Search Results for: AGA: linee guida sulla de-prescrizione degli inibitori della pompa protonica

Stroke, Volume 56, Issue Suppl_1, Page AWP152-AWP152, February 1, 2025. Background:Structural racism and disparities between rural and urban healthcare systems significantly impact stroke care delivery in the United States. This study explores the interaction between structural racism, urbanity, and the administration of acute ischemic stroke (AIS) interventions—specifically, intravenous thrombolysis (TPA) and endovascular thrombectomy (ET).Methods:This retrospective analysis utilized complete, de-identified inpatient Medicare data from January 1, 2016, to December 31, 2019. We included Medicare beneficiaries aged ≥65 years with incident AIS admissions in large metropolitan and non-urban settings. Structural racism was assessed using county-level validated metrics, including segregation, housing, employment, education, and income indices, and a composite structural racism score. We used multilevel logistic models adjusted for age, sex, and race (Black vs. White) to estimate the odds ratios (ORs) for TPA and ET receipt, with data clustered at the county level.Results:Among 951,914 AIS patients, those treated in rural hospitals had lower ICU capacity (27.5% vs. 88.6%), stroke certification (5.3% vs. 38.4%), and lower rates of TPA (1.6% vs. 12.3%) and ET (

Stroke, Volume 56, Issue Suppl_1, Page ATP213-ATP213, February 1, 2025. Introduction:Dolichoectasia (DE), a condition marked by dilation and tortuosity of intracranial arteries, is related to an increased risk of strokes. While the association between DE and ischemic stroke is well established, the relationship with hemorrhagic stroke remains less clear. We investigated the presence of dolichoectasia in patients with intracerebral hemorrhage (ICH) and examine its impact on clinical outcomes.Methods:We studied adult spontaneous ICH A retrospective study of patients aged consecutively admitted to an academic medical center between 2012 and 2023 with available hospitalization vascular imaging (CTA and/or MRA). DE was primarily assessed as a composite assessment based on artery arterial diameter cutoffs by either CTA or MRA imaging modality seen either in anterior or posterior circulations. Multivariable logistic regression models assessed DE’s association with poor discharge ICH outcomes (modified Rankin Scale 4-6) after adjusting for relevant covariates. Separate models explored relationships of DE with ICH characteristics (volume, location), and exploratory analyses were performed defining DE based on vascular location.Results:Of 373 of 860 patients met the inclusion criteria for meeting criteria for analyses, 56.1% were male, mean age was 66, and 29.8% were white. DE was present in 20% of the cohort. Patients with DE were more likely older, white patients. In our regression models, we identified that DE was associated with decreased odds of poor discharge ICH outcomes (adjusted OR 0.49, 95%CI: 0.26-0.92,p=0.03). In exploratory analyses, we did not identify relationships of DE (as a composite assessment) with ICH volume. However, when assessing DE based on anatomical location, we identified that posterior circulation DE was associated with deep ICH location (95%CI: 1.04-4.98, p=0.04). No interactions of age, sex, race, with DE’s association with outcomes were seen.Conclusion:We identified a high prevalence of DE in our cohort of ICH patients. Furthermore, we identified that the presence of DE was associated with decreased odds of poor ICH outcomes. Further work is warranted to clarify the role of this vasculopathy in ICH pathogenesis and outcomes, especially to identify if flow-related changes mediate the observed improved outcomes.

Stroke, Volume 56, Issue Suppl_1, Page ATP273-ATP273, February 1, 2025. Introduction:Patients suffering from transient ischemic attack (TIA) are at high risk of ischemic stroke (IS). This study describes clinical characteristics and outcomes in patients with a first non-cardioembolic TIA.Methods:Using two US administrative claims databases (MarketScan and Optum’s de-identified Clinformatics® Data Mart Database [CDM]) converted to the Observational Medical Outcomes Partnership (OMOP) common data model, we conducted an observational, retrospective cohort study of adults with a first diagnosis of non-cardioembolic TIA between 2012 and 2022. Demographic and clinical characteristics were described at baseline, and incidence rates of IS, intracranial bleeding, and bleeding leading to hospitalization with sensitivity analyses at different time points were calculated.Results:Overall, 203,757 patients were included in the study, 97,481 from MarketScan, 106,276 from CDM. Mean age was 62 years in MarketScan and 72 years in CDM. Patients were mostly women (57.6% in MarketScan, 59.3% in CDM). At baseline, prevalence of comorbidities was high (hypertension 66% and 84%, hyperlipidemia 53% and 75%, coronary artery disease 18% and 31%, diabetes 25% and 38% in MarketScan and CDM, respectively). Median follow-up time was 569 days in MarketScan and 716 days in CDM. At 1 year follow-up, incidence rates per 100 person-years of IS, intracranial bleeding, and bleeding leading to hospitalization were 10.9, 0.9, and 4.2, respectively, in MarketScan and 20.2, 1.6, and 7.6 respectively, in CDM. Sensitivity analyses showed that most IS events occurred within 7 days of the index event. Additional event rates and sensitivity analyses are shown in Table 1.Conclusion:Results from two US claims databases show that the annual risk of IS is higher than expected following a first TIA diagnosis, especially when including the first 7 days in the ascertainment. Implementation of guideline directed antiplatelet therapies, or new antithrombotic strategies, is needed.

