Dynamic Risk Research Articles

Increasing extreme hourly precipitation risk for New York City after Hurricane Ida.

The remnants of Hurricane Ida caused major damage and death in the United States on September 1st, 2021, and 11 people drowned in flooded basement apartments within New York City (NYC). It was catastrophic because the maximum hourly precipitation intensity, recorded as 3.47 inches (88.1mm) per hour at Central Park, was unprecedentedly high for the NYC region. The stormwater infrastructure in NYC is built for 1.75 inches (44.5mm) per hour, and so understanding the dynamic risk associated with Ida can inform city planning efforts for climate change's impact on short duration extreme precipitation events. We contextualize this storm's record-breaking hourly intensity within the historical record as well as project its risk in the near- to medium-term future using nonstationary stochastic models. These models are conditioned on average temperature (Tavg) and cooling degree day (CDD) projections from three climate models as a covariate, each with a SSP 126 and SSP 370 scenario. The likelihood of such a storm was slowly increasing even before Ida happened, but the projected aggregate reoccurrence risk of an event of Ida's magnitude over time from the non-stationary models ranges from 4 to 52 times higher than the risk given by the stationary model. Using CDD as a covariate resulted in risks that were more than twice the magnitude than when using Tavg. Presenting both covariates provides a broader envelope of uncertainty, which highlights the importance and nuances in the choice of a regionally appropriate covariate for non-stationary risk analysis.

Residue Dissipation Dynamics and Dietary Risk Assessment of Kasugamycin, Oxine-Copper, Dimethomorph, and Pyraclostrobin in Cucumber

Residue Dissipation Dynamics and Dietary Risk Assessment of Kasugamycin, Oxine-Copper, Dimethomorph, and Pyraclostrobin in Cucumber

Abstract IA018: Liquid biopsy in head and neck cancers: A model for tissue-agnostic MRD detection

Abstract Head and neck cancer (HNC) is a molecularly heterogenous disease with a variable clinical course. Current treatment decisions rely primarily on pre-treatment prognostic factors without consideration of treatment response. This presentation will introduce liquid biopsy biomarkers that can be repeated in HNC patients for the purpose of dynamic risk stratification, treatment adaptation, and post-treatment surveillance. Novel approaches to measuring circulating tumor DNA (ctDNA) will be described, with particular attention to viral-associated and non-viral- associated subtypes of HNC. Detection of molecular residual disease (MRD) using ctDNA will be discussed. As HNC is often treated by non-surgical means, there is a pressing need for tissue- agnostic methods for ctDNA-based MRD detection. Analysis of tumor-specific genetic and epigenetic aberrations within ctDNA each present distinct opportunities and challenges for tissue-agnostic characterization of ctDNA. Viral ctDNA has emerged as a convenient target for MRD detection in viral-associated HNC, while ctDNA methylome profiling offers a versatile approach for tissue-agnostic MRD detection across HNC subtypes. Future applications of these methods may improve individualized management of HNC patients without requiring access to precious tumor tissue samples. Citation Format: Scott V Bratman. Liquid biopsy in head and neck cancers: A model for tissue-agnostic MRD detection [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr IA018.

Dynamic inferences of crash risks in freeway merging zones: a spatio-temporal deep learning model

Freeway merging zones are critical for freeway operations and management due to potential crashes arising from complex vehicle merging behaviours. This paper investigates the application of a spatio-temporal deep learning model to infer crash risks in the zones. We first introduce a crash risk index based on Time-To-Collision and vehicle merging patterns. An innovation is that the developed spatio-temporal transformer model can analyze the evolving risk index. This model effectively captures dynamic risk features through a multi-head attention mechanism within its spatio-temporal learning components. Numerical experiments on nine inference tasks with varying spatial resolutions show improved performance of the model with lower resolution. Moreover, the ST-Transformer model is benchmarked against three advanced deep learning models, which consistently demonstrates its superiority in capturing spatio-temporal dependence in risk sequences. This investigation significantly contributes to a richer understanding of proactive traffic safety, providing valuable insights for advanced freeway management and driver assistance systems.

Dynamic risk factors for complex regional pain syndrome after distal radius fracture surgery: multivariate analysis and prediction.

