Risultati per: Screening per i disturbi lipidici nei bambini e negli adolescenti

Circulation, Volume 150, Issue Suppl_1, Page A4141975-A4141975, November 12, 2024. Background:Refugee populations often experience high rates of cardiovascular disease (CVD). Factors such as significant physiological stress, trauma, limited access to healthcare, substance abuse, and poor lifestyle choices contribute to disease progression and an increased incidence of cardiovascular events. We sought to evaluate the feasibility of using wearables to obtain high-fidelity ECG signals for CVD screening in refugees in Jordan.Methods:This observational cross-sectional study involved outpatients at one of four regional United Nations’ primary care clinics for Palestinian refugee in Jordan. Research assistants collected health histories from consented patients and recorded a 30-second, 6-lead ECG using a handheld, Bluetooth-enabled, wearable device (KardiaMobile 6L, AliveCor Inc., Mountain View, CA, USA). The digital ECG signals were stored on the Bluetooth-synced mobile device and then exported to a cloud server for offline analysis. The raw ECG recordings were preprocessed, and a single median beat was calculated per lead. Waveforms were segmented, and duration and amplitude measures were determined using a previously validated custom algorithm (University of Pittsburgh, PA, USA). All ECG recordings were reviewed by an independent physician.Result:The sample included 31 patients (age 52±13, 64% Females). Risk factors were prevalent in this group, including hypertension (74%), high cholesterol (65%), diabetes (64%), in-camp living (33%), and smoking (30%). Figure 1 shows the population-averaged median beat with 99% CI distribution of this sample. Mean QRS duration was 95±23 ms (range 53−150) and QTc interval was 403±53 (range 267−513). Most patients were in normal sinus rhythm (84%), and remaining patients were in atrial fibrillation or flutter (16%). Other clinically significant abnormalities included non-specific ST-T changes (9.7%), left bundle branch block (1.6%), and LVH with left ventricular strain (1.6%).Conclusion:This pilot study demonstrated that it is feasible to obtain high fidelity ECG signals using wearables to screen for CVD in refugees. Such affordable, noninvasive, point-of-care screening tools could enable early diagnosis and treatment in these patients.

Circulation, Volume 150, Issue Suppl_1, Page A4140494-A4140494, November 12, 2024. Background:Primary care after pregnancy is recommended, especially for individuals with recent adverse pregnancy outcomes (APOs, such as preeclampsia or gestational diabetes), who are at increased risk for future heart disease. Health-related social needs (HRSNs) are recognized barriers to care, yet their pregnancy-related prevalence and associations with care are unknown. We sought to (1) describe the pregnancy-related prevalence of HRSNs, and (2) assess associations between pregnancy-related HRSNs and subsequent linkage to primary care.Methods:We analyzed electronic health record data for individuals with prenatal care and delivery (2018-2021) at our urban safety-net hospital. HRSNs were assessed via a routine screener, and we summarized individual responses during pregnancy through 6 weeks post partum as: any positive, all negative, or never screened. Postpartum linkage to primary care was defined as a completed primary care visit after 6 weeks through 1 year post partum. We analyzed the prevalence of HRSNs and their associations with linkage to primary care, using adjusted log-linked binomial regression models. In stratified models we assessed for effect modification by APO history and other variables.Results:Of 4941 individuals in our sample, 53% identified as Black non-Hispanic and 21% as Hispanic, 68% were publicly insured, and 93% completed ≥1 HRSN screening. Nearly 1 in 4 screened positive for any HRSN, most often food insecurity (14%) or housing instability (12%), and 53% linked to primary care. Compared with those who screened negative for all HRSNs (n=3491), linkage to primary care was similar among those who screened positive for any HRSNs (n=1079; adjusted risk ratio, aRR 1.04, 95% confidence interval, CI: 0.98-1.10) and lower among those never screened (n=371; aRR 0.77, 95% CI: 0.68-0.86). We found no evidence of effect modification by APO history, race/ethnicity, insurance, language, or Covid-19 pandemic exposure.Conclusions:In this diverse postpartum sample, we identified a 24% prevalence of pregnancy-related HRSNs and 53% subsequent linkage to primary care. Linkage to primary care was not associated with HRSN screening result (positive versus negative) but was significantly negatively associated with being missed by HRSN screening. Further research is needed to better understand HRSN screening practices and who is missed by screening, and to identify modifiable barriers to postpartum primary care especially after APOs.

