Baseline Hazard Research Articles

Abstract PO1-09-10: County-level Social Vulnerability and Survival among Women with Inflammatory Breast Cancer

Abstract Purpose: Inflammatory breast cancer (IBC) is an aggressive form of breast cancer with a poor overall prognosis compared to other forms of breast cancer. Advances in treatment have contributed to improvement in IBC survival rates in recent years, yet disparities in outcomes persist. It is unclear whether social determinants of health impact IBC prognosis independent of access to quality cancer care. Thus, the aim of this study was to examine whether the US county-level social vulnerability index (SVI) was associated with IBC stage at diagnosis and overall survival (OS) among women with IBC treated at a single institution. Methods: Patients enrolled in the IBC registry at Dana-Farber Cancer Institute (DFCI) were included in this study. We linked participant’s residential zip codes to the 2020 CDC’s county-level SVI, which is calculated from 15 social determinants of health attributes from the American Community Survey including socioeconomic status, household composition, language and minority status, and housing and transportation factors. The SVI value reflects the percentile of counties in the nation that are less vulnerable than the county of interest. We assessed associations between SVI and stage at diagnosis using t-tests. Kaplan-Meier curves and log-rank tests were used to assess survival across SVI quartiles based on distribution (1st [least vulnerable] and 4th [most vulnerable]). Using hierarchical logistic regression models and Cox proportional hazards models with mixed effects incorporating cluster-specific random effects that modify the baseline hazard function, we assessed whether SVI is associated with the presence of de novo metastatic disease and OS in IBC. Results: A total of 586 women (86% White) enrolled in the IBC registry at DFCI between 1986 and 2021 were included in this study. Of these, 69.1% had stage III and 30.9% had de novo metastatic disease. 280 deaths occurred in the study population over a mean follow-up of 4.1 years. SVI for the counties where study participants resided ranged from 2.6% to 95.8% (median=34.2%). The mean SVI did not significantly differ according to stage at diagnosis (41.6% in non-metastatic and 40.8% in metastatic at presentation; p=0.70). OS did not significantly differ across SVI quartiles (p-value=0.63). In the age-adjusted models, the SVI was not associated with the presence of de novo metastatic disease (odds ratio (OR (95% CI)=0.99 (0.84, 1.16)) or OS (HR (95% CI)=0.79 (0.46, 1.33). Conclusions: Our findings that residing in socially vulnerable US counties is not associated with the presence of de novo metastatic disease or IBC survival among a subset of women accessing high-quality cancer care suggest that improving access to quality cancer care may overcome underlying socioeconomic factors that contribute to disparities in IBC outcomes. Future studies are needed to confirm these findings in other settings. Citation Format: Kelly Hirko, Elizabeth Troll, Sean Ryan, Faina Nakhlis, Jennifer Bellon, Olga Kantor, Christina Minami, Eren Yeh, Ilana Schlam, Laura Warren, Caroline Block, Susan Schumer, Filipa Lynce. County-level Social Vulnerability and Survival among Women with Inflammatory Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-09-10.

Semiparametric analysis of nonparametric proportional hazards models with mixed dependent censored data

To construct a nonparametric proportional hazards (PH) model for mixed informative interval-censored failure time data for predicting the risks in heart transplantation surgeries. Based on the complexity of mixed informative interval-censored failure time data, we considered the interdependent relationship between failure time process and observation time process, constructed a nonparametric proportional hazards (PH) model to describe the nonlinear relationship between the risk factors and heart transplant surgery risks and proposed a two-step sieve estimation maximum likelihood algorithm. An estimation equation was established to estimate frailty variables using the observation process model. Ⅰ-spline and B-spline were used to approximate the unknown baseline hazard function and nonparametric function, respectively, to obtain the working likelihood function in the sieve space. The partial derivative of the model parameters was used to obtain the scoring equation. The maximum likelihood estimation of the parameters was obtained by solving the scoring equation, and a function curve of the impact of risk factors on the risk of heart transplantation surgery was drawn. Simulation experiment suggested that the estimated values obtained by the proposed method were consistent and asymptotically effective under various settings with good fitting effects. Analysis of heart transplant surgery data showed that the donor's age had a positive linear relationship with the surgical risk. The impact of the recipient's age at disease onset increased at first and then stabilized, but increased against at an older age. The donor-recipient age difference had a positive linear relationship with the surgical risk of heart transplantation. The nonparametric PH model established in this study can be used for predicting the risks in heart transplantation surgery and exploring the functional relationship between the surgery risks and the risk factors.