Distal radius fractures are a common orthopedic injury, particularly prevalent among elderly and osteoporotic patients. Although surgical treatment effectively restores fracture alignment and stabilizes the bone, Complex Regional Pain Syndrome (CRPS) can occur as a postoperative complication, severely impacting patients' rehabilitation and quality of life. Therefore, assessing the risk factors for postoperative CRPS is crucial for early clinical intervention, aiming to improve recovery outcomes and overall patient well-being. This study aims to dynamically evaluate the risk factors for CRPS following distal radius fracture surgery through a retrospective analysis. The goal is to provide scientific evidence for early clinical identification and intervention. A retrospective analysis was conducted on 191 adult patients who underwent distal radius fracture surgery between January 2021 and December 2022, comprising 89 males and 102 females, with an average age of 59.7 ± 11.6(18-75) years. Baseline and follow-up data, including demographic information, clinical characteristics, and psychological assessments, were collected. Descriptive statistics, univariate, and multivariate logistic regression analyses were used to identify independent risk factors and assess their dynamic changes over time. Age, gender, occupation, fracture type, surgical method, osteoporosis, pain score (VAS), functional disability score (DASH), anxiety score (GAD-7), depression score (PHQ-9), physical therapy, and occupational therapy significantly affected the incidence of CRPS. Age and VAS scores were consistent risk factors. Female gender, internal fixation surgery, and high DASH scores posed higher risks in the early postoperative period but decreased over time. Occupation and fracture type were more significant in the mid to late postoperative period. Psychological factors were most significant in the midterm, while physical and occupational therapy were protective factors early on. The occurrence of CRPS is the result of many factors. Elderly patients, females, and manual laborers had higher risks, with complex fractures and internal fixation surgery patients showing increased risk later on. Osteoporotic patients had higher risks in the mid to late postoperative period. VAS, DASH, GAD-7, and PHQ-9 scores were significantly associated with CRPS. Early physical and occupational therapy can effectively prevent CRPS, but further research is needed to evaluate their long-term effects.

Evolutionary-based ensemble feature selection technique for dynamic application-specific credit risk optimization in FinTech lending

Evolutionary-based ensemble feature selection technique for dynamic application-specific credit risk optimization in FinTech lending

Where Should We Intervene, 20 Years Later? Case–Control and Prospective Cohort Designs Provide Similar Answers

In 2000, this journal published an influential case–control study identifying dynamic risk factors for sexual recidivism (Hanson & Harris, 2000). In 2017, updated recidivism information for the same sample was obtained with an average follow-up of 20 years. The current study compared the risk factors that differentiated between sexual recidivists and nonrecidivists between the two research designs: original case–control and updated prospective cohort. Of the 82 comparisons, 50 favored the prospective design while 32 favored the case–control; however, most of the differences were small and nonsignificant. Static and dynamic risk factors were approximately equivalent between study designs. Factors identified as sex-specific (e.g., sexual deviancy) were also equivalent between designs while general risk factors (e.g., substance use) were more likely identified in the prospective design. Overall, case–control studies can be used for the identification of risk factors, especially for low base rate behaviors such as sexual recidivism.

Spatial Analyses of Crisis Pregnancy Centers and Abortion Facilities in the United States, 2021 (Pre-Dobbs): Cross-Sectional Study

Background Crisis pregnancy centers (CPCs) are religious nonprofit organizations with a primary mission of diverting people from having abortions. One CPC tactic has been to locate near abortion facilities. Despite medical groups’ warnings that CPCs do not adhere to medical and ethical standards and pose risks, government support for CPCs has significantly increased. Objective This study aims to map CPCs, abortion facilities, and geographical areas in the United States into 4 zones based on their proximity to CPCs and abortion facilities. We sought to describe the number and percentage of reproductive-aged women living in each zone and the proximity of CPCs to abortion facilities. Methods Using 2021 data from CPC Map and the Advancing New Standards in Reproductive Health Abortion Facility Database, we determined the ratio of CPCs to abortion facilities. Along with census data, we categorized and mapped US block groups into 4 distinct zones based on locations of block group centroids within 15-mile (1 mile is approximately 1.609 km) radii of CPCs and abortion facilities, namely “no presence,” “CPC only,” “abortion facility only,” and “dual presence.” We calculated the number and percentage of block groups and reproductive-aged (15-49 years) women living in each zone. We calculated driving distances and drive times from abortion facilities to the nearest CPC and mapped abortion facilities with CPCs in close proximity. All analyses were conducted nationally and by region, division, and state. Results Nationally, the ratio of CPCs to abortion facilities was 3.4, and 54.9% (131,410/239,462) of block groups were categorized in the “dual presence” zone, 26.6% (63,679/239,462) as “CPC only,” and 0.8% (63,679/239,462) as “abortion facility only.” Most reproductive-aged women (45,150,110/75,582,028, 59.7%) lived in a “dual presence” zone, 26.1% (19,696,572/75,582,028) in a “CPC only” zone, and 0.8% (625,403/75,582,028) in an “abortion facility only” zone. The number of block groups and women classified as living in each zone varied by region, division, and state. Nationally, the median distance from abortion facilities to the nearest CPC was 2 miles, and the median drive time was 5.5 minutes. Minimum drive times were <1 minute in all but 11 states. The percentages of abortion facilities with a CPC within 0.25, 0.5, 1, and 3 miles were 14.1% (107/757), 22.6% (171/757), 36.1% (273/757), and 66.3% (502/757), respectively. Conclusions The findings suggest that CPCs’ tactic of locating near abortion facilities was largely realized before the 2022 US Supreme Court decision that overturned the federal right to abortion. Research on CPCs’ locations and tactics should continue given the dynamic abortion policy landscape and risks posed by CPCs. Tailored programming to raise awareness about CPCs and help people identify and access safe sources of health care may mitigate harm. Increased regulation of CPCs and government divestment may also mitigate CPC harms.