Stroke, Volume 56, Issue Suppl_1, Page ADP50-ADP50, February 1, 2025. Background:Local angiotensin activity is thought to play a critical role in arterial wall homeostasis and remodeling, which contributes to the pathogenesis of subarachnoid hemorrhage (SAH). Here we aimed to assess the association between pharmacologic inhibition of angiotensin-converting enzyme and subsequent non-traumatic SAHMethods:In a retrospective cohort study based on Optum’s Clinformatics® Datamart de-identified Database records (2000-2021), patients with hypertension were included. We collected medication history and assessed the risk of non-traumatic subarachnoid hemorrhage (SAH) associated with angiotensin-converting enzyme inhibitors or angiotensin receptor blockers (ACEI/ARB). Cox proportional hazard regression models were used to compare the time to SAH by type of antihypertensive treatment. Analyses were adjusted for baseline demographic and clinical characteristicsResults:7.5 million patients with hypertension were assessed. Patients on ACEI/ARBs with or without other antihypertensives (n=4.8 million,follow-up:6.3 years, average age 61.9, 50.4% female) had lower rates of SAH compared to those on alternative antihypertensive regimens (n=1.3 million, follow-up:5.7 years,average age 61.9, 60.3%female) [HR:0.94(0.91-0.97), p

Stroke, Volume 56, Issue Suppl_1, Page ATP225-ATP225, February 1, 2025. Background:Dolichoectasia (DE) is characterized by the abnormal dilation and elongation of brain arteries, leading to high morbidity and mortality, commonly presenting as ischemic stroke. However, the mechanisms underlying brain ischemia in DE are not well understood. Here, we explore the relationship between basilar artery (BA) diameters and blood flow velocities using transcranial doppler (TCD) in stroke patients to evaluate whether TCD is a viable method for evaluating the hemodynamic impact of DE.Methods:Using a cross-sectional design, we analyzed consecutive ischemic stroke patients admitted to a comprehensive stroke center from October 2017 to January 2018, who had completed both intracranial arterial imaging (CTA or MRA) and TCD during admission. Diameter of the BA was measured using semiautomatic vessel segmentation, and DE was defined as a BA diameter greater than the 95th percentile stratified by sex. TCD-derived blood flow velocities were recorded through the transforaminal window (depth 80-100mm). Linear regression models adjusted for age and sex were used to assess the association between BA diameter and averaged mean flow velocity (MFV). Subgroup analysis excluding cases with BA stenosis and fetal posterior cerebral artery (fPCA) was also performed.Results:Among 211 ischemic stroke patients, 44 had vessel imaging and TCD. The mean age was 64 ± 13 years, 66% were male, 36% had posterior circulation stroke, 11.4% had BA DE, 21% had BA stenosis, and 16% and 11% had unilateral and bilateral fPCA, respectively. The mean BA diameter was 3.7 ± 2.1 mm, and the mean averaged MFV was 40 ± 21 cm/s. A weak negative correlation was found between BA diameter and MFV (r = -0.37, p = 0.01). The linear regression model adjusted for age and sex showed an independent negative correlation (β per mm = -3.33 [95% CI -6.2 to -0.5]). In the subgroup without BA stenosis and fPCA (n = 25), the negative correlation between BA diameter and MFV was stronger (r = -0.65, p < 0.01), with β per mm of -4.44 (95% CI -7.0 to -1.9; figure).Conclusion:In ischemic stroke patients, larger BA diameters correlated with reduced TCD-derived blood flow velocities, particularly after excluding cases with fPCA and BA stenosis. Reduced blood flow velocities can lead to blood stagnation, thromboembolism, and hypoperfusion, suggesting that TCD may be useful for grading DE severity and assessing stroke risk.