Circulation, Volume 150, Issue Suppl_1, Page A4145524-A4145524, November 12, 2024. Introduction:White-coat hypertension (WCH) and masked hypertension (MH) complicate accurate blood pressure (BP) monitoring. While ambulatory BP monitoring (ABPM) is effective, its high cost and limited availability are significant barriers.Hypothesis:We hypothesized that a machine learning (ML) model using clinical data from a single outpatient visit could accurately predict WCH and MH.Aims:This study aimed to develop and validate ML-based prediction models for WCH and MH using accessible clinical data to improve diagnostic efficiency and accessibility.Methods:We enrolled patients from two hypertension cohorts, after excluding those with incomplete data. Patients were classified by office BP and ABPM readings per American Heart Association guidelines. ML models, including Multi-layer Perceptron （MLP）, Support Vector Machine (SVM), and Tabular Prior-Data Fitted Network (Tab-PFN), were developed. Input parameters included demographic data (age, gender, height, weight, smoker), and office BP (OBP) and heart rate measurements. Principal Component Analysis (PCA), kernel PCA (kPCA), or t-distributed stochastic neighbor embedding (t-SNE) were used to improve class separability.Results:The study population comprised 1481 participants with a mean age of 47.6 years (SD 13.6), 65% of whom were male and 20.1% were smokers. OBP measurements showed a mean systolic BP (SBP) of 128.7 mmHg (SD 15.4) and a mean diastolic BP (DBP) of 84.2 mmHg (SD 11.6). ABPM showed a mean 24-hour systolic BP of 122.5 mmHg (SD 11.8) and diastolic BP of 79.3 mmHg (SD 10.1). The inclusion of demographic and OBP data, along with advanced resampling and dimensionality reduction techniques, significantly improved the model’s predictive ability. The final TabPFN model achieved the best performance with recall, precision, F1 score, and accuracy of 0.747, 0.931, 0.829, and 0.807 for WCH, and 0.713, 0.954, 0.816, and 0.907 for MH.Conclusion:Our ML-based model effectively predicts WCH and MH using accessible clinical data, offering a cost-effective alternative before applying ABPM.

Circulation, Volume 150, Issue Suppl_1, Page A4124675-A4124675, November 12, 2024. Introduction:The prevalence of hypertrophic cardiomyopathy (HCM) in the UK Biobank based on ICD-10 codes (.07%) is lower than global estimates of disease prevalence (0.2 – 0.5%). Prior studies using this data have remarked on the limitations of findings given likely underdiagnosis. The availability of cardiac MRI scans on a fraction of the participants offers an opportunity to identify missed diagnoses.Aims:This study seeks to utilize a generalizable deep learning model to detect likely cases of undiagnosed hypertrophic cardiomyopathy from cardiac MRIs in the UK Biobank.Methods:The foundational model was trained on a multi-institutional dataset of 14,073 cardiac MRIs via a self-supervised contrastive learning approach that sought to minimize the divergence between scans and their associated radiology reports. The pre-trained model was fine-tuned to diagnose hypertrophic cardiomyopathy on a distinct cohort of 4,870 MRIs with 368 cases of HCM, achieving an AUC of 0.94. The fine-tuned model was applied to the UK Biobank cardiac MRI dataset to ascertain predicted probabilities of HCM. Cases exceeding a threshold of 95% – correlating to the top 0.5% of cases (expected specificity of 97% and sensitivity of 60%) – were screened in for manual reading. In a blinded fashion, a board-certified radiologist was tasked with diagnosing HCM on a sample of cases composed of high and low predicted probabilities.Results:Of the 43,017 patients with cardiac MRIs, only 9 (.02%) had an ICD diagnosis of HCM. 266 cardiac MRIs were manually reviewed: 216 had greater than 95% predicted probability of HCM; 50 negative controls were randomly selected amongst cases with predicted probability less than 10%. The radiologist concurred with an HCM diagnosis for 115 cases (sensitivity 53%, specificity 98%), 112 of which were previously undiagnosed. The prevalence of hypertension and aortic stenosis did not significantly differ between the cohort of true positives (69.2%) and false positives (76.6%). The corrected prevalence of HCM in the UK BioBank MRI cohort is estimated at 0.28%.Conclusions:The findings of this study illustrate the remarkable ability of a generalizable deep learning model to detect undiagnosed cases of a rare disease process from cardiac MRIs. This is an important milestone that may allow for widespread screening of hypertrophic cardiomyopathy while minimizing demand for radiologist labor, and thereby allow patients to reap the substantial benefits of earlier treatment.

