow-ups. Conversely, for stage III and IV patients, greater consideration should be given to DTC-related causes of death. In addition, for stage III and IV DTC patients, the risk of death from other cancers was significantly higher than for stages I and II.

Performance of photovoltaic panels with different inclinations under uniform thermal loading

Integrating photovoltaic (PV) panels with different tilt angles in building envelopes or roofs is widely employed for environmental sustainability. However, little is known about the influence of different tilt angles on the thermal failure of the photovoltaic façades or roofs in fire conditions. A total of 15 four-edge shielded PV panels (300 × 300 × 4.7 mm3), with five different inclinations of 0°, 15°, 30°, 45° and 60°, were heated to fail using a uniform radiant panel. Measurements were taken to track glass thermal breakage, surface temperatures, incident heat flux and failure characteristics. The glass fracture and pyrolysis of the internal thermoplastic materials were observed under thermal radiation. The average breakage time of glass in PV panels showed an increasing trend with increasing inclination of the PV panels. Moreover, when the PV panels were tilted beyond 30°, the time to failure increased more significantly. The maximum temperature difference and heat flux that the PV panels can withstand were primarily measured within the range of 61–84 °C and 8–15 kW/m2, respectively. Finally, the test results were simulated by a finite element method (FEM) model, calculating the heat transfer and thermal stress of PV panels: the average errors concerning temperature distributions and failure times were smaller than 15 % compared with the experimental results.

Failure signal of high orientation Poly(L-lactic acid) monofilament for self-expanding stent during lifetime: Disappeared amorphous connections

The premature failure of bioresorbable self-expanding stents for peripheral arterial disease could lead to form thrombosis and threaten patients′ lives. Predicting the time and cause of mechanical and structural failure is crucial to prevent premature failure of implanted stents. Herein, we report a situ accelerated hydrolysis to investigate the degradation properties of self-expanding stents braided by high-performance Poly(L-lactic acid) (PLLA) monofilaments in an accelerated term over a formal long one. Degradation, microstructure, and mechanical properties were comprehensively evaluated to identify key factors of the failure during lifetime. The results show that the upper temperature limit for accelerated hydrolysis is 50 °C. The mechanical properties undergo multiple stages of functional holding, decreasing, and eventual failure. It is attributed to uneven degradation and distribution of crystalline and amorphous regions. Specifically, the disappearance of amorphous chains between fibrils at 6 months leads to mechanical failure. Further disappearance of amorphous chains within fibrils at 18 months results in structural disintegration. Additionally, the degradation behavior of the stents and potential effects of degradation products on vascular tissues are validated for predictive accuracy. These insights provide valuable experimental references for optimizing design and clinical application of PLLA-based self-expanding stents.

Electrochemical Migration Study on Sn-58Bi Lead-Free Solder Alloy Under Dust Contamination.

With the development of electronic packaging technology toward miniaturization, integration, and high reliability, the diameter and pitch of solder joints continue to shrink. Adjacent solder joints are highly susceptible to electrochemical migration (ECM) due to the synergistic effects of high-density electric fields, water vapor, and contaminants. Dust has become one of the non-negligible causal factors in ECM studies due to air pollution. In this study, 0.2 mM/L NaCl and Na2SO4 solutions were used to simulate soluble salt in dust, and the failure mechanism of an Sn-58Bi solder ECM in the soluble salt in dust was analyzed by a water-droplet experimental method. It was shown that the mean failure time of the ECM of an Sn-58Bi solder in an NaCl solution (53 s) was longer than that in an Na2SO4 solution (32 s) due to the difference in the anodic dissolution characteristics in the two soluble salt solutions. XPS analysis revealed that the dendrites produced by the ECM process were mainly composed of Sn, SnO, and SnO2, and there were precipitation products-Sn(OH)2 and Na2SO4-attached to the dendrites. The corrosion potential in the NaCl solution (-0.351 V) was higher than that in the Na2SO4 solution (-0.360 V), as shown by a polarization test, indicating that the Sn-58Bi solder had better corrosion resistance in the NaCl solution. Therefore, an Sn-58Bi solder has better resistance to electrochemical migration in an NaCl solution compared to an Na2SO4 solution.

Joint regression analysis of clustered current status data with latent variables.