Invoice level forecasting with discrete survival methods for effective forecasting of account receivables in supply chain

The objective of this study is to obtain accurate and timely Account Receivables forecasts that can feed into the Cash Flow Forecasting (CFF) for a generic wholesales scenario in a supply chain company. The main components of the CFF are the Account Receivables (AR), Account Payables (AP) and Working Capital (WC). The focus of this work is on AR, in particular on how they contribute to the overall cash flow forecasting. The prediction of AR is based on the predicted payment date of each invoice, which can be obtained from the time to payment given the issue date. In this context, we propose the use of discrete survival methods for predicting the time to payment of issued invoices. To obtain more accurate predictions for time to payment, we fit a generalized linear mixed effects model with a time-varying baseline hazard function. This approach provides greater precision for subsequent account receivables predictions compared to the prediction based on simply the payment terms of each invoice, standard time series and classification models, and continuous-time survival methods. We demonstrate the approach on two financial data sets.

Cox model inference for relative hazard and pure risk from stratified weight-calibrated case-cohort data

The case-cohort design obtains complete covariate data only on cases and on a random sample (the subcohort) of the entire cohort. Subsequent publications described the use of stratification and weight calibration to increase efficiency of estimates of Cox model log-relative hazards, and there has been some work estimating pure risk. Yet there are few examples of these options in the medical literature, and we could not find programs currently online to analyze these various options. We therefore present a unified approach and R software to facilitate such analyses. We used influence functions adapted to the various design and analysis options together with variance calculations that take the two-phase sampling into account. This work clarifies when the widely used “robust” variance estimate of Barlow (Biometrics 50:1064–1072, 1994) is appropriate. The corresponding R software, CaseCohortCoxSurvival, facilitates analysis with and without stratification and/or weight calibration, for subcohort sampling with or without replacement. We also allow for phase-two data to be missing at random for stratified designs. We provide inference not only for log-relative hazards in the Cox model, but also for cumulative baseline hazards and covariate-specific pure risks. We hope these calculations and software will promote wider use of more efficient and principled design and analysis options for case-cohort studies.

Transfer learning under the Cox model with interval‐censored data

AbstractTransfer learning, focusing on information borrowing to address limited sample size issues, has gained increasing attention in recent years. Our method aims to utilize data from other population groups as a complement to enhance risk factor discernment and failure time prediction among underrepresented subgroups. However, a literature gap exists in effective knowledge transfer from the source to the target for risk assessment with interval‐censored data while accommodating population incomparability and privacy constraints. Our objective is to bridge this gap by developing a transfer learning approach under the Cox proportional hazards model. We introduce the tuning‐free Trans‐Cox‐MIC algorithm, enabling adaptable information sharing in regression coefficients and baseline hazards, while ensuring computational efficiency. Our approach accommodates covariate distribution shifts, coefficient variations, and baseline hazard discrepancies. Extensive simulations showcase the method's accuracy, robustness, and efficiency. Application to the prostate cancer screening data demonstrates enhanced risk estimation precision and predictive performance in the African American population.

Multi-hazard assessment of climate-related hazards for European coastal cities

The assessment of risk posed by climate change in coastal cities encompasses multiple climate-related hazards. Sea-level rise, coastal flooding and coastal erosion are important hazards, but they are not the only ones. The varying availability and quality of data across cities hinders the ability to conduct holistic and standardized multi-hazard assessments. Indeed, there are far fewer studies on multiple hazards than on single hazards. Also, the comparability of existing methodologies becomes challenging, making it difficult to establish a cohesive understanding of the overall vulnerability and resilience of coastal cities. The use of indicators allows for a standardized and systematic evaluation of baseline hazards across different cities. The methodology developed in this work establishes a framework to assess a wide variety of climate-related hazards across diverse coastal cities, including sea-level rise, coastal flooding, coastal erosion, heavy rainfall, land flooding, droughts, extreme temperatures, heatwaves, cold spells, strong winds and landslides. Indicators are produced and results are compared and mapped for ten European coastal cities. The indicators are meticulously designed to be applicable across different geographical contexts in Europe. In this manner, the proposed approach allows interventions to be prioritized based on the severity and urgency of the specific risks faced by each city.