Stroke, Volume 56, Issue Suppl_1, Page ATP209-ATP209, February 1, 2025. Introduction:Data sharing and analyses of neuroimaging data can be time-consuming and often represent a rate-limiting factor in clinical trial research. This workflow, which involves downloading imaging data from a clinical trial’s electronic data capture (EDC) system, performing biomarker analyses, and then re-entering data into the EDC, can be cumbersome when performed manually. Integrating the EDC’s application programming interface (API) with cloud-computing processes can significantly reduce the time required for these tasks. The objectives of this study aimed to evaluate whether an automated workflow using cloud computing can reduce the processing time while maintaining accuracy for biomarker evaluation compared to manual methods.Methods:We compared the time required for manual imaging data downloads, biomarker analyses, and data entry into a clinical trial’s EDC with that required for the same processes using an automated method. Additionally, we assessed the accuracy of automated biomarker analyses relative to semi-automated analyses performed manually by an expert reader, specifically focusing on the volumetric quantification of intracerebral hemorrhage (ICH).Results:The manual process, involving downloading, de-identification, and semi-automated volumetric quantification of ICH, took an average of 12 hours and 57 minutes per CT uploaded to the EDC. Cloud computing completed the same tasks in an average of 8 minutes and 13 seconds. The cloud-based biomarker analysis demonstrated high accuracy, with an average ICH volume difference of -1.29mL (n=214; p=0.0001) compared to the semi-automated method.Conclusion:In conclusion, the integration of the EDC’s API with cloud-computing processes for imaging ingestion and analysis reduces processing time of neuroimaging data in clinical trials. Additionally, the automated workflow maintained a high level of accuracy in the volumetric quantification of ICH. Although it may take a human reader 30 minutes to process neuroimaging data, limitations can delay these tasks. Multiple scans could be uploaded at once, delaying the time it takes a reader to analyze the images. Imaging data could be uploaded after workhours or on the weekend, postponing the analyses until the next working day. Yet, the automated cloud-based workflow drastically reduces processing time and ensures high accuracy of ICH volumetric calculations, offering a more efficient alternative to traditional manual methods.

Stroke, Volume 56, Issue Suppl_1, Page ADP30-ADP30, February 1, 2025. Background&Objectives:It remains unclear why unilateral proximal carotid artery occlusion (UCAO) causes benign oligemia, without progressing to cerebral infarction, in mice, yet leads to a wide variety of outcomes (asymptomatic-to-death) in humans. We hypothesized that inhibition of nitric oxide synthase (NOS) both transforms UCAO-mediated oligemia into full infarction and expands preexisting infarction.Methods:Using 900 mice, we i) investigated stroke-related effects of a single intraperitoneal dose of the NOS inhibitor Nω-nitro-L-arginine methyl ester (L-NAME, 400 mg/kg) + UCAO; ii) examined the rescue effect of the NO donor, molsidomine (200 mg/kg, at 30 minutes); iii) tested the impact of antiplatelet medications; iv) queried if UCAO without L-NAME administration could induce infarction when mice had hyperglycemia and hyperlipidemia; and v) measured blood levels of endogenous NOS inhibitors (asymmetric and symmetric dimethylarginines; ADMA and SDMA, respectively). Furthermore, we conducted i) a multi-center study (n=438 UCAO patients) to identify predictors of infarct volume and ii) Mendelian randomization analysis to estimate the causal effect of the endogenous NOS inhibitors on human ischemic stroke.Results:UCAO alone induced infarction rarely (~2%) or occasionally (~14%) in C57BL/6 and BALB/c mice, respectively. However, L-NAME+UCAO induced large-arterial infarction in ~75% of C57BL/6 and BALB/c mice. Laser speckle imaging for 6 hours detected spreading ischemia in ~40% of C57BL/6 and BALB/c mice with infarction (vs. none without), as assessed at 24 hours. In agreement with vasoconstriction and microthrombus formation shown by intravital microscopy, molsidomine and the endothelial NOS-activating antiplatelet cilostazol attenuated or prevented progression to infarction. Moreover, UCAO without L-NAME caused infarction in ~22% C57BL/6 and ~31% ApoE knock-out mice with hyperglycemia and hyperlipidemia, which, in turn, associated with ~60% greater SDMA levels. Further, increased levels of glucose and cholesterol associated with significantly larger infarct volumes in UCAO patients. Lastly, Mendelian randomization identified a causative role of NOS inhibition, particularly in elevated SDMA concentration, in ischemic stroke risk (OR=1.24; 95% CI, 1.11–1.38;P=7.69×10-5).Conclusions:NOS activity is a key factor determining the fate of hypoperfused brain following acute carotid occlusion or clamping, where SDMA could be a potential risk predictor.