Clustered current status data frequently occur in many fields of survival studies. Some potential factors related to the hazards of interest cannot be directly observed but are characterized through multiple correlated observable surrogates. In this article, we propose a joint modeling method for regression analysis of clustered current status data with latent variables and potentially informative cluster sizes. The proposed models consist of a factor analysis model to characterize latent variables through their multiple surrogates and an additive hazards frailty model to investigate covariate effects on the failure time and incorporate intra-cluster correlations. We develop an estimation procedure that combines the expectation-maximization algorithm and the weighted estimating equations. The consistency and asymptotic normality of the proposed estimators are established. The finite-sample performance of the proposed method is assessed via a series of simulation studies. This procedure is applied to analyze clustered current status data from the National Toxicology Program on a tumorigenicity study given by the United States Department of Health and Human Services.

Likelihood-Based Inference of Log-Logistic Accelerated Hazards Regression Models for Cross Hazards Data

ABSTRACT The proportional hazards model and accelerated failure time model are two important classes for analyzing survival data. However, the two models cannot capture time-scaled effects such as crossing hazard or the gradual effect of treatment over time. For the purpose of this detection, accelerated hazards model has been studied. In this paper, we propose the use of log-logistic distribution for the baseline hazard function of the accelerated hazards model which allows for time-dependent group effect. In particular, the log-logistic distribution gives various forms (e.g. non-monotone) of hazard function and also an easy implementation for likelihood-based model fitting due to an explicit form of hazard and survival functions. The likelihood-based estimation procedures of the accelerated hazards models are derived. Our method is demonstrated with two practical data sets, via the procedures of systematic model checking.

Core Concepts in Pharmacoepidemiology: Time-To-Event Analysis Approaches in Pharmacoepidemiology.

This article provides an overview of time-to-event (TTE) analysis in pharmacoepidemiology. The key concept of censoring is reviewed, including right-, left-, interval- and informative censoring. Simple descriptive statistics are explained, including the nonparametric estimation of the TTE distribution as per Kaplan-Meier method, as well as more complex TTE regression approaches, including the parametric Accelerated Failure Time (AFT) model and the semi-parametric Cox Proportional Hazards and Restricted Mean Survival Time (RMST) models. Additional approaches and various TTE model extensions are presented as well. Finally, causal inference for TTE outcomes is addressed. A thorough review of the available concepts and methods outlines the immense variety of available and useful TTE models. There may be underused TTE concepts and methods, which are highlighted to raise awareness for researchers who aim to apply the most appropriate TTE approach for their study. This paper constitutes a modern summary of TTE analysis concepts and methods. A curated list of references is provided.

A semiparametric accelerated failure time-based mixture cure tree

The mixture cure rate model (MCM) is the most widely used model for the analysis of survival data with a cured subgroup. In this context, the most common strategy to model the cure probability is to assume a generalized linear model with a known link function, such as the logit link function. However, the logit model can only capture simple effects of covariates on the cure probability. In this article, we propose a new MCM where the cure probability is modeled using a decision tree-based classifier and the survival distribution of the uncured is modeled using an accelerated failure time structure. To estimate the model parameters, we develop an expectation maximization algorithm. Our simulation study shows that the proposed model performs better in capturing nonlinear classification boundaries when compared to the logit-based MCM and the spline-based MCM. This results in more accurate and precise estimates of the cured probabilities, which in-turn results in improved predictive accuracy of cure. We further show that capturing nonlinear classification boundary also improves the estimation results corresponding to the survival distribution of the uncured subjects. Finally, we apply our proposed model and the EM algorithm to analyze an existing bone marrow transplant data.

Emergent resistance-associated mutations at first- or second-line HIV-1 virologic failure with second-generation InSTIs in two- and three-drug regimens: the Virostar-1 study.

