Dynamic Risk Assessment Research Articles

Dynamic probabilistic risk assessment considering the domino effect in chemical parks based on Monte Carlo simulation

Dynamic probabilistic risk assessment considering the domino effect in chemical parks based on Monte Carlo simulation

10 TIPS on how to use dynamic risk assessment and alerts for AKI

Abstract Acute kidney injury (AKI) is a common syndrome in hospitalized patients and is associated with increased morbidity and mortality. The focus of AKI care requires a shift away from strictly supportive management of established injury to the early identification and timely prevention of worsening renal injury. Identifying patients at risk for developing or progression of severe AKI is crucial for improving patient outcomes, reducing length of hospitalization and minimizing resource utilization. Implementation of dynamic risk scores and incorporation of novel biomarkers show promise for early detection and minimizing progression of AKI. Like any risk assessment tools, these require further external validation in a variety of clinical settings prior to widespread implementation. Additionally, alerts which may minimize exposure to a variety of nephrotoxic medications or prompt early nephrology consultation are shown to reduce the incidence and progression of AKI severity and enhance renal recovery. While dynamic risk scores and alerts are valuable, implementation requires thoughtfulness and should be used in conjunction with the overall clinical picture in certain situations, particularly when considering the initiation of fluid and diuretic administration or renal replacement therapy. Despite the contemporary challenges encountered with alert fatigue, implementing an alert-based bundle to improve AKI care is associated with improved outcomes, even when implementation is incomplete. Lastly, all alert-based interventions should be validated at an institutional level and assessed for their ability to improve institutionally relevant, clinical meaningful outcomes, reduce resource utilization and provide cost effective interventions.

A dynamic fire risk assessment method for compact historic villages based on the improved FRAME

There are a large number of high-value heritage sites in the historic villages preserved in the mountainous areas of southern China. These sites are exposed to major fire risks due to their compact layout and wooden building materials. By improving the fire risk assessment method for engineering (FRAME), we proposed a dynamic fire risk assessment method for compact historic villages. On one hand, a balance between universality and pertinence was established regarding the assessment indexes and weighting system by integrating general factors with unique regional factors. On the other hand, a connecting link between prescriptive fire codes and performance-based design adjustments was established through the cyclic operation path of assessment-intervention-reassessment. Xiaozhai village, which has experienced serious fire incidents, was selected as an example. Its original fire risk, established fire interventions, actual fire results, and optimized fire protection plan were compared and analyzed to validate the effectiveness and reliability of the assessment method. This study reveals the correlation between various factors of the village and building fire risk. Among these factors, building proximity, traditional customs, and fire protection layout, which are not included in the original FRAME, have a significant impact on fire risk. The results of this study can serve as a reference for fire prevention in historic villages with similar characteristics worldwide. Additionally, the proposed methodology can provide insights into the development of fire risk assessment methods for various types of historical settlements, thus promoting the sustainable development of built heritage in these settlements.

A 4IR-Driven operational risk model for CO2 storage in deepwater abandoned hydrocarbon reservoirs

This study presents the development of an advanced operational risk model that leverages Fourth Industrial Revolution (4IR) technologies to optimize carbon dioxide (CO2) storage within deepwater abandoned hydrocarbon reservoirs. The model systematically combines Artificial Neural Networks (ANN) with the optimization capabilities of Genetic Algorithms (GA) and the probabilistic analysis strengths of a Bayesian Network (BN) to perform dynamic and comprehensive risk assessments. By applying the model to a dataset covering a 200-year timeframe, it effectively forecasts CO2 storage capacities while simultaneously evaluating associated risks across different operational scenarios. One of the key innovations of this model is the introduction of a novel loss function designed to precisely manage forecast deviations and enhance the efficiency of operational processes. This function is critical in ensuring that the model remains robust and accurate in real-time risk assessments, allowing for more reliable decision-making in CO2 storage operations. In addition, the study conducts an economic evaluation that underscores the crucial role of 45Q tax credits in bolstering the financial sustainability of carbon sequestration projects. The analysis highlights how these credits significantly reduce the economic barriers to adopting carbon utilization, storage, and sequestration (CUSS) technologies, making large-scale implementation more feasible. The model's performance is underscored by its ability to achieve a 49% CO2 retention rate over two centuries, with an impressively low average error margin of 0.249%. These results highlight the model's impressive efficiency and accuracy, while also demonstrating its capacity to markedly improve the predictability of CO2 storage outcomes. The findings suggest that this model could play a pivotal role in advancing global sustainability efforts by optimizing CO2 storage processes, thereby contributing to the reduction of atmospheric CO2 levels and supporting long-term climate goals.