Stroke, Volume 56, Issue Suppl_1, Page A134-A134, February 1, 2025. Introduction:Pediatric stroke research is hindered by lack of funding and relative disease rarity. Shared data in pediatric stroke is done via non-reimbursed data input by clinical investigators at participating children’s hospitals with the International Pediatric Stroke Study (IPSS). Large Language Models (LLM) can potentially reduce investigator workload through automated data entry. In prior research, investigators were able to achieve 94% accuracy while using a prompt engineering approach with Generative Pretrained Transformer 4 (GPT4) to enter subject outcome forms of the IPSS using clinical notes. However, GPT4 performed only moderately (~50% correct) while attempting to answer some of the data questions. In this study we aim to utilize another toolkit called the “Instructor” to improve the performance of the LLM in areas where the prior method achieved less than 90% accuracy.Methods:This retrospective study used de-identified clinical notes of 50 patients who presented to UTHealth Pediatric Stroke Clinic between January 2020 and July 2023 with ischemic stroke. Each note was run through the offline HIPAA compliant LLM “GPT4o” to answer questions in the outcome form of IPSS. We focused on areas of the IPSS outcome form where prior approach yielded less than 90% accuracy. We implemented the “Instructor”, a Python library built on Pydantic, to enhance prompt engineering and ensure structured outputs. Accuracy was measured as percent agreement between the LLM generated and investigator-entered data. We used simple descriptive statistics to compare the accuracy (% correct) of Instructor method with clinical investigator-entered data and previously reported results from traditional prompt engineering method.Results:We analyzed neurological deficit severity and post discharge rehabilitation questions. This algorithm reported 100% accuracy for both neurological deficit severity and post discharge rehabilitation as compared to accuracy with the previous method (46-54% and 26-62% respectively).Conclusion:In this study, utilization of the “Instructor” shows promising results for reliable data retrieval. Moving forward, we will use Instructor to analyze the neurological deficit type, follow-up imaging type and findings based on imaging, and expand this approach to other sections of the IPSS forms. LLMs may reduce investigator workload and increase the efficiency of observational research for rare, underserved diseases like pediatric stroke in the future.

Stroke, Volume 56, Issue Suppl_1, Page ATP86-ATP86, February 1, 2025. Background:Artificial intelligence (AI) stroke imaging software is becoming mainstay in many hub-and-spoke hospitals. Brainomix 360 Stroke software is the market leader in Europe focused on leveraging simple imaging (non-contrast computerized topography (NCCT) and CT angiography (CTA)) and has recently been FDA-cleared for use in the USA. We evaluated the implementation of Brainomix 360 AI Stroke software in the 17 spoke, multi-state Mayo Clinic Health System (MCHS) telestroke network.Methods:This prospective study compared decision and treatment times before and after Brainomix AI implementation, as well as clinician feedback and simulated decision making to better understand any changes seen. Patients were included if they underwent telestroke evaluation at an MCHS emergency department within 90 days of implementation (2/9/2024-8/8/2024), and excluded if they were already admitted to the hospital or if video evaluation was not performed. Data collection included demographics, clinical decisions and treatment times. Qualitative surveys were conducted at baseline and evaluation end, as well as simulated decision making in 20 de-identified cases randomized to with/without AI through an online portal.Results:A total of 907 patients were included (444 pre- and 463 post-implementation, 287 (32%) with ischemic stroke final diagnosis). Median NIHSS was 2. IVT was recommended in 20.3% (27/148 ischemic stroke patients) pre- and 25.9% (36/139) post-implementation. EVT was recommended in 16.2% (24/148) pre and 14.4% (20/139) post. AI use was associated with trends of faster telestroke activation to IVT decision (36 vs 32 mins, p=0.6), IVT administration (47 vs 40 minutes, p=0.6), and EVT decision (36 vs 33 minutes, p=0.5). In the simulation, imaging interpretation was significantly faster when randomized to AI use (3.4 vs 2.1 mins, p