Second-generation integrase strand transfer inhibitors (InSTIs) have a high barrier to resistance and potent antiretroviral activity. They are recommended as first- or second-line (FL and SL) options in two- and three-drug regimens (2DR and 3DR) in international treatment guidelines. However, there are limited real-world data on emerging resistance at the time of virological failure (VF) with these regimens. The Virostar-1 study objective is to analyse the emergence of resistance-associated mutations (RAMs) over 3 years with DTG-based 2DRs and DTG- or bictegravir (BIC)-based 3DRs in people living with HIV (PLWH) experiencing a VF (FL or SL). Retrospective analysis of genotypic resistance detected at the time of a FL or SL VF with BIC/FTC/TAF, DTG/ABC/3TC, DTG/3TC and DTG/RPV between 2019 and 2022 was conducted from a French multicentre database. VF was defined as two consecutive HIV-1 plasma viral loads > 50 c/mL. Sanger assays were performed at VF within standard clinical care. Resistance mutations were reported using the ANRS algorithm. Selection biases prevent group comparisons. During the period, N = 5986 PLWH were followed either in FL or SL. The VF rate was overall low: BIC/FTC/TAF, 6.8%; DTG/ABC/3TC, 7.5%; DTG/3TC, 5.1%; and DTG/RPV, 2.1%. Some emergent InSTI or NRTI RAMs were detected with BIC/FTC/TAF 4%, DTG/ABC/3TC 8.5%, DTG/3TC 18% and 39% emergent NNRTI RAMs with DTG/RPV. However, a complete absence of dual resistance against NRTIs and InSTIs was observed. We detected rare emergent InSTI RAMs and few emergent NRTI RAMs in PLWH failing DTG- or BIC-based regimens in FL or SL. The observed rates of emergent RAMs at VF were 4% with BIC/FTC/TAF, 8.5% with DTG/ABC/3TC, 18% with DTG/3TC and 39% with DTG/RPV.

Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experiments

Various machine learning (ML) and deep learning (DL) techniques have been recently applied to the forecasting of laboratory earthquakes from friction experiments. The magnitude and timing of shear failures in stick-slip cycles are predicted using features extracted from the recorded ultrasonic or acoustic emission (AE) signals. In addition, the Rate and State Friction (RSF) constitutive laws are extensively used to model the frictional behavior of faults. In this work, we use data from shear experiments coupled with passive acoustic (variance, kurtosis, and AE rate) interleaved with active source ultrasonic monitoring (transmitted wave amplitude) to develop physics-informed neural network (PINN) models incorporating the RSF law and AE rate generation equation with wave amplitude serving as a proxy for friction state variable. This PINN framework allows learning RSF parameters from stick-slip experiments rather than measuring them through a series of velocity step experiments. We observe that when the stick-slip cycles are irregular, the PINN models outperform the data-driven DL models. Transfer learning (TL) PINN models are also developed by pre-training on data collected at one normal stress level followed by forecasting shear failures and retrieving RSF parameters at other stress levels (i.e., with different recurrence intervals) after retraining on a limited amount of new data. Our findings suggest that TL models perform better compared to standalone models. Both standalone and TL PINN-estimated RSF parameters and their ground truth values show excellent agreements thus demonstrating that RSF parameters can be retrieved from laboratory stick-slip experiments using the corresponding acoustic data and that the transmitted wave amplitude provides a good representation of the evolving frictional state during stick-slips.

Association between periodontitis with the all-cause and cause specific mortality among the population with hyperlipidemia

BackgroundTo explore the association between periodontitis and all-cause as well as cause-specific mortality rates in U.S. adults with hyperlipidemia.MethodsParticipants were extracted from NHANES during 1988–1994, 1999–2004 and 2009–2014 periods. To assess the association between moderate-to-severe periodontitis and mortality rates for both all-cause and cause-specific mortality, hazard ratios (HRs), time ratios (TRs), and their respective 95% confidence intervals (CIs) were calculated using Cox proportional hazards and Weibull accelerated failure time (AFT) models.ResultsOver a median follow-up duration of 11.83 years, 4,623 deaths of 16,848 participants were recorded. Multivariate Cox regression and AFT analyses showed moderate-to-severe periodontitis were associated with an increased risk of all-cause (HR: 1.31, 95% CI: 1.20–1.44, P < 0.001; TR: 0.85, 95% CI: 0.80–0.90, P < 0.001), cardiovascular disease (CVD)-related (HR: 1.36, 95% CI: 1.14–1.63, P = 0.001; TR: 0.83, 95% CI: 0.75–0.92, P < 0.001) and cancer-related mortality ( HR: 1.35, 95% CI: 1.12–1.63, P = 0.002; TR: 0.82, 95% CI: 0.72–0.93, P = 0.002). Meanwhile, there was a significant upward trend in the risk of mortality with increasing severity of periodontitis (P for trend < 0.001).ConclusionsOur study highlights the moderate-to-severe periodontitis is associated with an increased risk of all-cause, CVD-related and cancer-related mortality among U.S. adults with hyperlipidemia. And the mortality risk increasing alongside the severity of periodontitis.