Developing Individualized Follow-Up Strategies Based on High-Risk Recurrence Factors and Dynamic Risk Assessment for Locally Advanced Rectal Cancer.

Locally advanced rectal cancer (LARC) is one of the most common malignant tumors worldwide, and its incidence is increasing year by year. Despite multimodal treatment, the recurrence rate of LARC patients remains high, about 20%-50%. However, the follow-up strategy according to tumor stage has certain limitations. There is no consensus on the optimal frequency and duration of follow-up. This study aims to comprehensively analyze the high-risk factors for recurrence in LARC from clinical characteristics, nutritional indicators, and imaging indexes. It intends to utilize conditional survival (CS) evaluation to assess dynamic survival and recurrence risks after comprehensive treatment of LARC and to develop individualized follow-up strategies. Logistic regression was utilized to analyze the independent recurrence factors in LARC patients. Calibration curve, decision curve, and ROC curve were employed to evaluate the model's efficacy. Kaplan-Meier curve was used to calculate CS rate and compare survival differences among different risk groups. A total of 561 patients were analyzed in our study. Our multivariable logistic regression analysis revealed that the prognostic nutritional index (PNI), extramural vascular invasion (EMVI), vascular tumor thrombus, perineural invasion, and tumor size were independent factors for recurrence. Subsequently, a nomogram model was constructed and risk stratification was performed. Calibration curves and decision curves demonstrated that the model exhibited good clinical efficacy. The area under the ROC curve for the model was 0.763, indicating good sensitivity and specificity. Kaplan-Meier curves showed significant differences in survival among different risk groups. Furthermore, we observed that the CS without local recurrence and distant metastasis increased each year, while the cumulative recurrence risk decreased annually with prolonged survival time. Tailored follow-up intensities were developed for different risk groups and clinical stages based on the cumulative recurrence risk. The personalized follow-up strategy based on risk stratification can optimize resource allocation, early detection of recurrence or metastasis, and ultimately enhance the overall care and prognosis of LARC patients.

A dynamic risk assessment model for evacuation in healthcare facilities during fire scenarios

Evacuation is a critical procedure for safeguarding individuals during a fire incident within a healthcare facility. Assessing the risk associated with the evacuation process is instrumental in enhancing emergency decision-making and developing contingency strategies. Evacuation, a dynamic process, poses challenges in accurate risk evaluation using conventional static quantitative risk assessment (QRA) techniques. This study introduces a dynamic QRA approach to precisely evaluate the evacuation risk to individuals in healthcare settings during a fire. The dynamic event tree methodology is initially utilized to determine the evolving probabilities of various fire scenarios, considering the equipment failure and repair rates. Subsequently, data on fire products and the evacuation of individuals under different fire scenarios are gathered through simulations conducted with Fire Dynamics Simulator (FDS) and Pathfinder software. In addition, this research proposes a dynamic incapacitation model to calculate the consequences of fire, incorporating changes in individuals' locations and the fire products surrounding them during evacuation to accurately estimate the casualty risk. Lastly, the dynamic scenario probability was integrated with the average incapacitation probability for all individuals, introducing the concept of a fire evacuation safety time window to comprehensively assess hospital fire evacuation risk. The findings revealed that the dynamic quantitative risk analysis (DQRA) approach more effectively captures the influence of fire products on the evacuation risk compared to static methods. Specifically, the peak incapacitation probability in a hospital fire reached 0.85, with firefighting equipment, especially the sprinkler system, reducing this risk by over 97% and decreasing evacuation time by 45%. In addition, the risk of fire evacuation in hospitals reaches a critical threshold in the fourth year, enhancing the repair rate of the firefighting system postpones the risk's escalation to a critical threshold until the fourteenth year.