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

Mapping the composite institutional dynamics risks and spatial spillover effects on environmental sustainability in 28 Asian economies

This study emphasizes the impact of economic, financial, and political institutions on environmental sustainability across 28 Asian countries from 1995 to 2022. Using a spatial Durbin model, we analyzed the direct effects of these institutional factors within each country as well as their spatial spillover effects on neighboring nations. Our findings demonstrate the interconnectedness of environmental risks and the broader regional implications. This highlights the critical need for collaborative policy initiatives at the regional level to promote environmental sustainability. The results provide important insights for policymakers and international organizations seeking to address environmental sustainability in an increasingly globalized context. By emphasizing the role of institutions and spatial interdependencies, this research offers a nuanced understanding of the drivers of emissions in Asia and the potential for coordinated action to mitigate climate change.

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 novel dynamic risk score to predict venous thromboembolism (VTE) recurrences after 3 months of anticoagulation in patients with incident VTE and without active cancer

Abstract Background Predicting the risk of recurrent venous thromboembolism (rVTE) during and following anticoagulation is complex. Current models lack the ability to adapt to changing patient conditions over time and fail to account for the direct impact of anticoagulation in real-world clinical settings. Purpose To develop a dynamic risk prediction model for rVTE to help clinicians decide on the duration of anticoagulant therapy in conjunction with a model for bleeding (also submitted to the conference). Methods UK Clinical Practice Research Datalink data (2001-2020) was used to generate a retrospective cohort with first VTE who had received 3 months of anticoagulation. Patient episodes of rVTE were evaluated. Covariates were collected at baseline and subsequently as time-varying data to capture changes post-VTE. Hazards with 95% confidence intervals (CI) were estimated and covariates included in a Fine & Gray model used as predictors of rVTE. An additive (logarithmic) scoring scheme was developed from subdistribution hazard ratios, discrimination (expressed by the C-statistic) estimated from 10-fold cross-validation. Results 51,465 patients with a first VTE were included; 4041 rVTE were identified in 200,698 person-years of observation. Incidence rates for rVTE were 2.01 per 100 person-years. Nineteen independent predictors of rVTE (recorded before or after the VTE diagnosis) were included in the model, 13 associated with increased risk and 6 with decreased risk, Table 1. Patients recognised as lower-risk, medium and higher-risk (10.5%, 37% and 52.5% of the population, respectively, at 90 days after first VTE, assuming no anticoagulation treatment) had an annualised rVTE incidence rate of 2.20, 3.84, and 7.44 per 100 person-years. The C-statistic for rVTE was 0.65 (95%CI, 0.64-0.66). The points scored can be added or subtracted to predict risk (Table 2). Conclusions Our dynamic risk score effectively identifies patients at risk of rVTE three months post their initial VTE diagnosis. It allows for continuous monitoring of risk, and modelling of treatment impact, providing clinicians with a pragmatic tool to help determine treatment duration, and adapt their strategies in response to evolving patient conditions.Table 1:Predictors for VTE recurrenceTable 2:VTE recurrence risk score

A novel dynamic risk score to predict Clinically Significant Bleeding (CSB) after 3 months of anticoagulation in patients with incident Venous ThromboEmbolism (VTE) and without active cancer