Can satellite InSAR innovate the way of large landslide early warning?

Predicting landslide failure times is an essential component in landslide risk management. Although in-situ sensor-supported landslide early warning systems are still predominantly used, their high cost makes it impractical to monitor all the landslides, thereby posing a major challenge for the effective landslide risk management. Hence, this study investigated this problem from an earth observation perspective and proposed a probabilistic landslide failure time prediction framework integrating Interferometric Synthetic Aperture Radar (InSAR) monitoring information. Accordingly, 30 historical landslides that occurred between 2016 and 2021 in central and western China were collected to evaluate the feasibility of the aforementioned framework. Based on the landslide dataset, the performance of the satellite InSAR technology for landslide failure time prediction is evaluated systematically from an application perspective. It was evident that eleven landslides (36.67 %) were captured by InSAR with accelerated deformation signals before failure, and monitoring data from eight (26.67 %) of them provided enough information for their failure time prediction. Further, a probabilistic method integrating the conventional inverse velocity model and sequential Bayesian updating was proposed to dynamically predict the most likely failure time and related confidence interval. Case studies showed that the proposed method could successfully predict the failure time of the eight landslides, thus demonstrating the feasibility of the framework. Although the current long revisit period of satellites constrains their performance practically, this problem can be solved by advancements in future satellite missions. Thus, we believe that the InSAR era is imminent and will bring substantial values for large landslide early warning.

Comparative Study of Cox Proportional Hazard and Accelerated Failure Time Models on Survival of Diabetes Mellitus and Hypertension Patients

Comparative analysis of survival models for hypertension and diabetics carried out in this study. The study compared the performance of Cox Proportional Hazard (CPH) and Accelerated Failure Time (AFT) models with and without random effects in analyzing hypertension and diabetes survival data obtained from General Hospital Keffi and General Hospital Nasarawa for the period of five years (2018-2022). Models evaluated include CPH, AFT (Exponential, Weibull, Log-Logistic) and their corresponding random effect counterparts. The Weibull AFT random effect model outperformed all other models in terms of goodness-of-fit, predictive accuracy and interpretability. This model accounted for individual heterogeneity and clustering effects, providing a more nuanced understanding of risk factors’ impact on survival rates. The Weibull AFT random effects model is the most suitable choice for analyzing hypertension and diabetes survival data, offering improved predictive power and insight into the complex relationships between risk factors and time-to-event outcomes. These findings have significant implications for healthcare professionals, policymakers, and researchers seeking to optimize survival analysis methodologies.

Evaluation of the Antecedent Saturation and Rainfall Conditions on the Slope Failure Mechanism Triggered by Rainfalls

The stability analysis of rainfall-induced slope failures considers a number of factors including the characteristics of the rainfall, vegetation, geometry of the slope, unsaturated soil characteristics, infiltration capacity, and saturation degree variations. Amongst all these factors, this study aims to investigate the effects of the antecedent rainfall and saturation conditions. A numerical modeling study was conducted using finite difference code software on a representative slope geometry with two different soil types. Two scenarios were followed: The first involved the application of three different rainfall intensities for varying initial saturation levels between 40% and 60%, representing the antecedent saturation conditions. The second scenario involved modeling successive rainfalls for a typical initial saturation degree of 50%. The impact of antecedent rainfall was assessed by determining the time required for failure during the application of a main extreme rainfall after a preceding rainfall of varying durations. Consequently, a zone of susceptible time for failure was suggested for use as a criterion in hazard management, allowing for the tracking of rainfall and its duration through the proposed chart for potential failures. Once the anticipated critical rainfall intensities have been determined through a meteorological analysis, a risk assessment for a specific slope can be conducted using the proposed practical procedure. Accordingly, a control mechanism may be established to detect the potential for a natural hazard. Furthermore, the proposed procedure was applied to a case study, whose modeling insights were in harmony with the real conditions of the slope failure. Thus, this demonstrated the significance of the antecedent conditions in modeling landslides triggered by rainfalls.

Deep learning-based anomaly detection using one-dimensional convolutional neural networks (1D CNN) in machine centers (MCT) and computer numerical control (CNC) machines.