A Bayesian Network Inference Approach for Dynamic Risk Assessment Using Multisource-Based Information Fusion in an Interval Type-2 Fuzzy Set Environment

A Bayesian Network Inference Approach for Dynamic Risk Assessment Using Multisource-Based Information Fusion in an Interval Type-2 Fuzzy Set Environment

Insight process safety of a hydrogen turbine supply system: A comprehensive dynamic risk assessment using a fuzzy Bayesian network

This study conducts a comprehensive risk assessment for a selected hydrogen supply system within a pilot hydrogen power plant. The assessment begins with hazard identification through Hazard and Operability (HAZOP) analysis, followed by quantification using bow-tie analysis and consequence modeling to evaluate the impacts of credible scenarios resulting from random failures. Furthermore, a fuzzy Bayesian network is employed to address uncertainties inherent in the quantitative approaches and to account for event combinations and dependencies. Sensitivity analysis is subsequently conducted to identify critical events. Utilizing three quantitative approaches, fault tree analysis, the Bayesian network, without and with evidence updating, the probability of the top event is determined to be 4.52 × 10−2/year, 4.50 × 10−2/year, and 4.70 × 10−2/year, respectively. Additionally, the estimated firezone resulting from the explosion's 300 mbar overpressure was 12 m. The findings highlight the significant role of pressure regulator valve and pressure transmitter failures, with posterior probabilities of 0.17/year and 1.90 × 10−3/year, in ensuring the system's safety.

Dynamic Analysis and Risk Assessment of Vegetation Net Primary Productivity in Xinjiang, China

Dynamic Analysis and Risk Assessment of Vegetation Net Primary Productivity in Xinjiang, China

Dynamic risk assessment framework for fire of power critical infrastructure: The case study of UHV converter transformer

Dynamic risk assessment framework for fire of power critical infrastructure: The case study of UHV converter transformer

Enhanced hybrid LSTM and SLAR modeling for in-depth analysis of temporal and spatial patterns in compositional data for environmental monitoring

A novel methodology for analyzing compositional data (CoDa) integrates long short-term memory (LSTM) networks with spatial lag autoregressive (SLAR) models to simultaneously capture temporal and spatial patterns. The proposed approach leverages LSTM’s strengths in handling temporal relationships and SLAR’s ability to capture spatial correlations, employing the centered log-ratio (CLR) transformation to manage CoDa’s characteristics, making it suitable for advanced analysis. The Adam optimizer ensures efficient and stable model training, enhancing convergence speed and precision. The combined approach involves preprocessing CoDa with CLR, initializing LSTM layers, performing forward passes through LSTM, integrating the SLAR model, generating outputs, post-processing data, calculating loss, and performing backpropagation. The methodology’s effectiveness is demonstrated through a case study on pollutant emissions data from a waste incineration plant, focusing on key pollutants such as Dioxins and Furans, Heavy Metals (Hg, Pb, Cd), Nitrogen Oxides (NO), Sulfur Dioxide (SO), Particulate Matter (PM), and Carbon Monoxide (CO). The LSTM network, configured with two hidden layers, dynamically adjusts learning rates for faster convergence. Integrating SLAR improves spatial pattern accounting, significantly enhancing predictive accuracy. Comparative analysis shows substantial performance metric improvements over standard LSTM models, with the LSTM-SLAR model reducing errors and explaining a higher proportion of data variability. In process safety and risk engineering, the proposed methodology enhances pollutant emission monitoring and control, enables proactive risk management, ensures regulatory compliance, and improves risk assessment accuracy, making it invaluable for dynamic risk assessment in environmental monitoring and is broadly applicable to fields requiring detailed temporal and spatial analysis, demonstrating robustness and versatility in handling complex CoDa.

Observational cohort study on safety and efficacy of robotic thyroidectomy with super-meticulous capsular dissection versus open surgery for thyroid cancer: Postoperative dynamic risk assessment of radioactive iodine therapy.