Abstract Background Predicting the risk of clinically significant bleeding (CSB), bleeding requiring hospitalisation or fatal bleeding, during and following anticoagulation is complex. Current models lack the ability to adapt to changing patient conditions over time and fail to account for the direct impact of anticoagulation in real-world clinical settings. Purpose To develop a dynamic risk prediction model for CSB to help clinicians decide on the duration of anticoagulant therapy in conjunction with a model for recurrences of VTE (also submitted to the conference). Methods UK Clinical Practice Research Datalink data (2001-2020) was used to generate a retrospective cohort with first VTE who had received 3 months of anticoagulation. Patient episodes of CSB and recurrent VTE were evaluated. Covariates were collected at baseline and subsequently as time-varying data to capture changes post-VTE. Hazards with 95% confidence intervals (CI) were estimated and covariates included in a Fine & Gray model used as predictors of CSB. An additive (logarithmic) scoring scheme was developed from subdistribution hazard ratios, discrimination (expressed by the C-statistic) estimated from 10-fold cross-validation. Results 51,621 patients with a first VTE were included; 3307 CSB were identified in 206,292 person-years of observation. Incidence rates for CSB were 1.60 per 100 person-years. 18 independent predictors of CSB (recorded before or after the VTE diagnosis) were included in the model, Table 1. Patients recognised as lower-risk, medium and higher-risk (67.5 %, 15.9 % and 16.7 % of the population, respectively, at 90 days after first VTE, assuming no anticoagulation treatment) had an annualised incidence rate of 0.84, 2.94, and 9.45 per 100 person-years. The C-statistic for CSB was 0.78 (95%CI, 0.77-0.79). The points scored can be added to predict risk (Table 2). Conclusions Our dynamic score effectively identifies patients at risk of CSB three months post their initial VTE diagnosis. It allows for continuous monitoring of risk, and modelling of treatment impact, providing clinicians with a pragmatic tool to help determine treatment duration, and adapt their strategies in response to evolving patient conditions.Table 1:Predictors for CSBTable 2:CSB risk score

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.

Time-varying cox model for heart failure prediction in phospholamban p.(Arg14del)-positive individuals

Abstract Background Phospholamban (PLN) p.(Arg14del)-positive individuals are at risk for developing severe heart failure (HF), yet a comprehensive risk model for this outcome is missing. Our PLN registry contains longitudinal data which enables us to capture dynamic changes in covariables over time, while prior prediction models rely on baseline clinical information which may change over time. Using this longitudinal clinical information will enhance the accuracy of HF prediction and may aid patients selection for future (gene)therapy. Purpose The aim of this study is to develop a dynamic HF risk prediction model for PLN p.(Arg14del)-positive individuals. Method Data were collected of 594 PLN p.(Arg14del)-positive individuals who had no documented history of HF or myocardial infarction at time of the first echocardiography. We incorporated two time-dependent covariates in our time-varying Cox regression model: longitudinal individual predicted left ventricular ejection fraction (LVEF) and longitudinal individual predicted relative risk for major ventricular arrhythmia (VA). Predicted LVEF values were derived using observed LVEF values in a linear mixed-effect model, while the relative risk for major VA was predicted using a Cox regression model with major VA as the outcome. These time-dependent covariates were included in the time-varying Cox regression along with baseline covariables age at inclusion and previous major VA. The results were validated using 5-fold cross-validation. Results Over a median follow-up period of 3.6 years (interquartile range 1.4-6.8), 77 (13%) individuals developed HF, defined as a composite endpoint of HF hospitalisation, implantation of left ventricular assist device, heart transplantation and HF-related mortality. Our time-varying Cox regression model demonstrated a robust 5-year mean C-statistic of 0.904 (95% CI 0.901-0.908). The hazard ratio for time-dependent LVEF predictions was 0.89 per % LVEF increase (95% CI 0.87-0.91, p <0.001) and for time-dependent relative major VA risk predictions 3.59 (95% CI 1.49-8.69, p = 0.005). Regarding baseline covariates, age at inclusion had a hazard ratio of 1.01 (95% CI 0.99-1.03, p = 0.47) and previous major VA 0.48 (95% CI 0.21-1.1, p = 0.08). Combining the hazard ratios of time-dependent predicted relative risk for major VA and major VA in history results in a hazard ratio of 1.73. Conclusion Our study introduces a novel time-varying Cox regression model for the individual prediction of heart failure events in PLN p.(Arg14del)-positive individuals which may aid patient selection for future (gene)therapy. Results demonstrate robust predictive performance (C-statistic of 0.904, 95% CI 0.901-0.908), highlighting the significance of incorporating longitudinal data for enhanced prediction accuracy.