Changes in Global Nutrition Practices in Critically Ill Children and the Influence of Emerging Evidence: A Secondary Analysis of the Pediatric International Nutrition Studies, 2009-2018

BackgroundThe timeline of the 3 Pediatric International Nutrition Studies (PINS) coincided with the publication of 2 major guidelines for the timing of parenteral nutrition (PN) and recommended energy and protein delivery dose. ObjectiveThe study's main objective was to describe changes in the nutrition delivery practice recorded in PINS1 and PINS2 (PINS1-2) (conducted in 2009 and 2011, preexposure epoch) vs PINS3 (conducted in 2018, postexposure epoch), in relation to the published practice guidelines. DesignThis study is a secondary analysis of data from a multicenter prospective cohort study. Participants/settingData from 3650 participants, aged 1 month to 18 years, admitted to 100 unique hospitals that participated in 3 PINS was used for this study. Main outcome measuresThe time in days from pediatric intensive care unit admission to the initiation of PN and enteral nutrition delivery were the primary outcomes. Prescribed energy and protein goals were the secondary outcomes. Statistical analyses performedA frailty model with a random intercept per hospital with stratified baseline hazard function by region for the primary outcomes and a mixed-effects negative binomial regression with random intercept per hospital for the secondary outcomes. ResultsThe proportion of patients receiving enteral nutrition (88.3% vs 80.6%; P < .001) was higher, and those receiving PN (20.6% vs 28.8%; P < .001) was lower in the PINS3 cohort compared with PINS1-2. In the PINS3 cohort, the odds of initiating PN during the first 10 days of pediatric intensive care unit admission were lower, compared with the PINS1-2 cohort (hazard ratio 0.8, 95% CI 0.67 to 0.95; P = .013); and prescribed energy goal was lower compared with the PINS1-2 cohort (incident rate ratio 0.918, 95% CI 0.874 to 0.965; P = .001). ConclusionsThe likelihood of initiation of PN delivery significantly decreased during the first 10 days after admission in the PINS3 cohort compared with PINS1-2. Energy goal prescription in children receiving mechanical ventilation significantly decreased in the postguidelines epoch compared with the preguidelines epoch.

Sample size estimation for stratified cluster randomization trial with survival endpoint.

Cluster randomization trials with survival endpoint are predominantly used in drug development and clinical care research when drug treatments or interventions are delivered at a group level. Unlike conventional cluster randomization design, stratified cluster randomization design is generally considered more effective in reducing the impacts of imbalanced baseline prognostic factors and varying cluster sizes between groups when these stratification factors are adopted in the design. Failure to account for stratification and cluster size variability may lead to underpowered analysis and inaccurate sample size estimation. Apart from the sample size estimation in unstratified cluster randomization trials, there are no development of an explicit sample size formula for survival endpoint when a stratified cluster randomization design is employed. In this article, we present a closed-form sample size formula based on the stratified cluster log-rank statistics for stratified cluster randomization trials with survival endpoint. It provides an integrated solution for sample size estimation that account for cluster size variation, baseline hazard heterogeneity, and the estimated intracluster correlation coefficient based on the preliminary data. Simulation studies show that the proposed formula provides the appropriate sample size for achieving the desired statistical power under various parameter configurations. A real example of a stratified cluster randomization trial in the population with stable coronary heart disease is presented to illustrate our method.

Case weighted power priors for hybrid control analyses with time-to-event data.

We develop a method for hybrid analyses that uses external controls to augment internal control arms in randomized controlled trials (RCTs) where the degree of borrowing is determined based on similarity between RCT and external control patients to account for systematic differences (e.g., unmeasured confounders). The method represents a novel extension of the power prior where discounting weights are computed separately for each external control based on compatibility with the randomized control data. The discounting weights are determined using the predictive distribution for the external controls derived via the posterior distribution for time-to-event parameters estimated from the RCT. This method is applied using a proportional hazards regression model with piecewise constant baseline hazard. A simulation study and a real-data example are presented based on a completed trial in non-small cell lung cancer. It is shown that the case weighted power prior provides robust inference under various forms of incompatibility between the external controls and RCT population.