To assess the efficacy and safety of robotic thyroidectomy (RT) with super-meticulous capsular dissection (SMCD) versus open thyroidectomy (OT) we used a dynamic risk assessment system incorporating 131I-WBS along with radioactive iodine (RAI) efficacy evaluation. Currently, the therapeutic efficacy of robotic surgery remains controversial. The 131I whole-body scan (131I-WBS) dynamic risk assessment system can detect small residual thyroid tissues and lesions, which may be used as indicators for the surgical efficacy of RT or OT thyroidectomy in differentiated thyroid cancer (DTC). This retrospective cohort study included 2,349 patients who underwent total thyroidectomy followed by RAI therapy in our department between August 2017 and June 2023. Propensity score matching was performed at a ratio of 1:3 based on surgical type and mean follow-up duration to minimize selection bias after excluding those lost to follow-up. The primary outcome was surgical completeness, assessed using a dynamic risk system incorporating 131I-WBS along with RAI efficacy evaluation. There was no significant difference in the number of metastatic lymph nodes removed between the two groups (P=0.45). The incidence rate of parathyroid gland transplantation was 395 (68.7%) in the OT group and 8 (3.8%) in the RT group (P<0.001). There were no differences in the thyroidectomy completeness based on the 3-hour iodine uptake rate and 99mTcO4- thyroid imaging between the two groups. The dynamic risk assessment with and without 131I-WBS showed significant differences (P<0.001). The postoperative and post-RAI dynamic risk scores, evaluated at the time of RAI and 6 months after RAI, did not differ significantly between the two groups (P>0.05). The rates of transient and permanent hypoparathyroidism were higher in the OT group than in the RT group (P<0.05). The local recurrence rates showed no significant difference between the groups. This study demonstrates that RT with SMCD can achieve outcomes equivalent to those of traditional open surgery when integrating the 131I-WBS dynamic evaluation system and the therapeutic effects of RAI. Additionally, robot surgery demonstrated a notable advantage in protecting parathyroid function.

Innovative Approaches in Therapeutic Clinical Nutrition: Enhancing Quality of Care through Technology

Background: This systematic review explores a diverse range of international and indigenous healthcare research studies, spanning topics from diabetes management to cardiovascular care. The synthesis aims to identify overarching themes, sub-themes, and emerging trends in healthcare practices, emphasizing the integration of digital technologies and advancements in nutrition therapy. Aim: The primary objective is to provide a comprehensive overview of healthcare literature, combining international and indigenous perspectives, to uncover commonalities and distinctions in approaches to patient care, treatment modalities, and research methodologies. Method: A systematic and thorough review of 20 selected studies, encompassing international and Saudi Arabian research, was conducted. The studies, published between 2020 and 2024, were analyzed for their key themes, sub-themes, and trends, creating a detailed understanding of the current landscape of healthcare practices. Results: The synthesis revealed recurring themes, including the integration of digital technologies in diabetes care, advancements in medical nutrition therapy for obesity management, dynamic risk assessment in atrial fibrillation, and the potential of genetically modified food for nutritional security in Saudi Arabia. Sub-themes elucidated nuanced aspects of these overarching themes, offering a holistic perspective on contemporary healthcare strategies. Conclusion: International and indigenous research studies collectively underscore the importance of collaborative and context-specific approaches to healthcare. While global insights contribute to a broader understanding, indigenous studies emphasize the necessity of tailoring interventions to local needs. The synthesis not only highlights current achievements in healthcare but also identifies areas for further exploration and improvement.

Dynamic industrial risks assessment for enhanced safety in the oil and gas industry: a methodological innovations and applications

In recent years, continued and accelerated growth in the oil and gas sector has significantly contributed to catastrophic accidents in various regions of Algeria, especially those with high population densities. Therefore, assessing the risks associated with these industries is crucial to prevent such disasters. This study introduces a methodology integrating several Quantitative Risk Assessment techniques, such as Failure Modes and Effects Analysis, Fault Tree Analysis, Event Tree Analysis, and the Bow-Tie method, coupled with simulation tools (ALOHA and FLUENT). This integration created a Dynamic Industrial Risk Assessment Methodology (DIRAM), a comprehensive approach to assessing industrial risk consequences in the oil and gas industries. The study demonstrates DIRAM's effectiveness in understanding the dynamics of catastrophic events. It provides a visual representation of CO2 plume and heat dispersion after a Boiling Liquid Expanding Vapor Explosion (BLEVE), emphasising the importance of spatial-temporal awareness in risk assessment and emergency response. DIRAM offers a valuable basis for preventing and managing potential risks and reinforcing safety measures at hydrocarbon plants.