Evaluation of repeated machine learning-based phenotyping in patients with cardiogenic shock

Abstract Background Cardiogenic shock (CS) is a highly mortal disease with variable clinical manifestations, severity, and in-hospital trajectories. Multiple clinical trials have failed to show an impact of therapies on outcomes, partly because they focus on etiology as a classifier, failing to capture the clinical heterogeneity. A phenotype-based approach to CS allows for a dynamic risk stratification based on disease severity. We have previously presented three CS phenotypes at baseline using machine learning-based algorithms: phenotype I (non-congested), II (cardiorenal), and III (cardiometabolic). However, little is known about the clinical course of these CS phenotypes during hospitalization and whether repeated phenotyping may provide additional insights. Purpose To assess relevance and prognostic impact of repetitive reassessment of CS phenotypes and their trajectories during the first 72h after CS diagnosis. Methods We included all-cause CS patients from the multi-center, multinational Cardiogenic Shock Working Group (CSWG) V3 and V4 registries (Yr. 2022-2023). Phenotypes were assigned by nearest centroid classification based on the centroids from the initial CSWG V1 (Yr. 2019) registry derivation cohort. Missing values required for phenotype assignment were imputed for all patients, but imputed values were only used for phenotype assignment and not reported as outcomes. Results A total of 4308 patients were included, of which 928 had CS due to myocardial infarction (MI-CS) and 2164 had CS due to heart failure (HF-CS). At time of diagnosis/ baseline, 1627 (37.8%), 1832 (42.5%), and 849 (19.7%) of patients were in phenotype I (non-congested), II (cardiorenal) and III (cardiometabolic), respectively. In MI-CS, patients were evenly distributed between the three phenotypes (35.5%, 34.4%, and 30.2%, respectively), whereas in HF-CS, non-congested and cardiorenal CS were more common than cardiometabolic shock (38.8%, 48.9%, and 12.3%, respectively). Within the first 12 hours, most patients in cardiorenal and cardiometabolic CS shifted to non-congested CS (Figure 1). In-hospital mortality in admission phenotype I, II, and III was 21.7%, 32.1%, and 50.1% (p<0.0001 for difference), respectively, in concordance with previous reports. When assessed at different time points, phenotypes II and III were associated with higher odds of mortality than phenotype I at 24h, 48h, and 72h after initial shock diagnosis (p≤0.03 for all, Figure 2). Conclusion Beyond baseline, re-assessment of machine learning-based CS phenotypes within the first 72h after CS diagnosis may provide additional prognostic insights. These data may inform future clinical trial design to tailor interventions to specific CS phenotypes.Figure 1Figure 2

Dynamic change and risk factors of intense thirst in patients admitted to neuro-intensive care unit: An observational study.

Thirst is a clinical discomfort symptom reported by most patients admitted to intensive care unit (ICU). Little is known about the dynamic change and risk factors of intense thirst in neuro-intensive care unit (Neuro-ICU). The objective of this study was to explore the dynamic change and determine the risk factors of intense thirst in patients admitted to Neuro-ICU, providing reference for personalized interventions of intense thirst. The study design is a prospective observational study. Demographic and disease-related,treatment, physiological and biochemical data were collected for 230 patients from Neuro-ICU of a hospital from May 2023 to November 2023. We assessed thirst intensity on numeric rating scale (NRS) of 0-10 (10 = worst possible thirst) at eight time points: 7:00, 9:00, 11:00, 13:00, 15:00, 17:00, 19:00 and 21:00 and a self-designed general information questionnaire based on safety protocol for thirst management to analyse the risk factors of intense thirst in Neuro-ICU patients. If thirst scores were more than 7, we defined it as intense thirst. This study followed the STROBE checklist for cross-sectional studies. A total of 230 Neuro-ICU patients were observed. The dynamic analysis results showed an overall downward trend in thirst intensity, with the highest NRS thirst scores at 07:00 (6.13 ± 2.14) and the lowest at 21:00 (4.02 ± 2.72). The investigation of the current situation showedthat the incidence of intense thirst in Neuro-ICU patients was 47.4%. Intense thirst in Neuro-ICU patients was predicted by dysphagia (odds ratio [OR] = 1.436, 95% confidence interval [CI]:1.063-1.941), open mouth breathing (OR = 2.201, 95% CI:1.041-4.656), high glucose (OR = 2.584, 95% CI:1.097-6.087), xerostomia (OR = 3.049, 95% CI:1.950-4.767) (all p < .05). The intensity of thirst was dynamically changing and the incidence of intense thirst in Neuro-ICU patients was relatively high. Timely assessment of Neuro-ICU patients' thirst severity and identification of those at high risk can ensure the implementation of effective interventions based on patients' characteristics. Thirst is a pervasive distressing symptom often reported by critically ill patients. This study revealed that the clinical nurses need to enhance their focus on dynamic change of thirst, which is helpful for improving the efficiency of bundled thirst interventions at the suitable time.