Nonparametric Estimation of Proportional Hazards with Monotone Baseline Hazard and Covariate Effect

Nonparametric Estimation of Proportional Hazards with Monotone Baseline Hazard and Covariate Effect

A New Two-Parameters Lindley-Frailty Model: Censored and Uncensored Schemes under Different Baseline Models: Applications, Assessments, Censored and Uncensored Validation Testing

Classical survival models assume homogeneity among the population of individuals who are susceptible to the event of interest. However, in many practical circumstances, there is a certain amount of unobserved heterogeneity that can be caused by a variety of sources, such as environmental or genetic factors. If the heterogeneity is ignored, many issues could arise, including an overestimation of the hazard rate and inaccurate estimates of the regression coefficients. Frailty models are usually used to model the heterogeneity among individuals. In this paper, we propose a novel univariate frailty model. The frailty variable is assumed to follow the Two Parameter Lindley distribution. The maximum likelihood method is used to estimate the model parameters. The baseline hazard functions are assumed to follow Weibull, Exponential, Gompertz, and Pareto distributions, and a simulation study is performed under this assumption. We examine the characteristics of the distribution and assess its performance compared to other distributions that are frequently applied in frailty modeling by using both Nikulin-Rao-Robson and Bagdonavicius-Nikulin goodness-of-fit tests to determine the adequacy of the model. We analyze a fresh medical dataset collected from an emergency hospital in Algeria to evaluate the effectiveness and applicability of the proposed model.

Partly linear single-index cure models with a nonparametric incidence link function.

In cancer studies, it is commonplace that a fraction of patients participating in the study are cured, such that not all of them will experience a recurrence, or death due to cancer. Also, it is plausible that some covariates, such as the treatment assigned to the patients or demographic characteristics, could affect both the patients' survival rates and cure/incidence rates. A common approach to accommodate these features in survival analysis is to consider a mixture cure survival model with the incidence rate modeled by a logistic regression model and latency part modeled by the Cox proportional hazards model. These modeling assumptions, though typical, restrict the structure of covariate effects on both the incidence and latency components. As a plausible recourse to attain flexibility, we study a class of semiparametric mixture cure models in this article, which incorporates two single-index functions for modeling the two regression components. A hybrid nonparametric maximum likelihood estimation method is proposed, where the cumulative baseline hazard function for uncured subjects is estimated nonparametrically, and the two single-index functions are estimated via Bernstein polynomials. Parameter estimation is carried out via a curated expectation-maximization algorithm. We also conducted a large-scale simulation study to assess the finite-sample performance of the estimator. The proposed methodology is illustrated via application to two cancer datasets.

Case-Base Neural Network: Survival analysis with time-varying, higher-order interactions

In the context of survival analysis, data-driven neural network-based methods have been developed to model complex covariate effects. While these methods may provide better predictive performance than regression-based approaches, not all can model time-varying interactions and complex baseline hazards. To address this, we propose Case-Base Neural Networks (CBNNs) as a new approach that combines the case-base sampling framework with flexible neural network architectures. Using a novel sampling scheme and data augmentation to naturally account for censoring, we construct a feed-forward neural network that includes time as an input. CBNNs predict the probability of an event occurring at a given moment to estimate the full hazard function. We compare the performance of CBNNs to regression and neural network-based survival methods in a simulation and three case studies using two time-dependent metrics. First, we examine performance on a simulation involving a complex baseline hazard and time-varying interactions to assess all methods, with CBNN outperforming competitors. Then, we apply all methods to three real data applications, with CBNNs outperforming the competing models in two studies and showing similar performance in the third. Our results highlight the benefit of combining case-base sampling with deep learning to provide a simple and flexible framework for data-driven modeling of single event survival outcomes that estimates time-varying effects and a complex baseline hazard by design. An R package is available at https://github.com/Jesse-Islam/cbnn.

Shape restricted additive hazards models: Monotone, unimodal, and U-shape hazard functions.

We consider estimation of the semiparametric additive hazards model with an unspecified baseline hazard function where the effect of a continuous covariate has a specific shape but otherwise unspecified. Such estimation is particularly useful for a unimodal hazard function, where the hazard is monotone increasing and monotone decreasing with an unknown mode. A popular approach of the proportional hazards model is limited in such setting due to the complicated structure of the partial likelihood. Our model defines a quadratic loss function, and its simple structure allows a global Hessian matrix that does not involve parameters. Thus, once the global Hessian matrix is computed, a standard quadratic programming method can be applicable by profiling all possible locations of the mode. However, the quadratic programming method may be inefficient to handle a large global Hessian matrix in the profiling algorithm due to a large dimensionality, where the dimension of the global Hessian matrix and number of hypothetical modes are the same order as the sample size. We propose the quadratic pool adjacent violators algorithm to reduce computational costs. The proposed algorithm is extended to the model with a time-dependent covariate with monotone or U-shape hazard function. In simulation studies, our proposed method improves computational speed compared to the quadratic programming method, with bias and mean square error reductions. We analyze data from a recent cardiovascular study.