Computer numerical control (CNC) and machine center (MCT) machines are mechanical devices that manipulate different tools using computer programming as inputs. Predicting failures in CNC and MCT machines before their actual failure time is crucial to reduce maintenance costs and increase productivity. This study is centered around a novel deep learning-based model using a 1D convolutional neural network (CNN) for early fault detection in MCT machines. We collected sensor-based data from CNC/MCT machines and applied various preprocessing techniques to prepare the dataset. Our experimental results demonstrate that the 1D-CNN model achieves a higher accuracy of 91.57% compared to traditional machine learning classifiers and other deep learning models, including Random Forest (RF) at 89.71%, multi-layer perceptron (MLP) at 87.45%, XGBoost at 89.67%, logistic regression (LR) at 75.93%, support vector machine (SVM) at 75.96%, K-nearest neighbors (KNN) at 82.93%, decision tree at 88.36%, naïve Bayes at 68.31%, long short-term memory (LSTM) at 90.80%, and a hybrid 1D CNN + LSTM model at 88.51%. Moreover, our proposed 1D CNN model outperformed all other mentioned models in precision, recall, and F-1 scores, with 91.87%, 91.57%, and 91.63%, respectively. These findings highlight the efficacy of the 1D CNN model in providing optimal performance with an MCT machine's dataset, making it particularly suitable for small manufacturing companies seeking to automate early fault detection and classification in CNC and MCT machines. This approach enhances productivity and aids in proactive maintenance and safety measures, demonstrating its potential to revolutionize the manufacturing industry.

Upper- vs Lower-Extremity Secondary Access During Transcatheter Aortic Valve Implantation

An upper-extremity approach for secondary access during transfemoral transcatheter aortic valve implantation (TAVI) may reduce clinically relevant secondary access site-related bleeding. To investigate the safety and efficacy of an upper-extremity approach compared with a lower-extremity approach in patients undergoing TAVI. The TAVI XS trial was a randomized clinical trial performed between November 28, 2022, and November 15, 2023, with a 30-day follow-up, in 4 TAVI centers in the Netherlands. Eligibility was determined first, and only those patients with severe aortic stenosis and no contraindication for upper- or lower-extremity secondary access were informed about the study and asked to participate. Participants were randomized 1:1 between the upper-extremity approach (radial artery diagnostic access and upper-arm vein for temporary pacing lead placement) and lower-extremity approach (femoral artery diagnostic access and femoral vein for temporary pacing lead placement) for secondary access during TAVI. Primary end point was clinically relevant bleeding (Bleeding Academic Research Consortium type 2, 3, or 5) of the randomized secondary access. Secondary end points included any clinically relevant bleeding, time to mobilization, duration of hospitalization, secondary access failure, and procedural time. Of a total of 324 eligible patients, 238 patients undergoing transfemoral TAVI (mean [SD] age, 79.4 [6.5] years; 150 male [63.0%]; median European System for Cardiac Operative Risk Evaluation II score, 2.2% [IQR, 1.5%-3.5%]) were included. The primary end point occurred in 5 of 119 patients (4.2%) in the upper-extremity group and 16 of 119 (13.4%) in the lower-extremity group (odds ratio [OR], 0.28 [95% CI, 0.10-0.80]; P = .01). Incidence of any clinically relevant bleeding was decreased in the upper-extremity group (25 of 119 [21.0%] vs 41 of 119 [34.5%] patients; OR, 0.51 [95% CI, 0.28-0.91]; P = .02). There was no difference in time to mobilization or duration of hospitalization. Secondary access failure (14 of 119 [11.8%] vs 1 of 119 [0.8%] patients; OR, 15.73 [95% CI, 2.03-121.69]; P = .001) and procedural time (60.0 [IQR, 39.0-88.0; 95% CI, 53.0-70.0] vs 48.0 [IQR, 34.0-64.0; 95% CI, 40.0-55.0] minutes; P = .002) were higher in the upper-extremity cohort. In this randomized clinical trial of patients undergoing transfemoral TAVI, the upper-extremity approach for secondary access was associated with less clinically relevant access site-related bleeding compared with the conventional lower-extremity approach and should be considered to reduce periprocedural bleeding complications. ClinicalTrials.gov Identifier: NCT05672823.