Transitions in metabolic syndrome and metabolic obesity status over time and risk of urologic cancer: A prospective cohort study.

The effects of metabolic obesity (MO) phenotypes status and their dynamic changes on urologic cancer (UC) is ignored. We aimed to investigate the association between metabolic syndrome (MetS) and MO status at baseline, their dynamic changes and UC risk. This paper studied 97,897 subjects who were free of cancers at baseline (2006-2007). Individuals were classified into four MO phenotypes by MetS and obesity at baseline. Transitions in MetS and MO status from 2006-2007 to 2008-2009 were considered. The hazard ratios (HRs) and 95% confidence intervals (CIs) for UC were assessed by multifactorial Cox proportional risk regression models. The main limitations of this study are as follows: the ratio of men to women in the cohort is unbalanced; the impacts of MetS and MO on each cancer type (kidney cancer, prostate cancer, bladder cancer) have not been analyzed separately; the transition intervals of MetS and MO phenotypes are relatively short. From baseline (2006-2007) survey to December 31, 2020, during a median follow-up of 14.02 years, 554 cases of UC were diagnosed. Participants with MetS [HRs (95% CI) = 1.26 (1.06-1.49)] and metabolically unhealthy obesity (MUO) [HRs (95% CI) = 1.49 (1.17-1.89)] had significantly higher risk of UC than those with non-MetS and metabolically healthy normal weight (MHN). Transitions in MetS and MO phenotypes over time were studied. Compared with non-MetS to non-MetS, the risks for UC in MetS to MetS [HRs (95% CI) = 1.45 (1.11-1.88)] was increased. Compared with MHN to MHN, both MUO to metabolically healthy obesity (MHO) [HRs (95% CI) = 2.65 (1.43-4.92)] and MUO to MUO [HRs (95% CI) = 1.60 (1.06-2.42)] had significantly higher UC risk. MetS and MUO increased the UC risk at baseline. Transitions of MetS to MetS, MUO to MUO and even MUO to MHO over time significantly increased the risk of UC development.

The spatial-temporal evolution patterns of landslide-oriented resilience in mountainous city: A case study of Chongqing, China

Cities, as complex systems with multi-interconnected subsystems, face significant challenges from both rapid urbanization and climate change. Ensuring high resilience in urban areas is essential for managing these dynamic risks effectively. This study introduces an innovative, data-driven approach to quantitatively analyze the spatial-temporal evolution patterns of urban resilience, validated through a case study of Chongqing, a representative mountainous city in China. Based on historical landslide data from Chongqing (2010–2020), which includes 4464 events, along with indicator data from the Chongqing Statistical Yearbook, we developed a comprehensive assessment framework. This framework incorporates 33 variables, covering indicators of physical-environmental resilience (PER) and socio-economic resilience (SER). The model integrates the Random Forest (RF) algorithm, Analytic Hierarchy Process (AHP), and Coupling Coordination Degree (CCD) model. Key findings include: (1) Social development in mountainous cities like Chongqing follows a point-to-area pattern. Although there is an overall increase in SER, the CCD in more developed areas (Chongqing urban circle) was generally higher than in less developed areas (northeastern and southeastern Chongqing) (2) The PER model demonstrated exceptional performance (AUC values consistently above 0.95). Spatiotemporal evolution models reveal that Chongqing maintains a high overall PER. Notably, from 2019 to 2020, the proportion of administrative units classified as highly resilient peaked at 24.5%, marking a historical high. (3) Multi-year average rainfall primarily impacts PER (ranked first), while Gross Domestic Product (GDP) significantly affect SER. The development of multi-dimensional recovery indicators provides a robust framework for assessing resilience against landslides in mountainous cities. The CCD model illustrates the importance of regional dynamic coordinated development in resilience trajectories. This study provides a detailed blueprint for the scientific development of resilient mountainous cities, emphasizing the need for a spatial-temporal perspective on resilience and the benefits of coordinated regional development.