Regression analysis of group-tested current status data

Summary Group testing is an effective way to reduce the time and cost associated with conducting large-scale screening for infectious diseases. Benefits are realized through testing pools formed by combining specimens, such as blood or urine, from different individuals. In some studies, individuals are assessed only once and a time-to-event endpoint is recorded, for example, the time until infection. Combining group testing with this type of endpoint results in group-tested current status data (Petito &amp; Jewell, 2016). To analyse these complex data, we propose methods that estimate a proportional hazard regression model based on test outcomes from measuring the pools. A sieve maximum likelihood estimation approach is developed that approximates the cumulative baseline hazard function with a piecewise constant function. To identify the sieve estimator, a computationally efficient expectation-maximization algorithm is derived by using data augmentation. Asymptotic properties of both the parametric and nonparametric components of the sieve estimator are then established by applying modern empirical process theory. Numerical results from simulation studies show that our proposed method performs nominally and has advantages over the corresponding estimation method based on individual testing results. We illustrate our work by analysing a chlamydia dataset collected by the State Hygienic Laboratory at the University of Iowa.

Disparities in the incidence of next-generation sequencing (NGS) among patients (pts) with advanced or metastatic prostate cancer (mPC) and urothelial carcinoma (mUC).

542 Background: Based on availability of approved agents targeting tumor somatic mutations, current guidelines recommend NGS for Pts with mPC and mUC. However, large cohort studies in mPC and mUC evaluating disparities in NGS testing have not been reported. Herein, we investigated the effect of social determinant of health (SDOH) on disparities in NGS testing in a large real-world dataset of pts with mPC and mUC. Methods: Pts data were extracted from the nationwide Flatiron Electronic Health Record (EHR)-derived de-identified database. Inclusion: new diagnosis of advanced or metastatic (adv/met) PCa and UCa between March 2015 to December 2022 and receipt of cancer therapy (to include only treatment eligible pts). Disparities in incidence of NGS testing after adv/met diagnosis by SES, R/E, region, and insurance were assessed using Fine and Grey’s (FG) modified stratified Cox proportional hazard (PH) model. FG model considered death as a competing risk. Stratification was done by year of adv/met diagnosis (accounting heterogeneity) and R/E (an effect modifier for SES, region, and insurance), assuming different baseline hazards in each stratum. All analysis done using R v.4.2.3. Results: Analytic cohorts comprised 11,936 pts with mPC and 6,490 pts with mUC. Hazard ratios (HR) and 95% confidence intervals (CI) from FG Cox PH models are reported in the table. For both cancer types, pts were more likely to get NGS testing when they were White, non-Hispanic (compared to Black, non-Hispanic), had higher SES, and had commercial insurance. Conclusions: Our data suggest an association between incidence of NGS testing and several SDOH in mPC and mUC. Further studies are needed to elucidate the reasons for these disparities and ways to mitigate them. [Table: see text]

Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data.

Longitudinal time-to-event analysis is a statistical method to analyze data where covariates are measured repeatedly. In survival studies, the risk for an event is estimated using Cox-proportional hazard model or extended Cox-model for exogenous time-dependent covariates. However, these models are inappropriate for endogenous time-dependent covariates like longitudinally measured biomarkers, Carcinoembryonic Antigen (CEA). Joint models that can simultaneously model the longitudinal covariates and time-to-event data have been proposed as an alternative. The present study highlights the importance of choosing the baseline hazards to get more accurate risk estimation. The study used colon cancer patient data to illustrate and compare four different joint models which differs based on the choice of baseline hazards [piecewise-constant Gauss-Hermite (GH), piecewise-constant pseudo-adaptive GH, Weibull Accelerated Failure time model with GH & B-spline GH]. We conducted simulation study to assess the model consistency with varying sample size (N = 100, 250, 500) and censoring (20 %, 50 %, 70 %) proportions. In colon cancer patient data, based on Akaike information criteria (AIC) and Bayesian information criteria (BIC), piecewise-constant pseudo-adaptive GH was found to be the best fitted model. Despite differences in model fit, the hazards obtained from the four models were similar. The study identified composite stage as a prognostic factor for time-to-event and the longitudinal outcome, CEA as a dynamic predictor for overall survival in colon cancer patients. Based on the simulation study Piecewise-PH-aGH was found to be the best model with least AIC and BIC values, and highest coverage probability(CP). While the Bias, and RMSE for all the models showed a competitive performance. However, Piecewise-PH-aGH has shown least bias and RMSE in most of the combinations and has taken the shortest computation time, which shows its computational efficiency. This study is the first of its kind to discuss on the choice of baseline hazards.