Quantitative assessment rules and models for dynamic disaster risk in high-density gas gathering stations: Practical application in a largest CBM gathering station

Coalbed Methane (CBM) gathering stations involve multiple engineering disciplines, with a concentration of pressure vessels and strong production continuity. Once accidents occur, they can easily result in severe casualties, property losses, and environmental pollution. Therefore, prior to the large-scale promotion and urban application of CBM, this paper proposes a dynamic quantitative risk assessment model for the operational and task-related risks in CBM gathering stations. Firstly, various methods are employed for risk factor identification. Then, a dynamic Bayesian network (DBN) model is constructed to assess the dynamic operational risks and task-related risks of CBM gathering stations under different maintenance conditions. This model provides the initial probability of operational risk occurrence at stations, predicts changes in the probability of equipment multi-state operational risks under different maintenance conditions over 10 years, and forecasts the probability of task-related risks during operations. Finally, a combined weighting model is established, incorporating instance information to comprehensively evaluate safety management and derive safety management correction coefficients. These coefficients are integrated with the probabilities of station operation and task-related risk occurrence to ultimately determine the likelihood of operational risk occurrence during station production, completing the dynamic quantitative assessment of operational risks in CBM gathering stations.

Characteristics of Overburden Damage and Rainfall-Induced Disaster Mechanisms in Shallowly Buried Coal Seam Mining: A Case Study in a Gully Region

Shallow coal mining in gully regions has resulted in significant subsidence hazards and increased the risk of surface water inflow into mining panels, compromising the sustainability of surface water management and underground resource exploitation. In this study, the chain disaster process caused by shallow coal seam mining and heavy rainfall is quantitatively analyzed. The findings reveal that shallow coal seam mining leads to the formation of caved and fractured zones in the vertical direction of the overlying rock. The fractured zone can be further classified into a compression subsidence zone and a shear subsidence zone in the horizontal direction. The shear subsidence zone is responsible for generating compression and shear deformations, intercepting rainfall runoff, and potentially triggering landslides, necessitating crack landfill treatments, which are critical for promoting sustainable mining practices. The HEC-RAS program was utilized to integrate annual maximum daily rainfall data across different frequencies, enabling the establishment of a dynamic risk assessment model for barrier lakes. Numerical simulations based on unsaturated seepage theory provide insights into the infiltration and seepage behavior of rainfall in the study area, indicating a significant increase in saturation within lower gully terrain. Precipitation infiltration was found to enhance the saturation of the shallow rock mass, reducing matric suction in unsaturated areas. Finally, the disaster chain is discussed, and recommendations for managing different stages of risk are proposed. This study offers a valuable reference for the prevention and control of surface water damage under coal mining conditions in gully regions.

Dynamic risk assessment of Uncertain Random System considering operator's simple emergency-stop action in short time window

Human-machine systems can be considered as typical Uncertain Random Systems (URS). In human-machine systems, operator's simple emergency-stop actions act as a vital soft barrier. Conventional Human Reliability Analyses face challenges in evaluating operator's simple emergency-stop action due to their static characteristics, incompatibility with short time windows, and problems posed by small samples. This paper provides a novel solution to risk assessment of human-machine systems involving operator's simple emergency-stop mechanism. Specifically, an evaluation method of operator's simple emergency-stop action is established, where human cognition is decomposed according to its temporal and logical dimensions, key indicators of human sub-cognition are evaluated, and uncertainty theory is used to integrate these indicators. The output of proposed method is uncertainty of operator's behavior (UOB), which can offer more information than traditional Human Error Probability (HEP) and can be readily converted into uncertainty of human error. Further, a precise simulation method for dynamic risk assessment of Uncertain Random Systems is developed, in which the integration of the UOB is achieved. A case study demonstrates the effectiveness of the proposed methods, and its results show that the proposed methods can clearly reflect the differences in system risk under short time window conditions that have minor distinctions.

Dynamic risk assessment of natural gas transmission pipelines with LSTM networks and historical failure data

In the realm of energy infrastructure, ensuring the security of gas transmission pipelines is critical. This research introduces an advanced dynamic risk assessment framework that leverages the predictive capabilities of LSTM networks, presenting an improvement over conventional failure prediction models. Unlike traditional approaches that rely on averaging historical failure records, this framework dynamically processes historical pipeline failure incidents into sequential time series analysis, facilitating and improving the accuracy of the current failure rate. The model refines the failure rate estimation for individual pipelines by incorporating unique characteristics and modification factors, resulting in a highly precise failure likelihood estimation. Additionally, this study introduces a quantifiable linkage between mortality risk and the fatality probit value across various accident scenarios, enhancing consequence evaluation. A sensitivity analysis is then performed to assess the impact of various input parameters on the model's performance. The practical application of the model on a U.S. pipeline confirms its effectiveness. This proposed methodology substantially enhances the understanding of incident causation in gas pipeline systems, paving the way for superior safety management strategies. It is instrumental in enhancing pipeline safety, refining infrastructure planning, and optimizing safety resource allocation. The methodology offers benefits for pipeline operators, industry professionals, and regulatory agencies, contributing to improved operational safety and resource management in the pipeline industry.