Stroke, Volume 56, Issue Suppl_1, Page AWP215-AWP215, February 1, 2025. Background:FDA clearance of fully automated artificial intelligence (AI)-based software for quantifying intracerebral hemorrhage (ICH) volumes has the potential to meaningfully impact the acute management of hemorrhagic stroke. ICH volume is a critical prognostic factor, with larger hemorrhages associated with oral anticoagulant (OAC) use typically resulting in poorer outcomes. Quantifying ICH volume in OAC-related ICH presents challenges due to variability of morphology and density. Although prior studies suggest AI models may improve the accuracy of ICH volume calculation compared to the ABC/2 method and Semi-Autonomous Segmentation (SAS), there is limited data evaluating their performance in OAC-related ICH.Methods:A retrospective analysis was conducted on 161 adults presenting to a comprehensive stroke center from 2016-2021 with acute supratentorial OAC-related ICH. Volumes on initial de-identified CT brain were measured using SAS (Syngo.Via, Siemens) and AI (Viz ICH volume, Viz.ai). Agreement of ICH volumes between SAS and AI was assessed using the Intraclass Correlation Coefficient (ICC) for absolute agreement. A two-way mixed effects model was employed for single measurements. A Bland-Altman (BA) analysis with proportional bias assessment was performed. Data analysis was conducted using R studio.Results:Out of 161 adults, 50 met eligibility criteria and 39 (78%) CT scans were analyzed by AI and SAS. AI software failed to correctly process 11 scans due to small ICH volumes (n=8), misclassification of lesions as subdural (n=1), or image retrieval issues (n=2). For the 39 scans that were analyzed, the median ICH volume measured by SAS was 15.89 cm3(IQR 5.69 – 41.86 cm3) and by AI was 17.0 cm3(IQR 5.0 – 44.5 cm3). The ICC for absolute agreement between the software platforms was 0.973 (95% CI 0.950 – 0.986), indicating excellent reliability. A BA plot revealed a mean difference (bias) of -0.861 cm3(95% CI -3.1 – 1.37 cm3) with limits of agreement from -14.84 cm3to 13.12 cm3, demonstrating good agreement between the two methods with no significant proportional bias.Conclusions:There is strong agreement and reliability in OAC-related ICH volume measurements between SAS and AI. Such local validation is imperative for safe and responsible integration of AI tools into clinical workflows. Further research into limitations of AI, including failure modes and biases is necessary to inform human oversight.

Stroke, Volume 56, Issue Suppl_1, Page AWP269-AWP269, February 1, 2025. Introduction:All-cause dementia remains a significant public health concern, with stroke recognized as a key risk factor. Few studies have applied Machine Learning (ML) models to accurately predict cognitive impairment and dementia, yet none have specifically focused on post-stroke dementia risk prediction. This study aims to compare the efficacy of ML approaches and traditional biostatistical methods for predicting the incidence of one-year post-stroke all-cause dementia using electronic health record (EHR) data.Methods:We analyzed de-identified data extracted from the TriNetX network, covering 60 healthcare organizations. This study included patients aged 20+ who experienced their first stroke (any type) in 2018 (baseline). We excluded those with dementia history, lacking data 3 years after stroke onset, or without relevant health data within 3 years preceding stroke. We developed four models: Logistic regression (LR) with backward selection, regularized LR (LASSO and Ridge regression), and Random Forest (RF). The primary outcome was the incidence of all-cause dementia within one year post-stroke. Covariates included demographics, comorbidities, medications, laboratory measures, and vital signs. Model performance was evaluated using accuracy and the area under the curve (AUC) of the receiver operating characteristic (ROC).Results:The final cohort comprised 55,888 adults, of whom 8% developed all-cause dementia within the subsequent year. The sample was 48.4% female, with a distribution of 8.7% aged 20-44, 37.2% aged 45-64, and 54.0% aged 65+. About 64% were non-Hispanic Whites. Among those who developed dementia, 49.7% were female and 80.5% were 65+. They had slightly higher systolic blood pressure, lower BMI, higher rates of comorbidities, and medication use (Table 1). Performance metrics for the models were as follows: LR with backward selection (accuracy: 92.07%; AUC: 0.8033), LASSO regression (92.09%;0.8000), Ridge regression (92.04%; 0.8026), and RF (92.20%; 0.7828) (Table 2).Conclusion:This study demonstrated the feasibility of using ML models to accurately predict post-stroke all-cause dementia incidence. All models showed high accuracy and robust discriminative ability, with the RF model achieving the best accuracy and traditional LR displaying the highest AUC. ML approaches can effectively learn from the data to identify individuals at higher risk of post-stroke dementia, potentially enabling targeted interventions and improved patient care.