One-shot distributed algorithms for addressing heterogeneity in competing risks data across clinical sites

ObjectiveTo characterize the interplay between multiple medical conditions across sites and account for the heterogeneity in patient population characteristics across sites within a distributed research network, we develop a one-shot algorithm that can efficiently utilize summary-level data from various institutions. By applying our proposed algorithm to a large pediatric cohort across four national Children’s hospitals, we replicated a recently published prospective cohort, the RISK study, and quantified the impact of the risk factors associated with the penetrating or stricturing behaviors of pediatric Crohn's disease (PCD). MethodsIn this study, we introduce the ODACoRH algorithm, a one-shot distributed algorithm designed for the competing risks model with heterogeneity. Our approach considers the variability in baseline hazard functions of multiple endpoints of interest across different sites. To accomplish this, we build a surrogate likelihood function by combining patient-level data from the local site with aggregated data from other external sites. We validated our method through extensive simulation studies and replication of the RISK study to investigate the impact of risk factors on the PCD for adolescents and children from four children's hospitals within the PEDSnet, A National Pediatric Learning Health System. To evaluate our ODACoRH algorithm, we compared results from the ODACoRH algorithms with those from meta-analysis as well as those derived from the pooled data. ResultsThe ODACoRH algorithm had the smallest relative bias to the gold standard method (−0.2%), outperforming the meta-analysis method (−11.4%). In the PCD association study, the estimated subdistribution hazard ratios obtained through the ODACoRH algorithms are identical on par with the results derived from pooled data, which demonstrates the high reliability of our federated learning algorithms. From a clinical standpoint, the identified risk factors for PCD align well with the RISK study published in the Lancet in 2017 and other published studies, supporting the validity of our findings. ConclusionWith the ODACoRH algorithm, we demonstrate the capability of effectively integrating data from multiple sites in a decentralized data setting while accounting for between-site heterogeneity. Importantly, our study reveals several crucial clinical risk factors for PCD that merit further investigations.

Sex-Specific Prognosis of Left Ventricular Size and Function Following Repair of Degenerative Mitral Regurgitation

BackgroundPrior studies have demonstrated worse long-term outcomes for women after surgery for severe mitral regurgitation (MR). The current Class I indications for surgery for severe degenerative MR use cutoffs of left ventricular end-systolic dimension (LVESD) and left ventricular ejection fraction (EF) that do not account for known sex-related differences. ObjectivesThe primary objective of this study was to assess long-term mortality following mitral valve repair in women compared with men on the basis of preoperative left ventricular systolic dimensions and EF. MethodsConsecutive patients who underwent isolated mitral valve repair for degenerative MR at a single institution between 1994 and 2016 were screened. Adjusted HRs for all-cause mortality were compared according to baseline LVESD, LVESD indexed to body surface area (LVESDi), and EF for men and women. ResultsAmong 4,589 patients, 1,825 were women (40%), and after a median follow-up period of 7.2 years, 344 patients (7.5%) had died. The risk for mortality for women increased from the baseline hazard at an LVESD of 3.6 cm, whereas an inflection point for increased risk with LVESD was not evident in men. Regarding LVESDi, the risk for women increased at 1.8 cm/m2 compared with 2.1 cm/m2 in men. For EF, women and men had a similar inflection point (58%); however, mortality was higher for women as EF decreased. ConclusionsAfter mitral valve repair, women have a higher risk for all-cause mortality at lower LVESD and LVESDi and higher EF. These results support consideration of sex-specific thresholds for LVESDi in surgical decision making for patients with severe MR.

Environmental risk assessment of landfill

The paper aims to conduct a risk analysis approach that can be used to manage landfill impacts on the environment and public health. This study reviewed the application of environmental risk assessment in landfills. Assessment has been conducted for 30 articles from 2018-2022 resulting in the summary of the baseline data, hazard identification, exposure assessment, risk characterization, and risk management. The study found the necessity of detailed baseline data of landfill sites, identifying relevant toxicity data, recognizing exposed receptors, and potential exposure pathways. It is necessary to conduct research that considers the age of the landfill (old and new landfill) to find out the cumulative effects of the landfill and research related to the impact of the landfill on the health of communities around the landfill at a certain distance from the landfill.