Assessment of dynamic hydrological drought risk from a non‐stationary perspective

AbstractThe stationarity hypothesis of hydrometeorological elements has been questioned in the context of global warming and intense human disturbance. The conventional drought index and methods of frequency analysis may no longer be applicable for hydrological drought risk evaluation under a changing environment. In this study, a new dynamic hydrological drought risk evaluation framework is proposed for application to the Hanjiang River basin (HRB), which simultaneously considers the non‐stationarity in the construction of drought index as well as in the frequency analysis. First, a non‐stationary standardized runoff index (NSRI) is developed using a generalized additive model for location, scale and shape (GAMLSS) framework. Then, hydrological drought characteristics including duration and severity are identified, and their marginal distributions are established. Finally, based on the dynamic copula, considering the non‐stationarity of the dependence structure, the dynamic joint probability distribution, conditional probability distribution and return period of the bivariate hydrological drought properties are analysed. Results showed that NSRI, which integrates the impacts of climate change and anthropogenic activities on the non‐stationarity of runoff series, had a better ability to capture runoff extremes than had SRI. In addition, it is indispensable to consider the non‐stationarity of the dependence structure between variables when discussing the multivariate joint risk of hydrological drought. The risk of hydrological drought in the study area has shown an increasing trend in the past 65 years, and the drought conditions from upstream to downstream have been alleviated first and then intensified. This study provides valuable information for regional drought risk estimation and water resources management from a non‐stationary perspective.

Variation in intention-to-treat survival by MELD subtypes: All models created for end-stage liver disease are not equal

Kidney dysfunction is a major determinant of prognosis in patients with decompensated cirrhosis awaiting transplantation. We hypothesized that for identical model for end-stage liver disease (MELD) scores at listing, outcomes before and after liver transplantation may vary if the predominant driver of the MELD score is serum creatinine (Cr) vs. serum bilirubin (Br) or international normalized ratio (INR). We evaluated all adult patients registered for liver transplantation (LT) between 2016-2020 and excluded patients receiving MELD exceptions or undergoing dual organ transplantation. Using K-Means clustering analysis, we classified each patient as MELD-Br, MELD-INR or MELD-Cr depending on the dominant variable for their MELD score. The primary outcome was intent-to-treat (ITT) survival, defined as survival within 1 year from listing with or without LT. MELD scores of LT waitlist registrants were clustered into three subtypes: MELD-Br (n= 13,658), MELD-INR (n= 13,809), and MELD-Cr (n= 12,412). One-year ITT survival rates were 78% (MELD-Br), 75% (MELD-INR), and 65% (MELD-Cr), p <0.01. ITT survival was lower for each MELD subtype for females compared to males (e.g. MELD-Cr: 63% females vs. 67% males, p<0.0001). The MELD-Cr subtype had the highest MELD at listing (MELD-Cr 23.4 vs. MELD-Br 19.2 vs. MELD-INR 21.0) and the largest decline in MELD over 3 months (23% vs. 12% vs. 21%). In adjusted analyses including MELD-Na, MELD-Cr was associated with higher waitlist mortality (hazard ratio 1.339, 95% CI 1.279-1.402) and lower LT rates (hazard ratio 0.688, 95% CI 0.664-0.713) compaed to the other subtypes. For equivalent listing practices, registrants with the MELD-Cr subtype have lower ITT survival. MELD subtype may serve as a more sophisticated variable for dynamic assessment of mortality risk and to guide organ allocation. The model for end-stage liver disease (MELD) score is an excellent predictor of waitlist mortality; however, our work highlights that the driver of a patient's MELD score matters and particularly those driven by elevated creatinine are associated with lower 1-year intent-to-treat survival. The 1-year intent-to-treat survival is also lower for women compared to men within the Cr-dominant subtype. These results are important for physicians and patients undergoing LT evaluation as creatinine may serve as a marker of prognosis and even if creatinine levels improve the prognosis remains poor, necessitating discussion about alternative pathways for transplant. Our work also highlights that the type of kidney injury matters, in that those with acute kidney injury were more likely to die or remain on the waitlist than those with chronic kidney disease within the creatinine-dominant subtype.