Introduction
Migrant women in transit face high risk of developing mental health problems such as depression and anxiety, driven by gendered social-structural factors including violence, social isolation, migration uncertainty, limited access to services and gender inequities. Although migrant women who endure such conditions have high need for mental health prevention, few evidence-based interventions are tailored to this population. Moreover, while women and children’s mental health are interconnected, few mental health interventions address parenting needs. The aim of this study is to pilot-test a novel parenting and mental health prevention intervention for migrant mothers with young children (MMC) in Tijuana, Mexico, including (a) assessing acceptability; (b) estimating effect sizes on symptoms of depression, anxiety, and parenting stress; (c) identifying which theory-based mechanisms of action predict changes in outcomes; and (d) identifying factors associated with differential intervention response.

Methods and analysis
‘Mamá Empoderada’ (Mom Power) is a theory-based, trauma-informed group intervention to promote mental health and responsive parenting among mothers with young children (0–5 years). This is an evidence-based intervention that has been previously evaluated in the USA and has been recently adapted for Spanish-speaking mothers. We have recently adapted this intervention for MMC in Mexico and will conduct a pilot randomised controlled trial (RCT) of the intervention with MMC (n=100; Ntreatment=50; Ncontrol=50). The intervention group (IG) will receive 10 group and three individual sessions addressing attachment-based parenting skills, linkage to resources (eg, food, shelter), social support, and self-care and resilience over a 5-week period. The control group will receive standard of care programming and will be offered participation in the intervention following completion. Both groups will complete baseline and exit surveys, as well as follow-up surveys at 2, 4 and 6 months postintervention. Statistical analyses will compare primary (ie, symptoms of depression and anxiety; parenting stress) and intermediate outcomes (eg, resilience, service utilisation) by exposure to intervention condition.

Ethics and dissemination
This study is approved by the San Diego State University and El Colegio de la Frontera Norte Institutional Research Boards. Findings will inform a larger trial to evaluate intervention efficacy. In collaboration with our community partners, results will be disseminated via peer-reviewed publications; presentations; and plain-language reports, infographics, and presentations to community, clinical, and policy audiences. If efficacious, this intervention is highly promising as a novel, low-cost, and feasible model that could be implemented in border settings in Mexico, the USA and elsewhere. Amid rising population displacement and prolonged and traumatic migration journeys, this study addresses an urgent need for scalable and tailored mental health prevention for MMC in border contexts.

Trial registration number
NCT06468046.

Objective
To assess the prevalence and trends of chronic kidney disease (CKD) in Western Australia (WA) from 2010 to 2020 using linked pathology data.

Design
A retrospective observational cohort study using linked de-identified data from WA pathology providers, hospital morbidity records and mortality records.

Setting
A Western Australian population-based study.

Participants
All individuals aged 18 years and older with at least one serum creatinine test.

Primary outcome measure
CKD status as determined by estimated glomerular filtration rate and urine albumin-creatinine ratio.

Results
Analysing data from 2 501 188 individuals, there was a significant increase in age-sex standardised CKD prevalence from 4.7% in 2010 to 6.0% in 2020, with annual average percentage change of 3.0% (95% CI: 2.3% to 3.7%). Prevalence of CKD stages 3 and above was 4.8% in 2020. Higher CKD prevalence was observed in regional and remote areas compared with major cities, and among individuals in the most socioeconomically disadvantaged quintiles. Sensitivity analysis indicated minor impacts from data exclusions and methodological choices.

Conclusions
CKD prevalence in WA has been steadily increasing, reflecting broader Australian trends. The study highlights significant disparities in CKD prevalence based on age, socioeconomic status and geographic remoteness.

Hepatitis B reactivation (HBVr) can occur due to a variety of immune-modulating exposures, including multiple drug classes and disease states. Antiviral prophylaxis can be effective in mitigating the risk of HBVr. In select cases, clinical monitoring without antiviral prophylaxis is sufficient for managing the risk of HBVr. This clinical practice guideline update aims to inform frontline health care practitioners by providing evidence-based practice recommendation for the management of HBVr in at-risk individuals.