Circulation, Volume 150, Issue Suppl_1, Page A4143847-A4143847, November 12, 2024. Introduction:Our previous studies have shown that sustained activation of the DNA damage response (DDR) in cardiomyocytes leads to p53/p21 activation and cardiac dysfunction. Although the DDR generally involves molecules in DNA replication and repair pathways, the non-proliferative nature of cardiomyocytes suggests a cardio-specific DDR mechanism. However, our understanding of DDR in cardiomyocytes remains limited. Here, we aim to use CRISPR interference (CRISPRi) knockdown screens to identify genes critically involved in DDR regulation in human cardiomyocytes. We hypothesize that identifying these gene clusters may allow us to develop methods to prevent cardiac dysfunction by suppressing DDR in cardiomyocytes.Methods and Results:We established a human iPS cell line stably expressing dCas9-KRAB, which allows CRISPRi-mediated gene knockdown, and differentiated the cells into cardiomyocytes. The resulting human iPS cell-derived cardiomyocytes (hiPSCMs) showed the achievement of approximately 80% knockdown efficiency after gRNA transfection. We stimulated the hiPSCMs with H2O2and quantitatively evaluated the expression levels of the DDR markers γH2AX and p21 by immunostaining using the Operetta®high content imaging system. The DDR markers showed a significant concentration-dependent increase in response to H2O2administration. For arrayed CRISPRi screening, we constructed a gRNA library targeting 437 DDR-related genes. Using this library, we knocked down each DDR-related gene in hiPSCMs followed by H2O2stimulation. We quantified the expression levels of DDR markers by calculating the fluorescence intensity ratios relative to control after gene knockdown, and standardized them to calculate Z scores for all 437 genes. The screening successfully revealed the differential impact of each gene knockdown on γH2AX and p21 expression. We identified 71 genes that significantly affected their expression (Z-score < -1 or > 1). Mapping these genes to DDR pathways highlighted the differential impact of gene knockdown within the same pathway, and stratified their importance in cardiomyocytes.Conclusions:Arrayed CRISPR screening using hiPSCMs revealed differential functional significance of DDR-related genes in cardiomyocytes, identifying 71 genes of particularly significant importance. These findings provide a critical understanding of the cardio-specific DDR pathway and important clues for establishing an appropriate method to suppress DDR in the failing heart.

Circulation, Volume 150, Issue Suppl_1, Page A4142502-A4142502, November 12, 2024. Background:AI-ECG is a cost-effective tool for left ventricular dysfunction (LVD) screening. However, its cost-effectiveness for other forms of structural heart disease (SHD) is unknown. While AI-ECG is inexpensive, a drawback is low positive predictive value (PPV), which leads to high costs from unnecessary follow-up tests. Therefore, strategies to improve the yield of AI-ECG-based screening are needed.Aim:To evaluate the cost savings of a stepwise approach to SHD screening with AI-ECG followed by POCUS compared to AI-ECG alone.Methods:286 adult outpatients undergoing AI-ECG were selected at random. Participants received same-day POCUS and had a recent TTE (our gold standard for SHD). We evaluated four SHDs: aortic stenosis (AS), cardiac amyloidosis (CA), HCM, and LVD. The costs of AI-ECG ($75) and TTE ($1,305) were obtained from Healthcare Bluebook. The cost of POCUS ($100) was estimated independently. Cost savings were analyzed for simultaneous screening for all forms of SHD and screening for individual SHDs.Results:AI-ECG identified potential SHD in 125 patients, but only 39 were true positives by TTE (31% PPV). In AI-ECG positive patients, POCUS demonstrated findings of SHD in 52/125. Compared to TTE, this stepwise approach yielded 32 true positives and 20 false positives (62% PPV). The cost per patient diagnosed with SHD was $4,733 with AI-ECG alone but decreased to $3,182 with stepwise screening (33% cost savings). Screening for individual SHDs resulted in cost reduction from $18,724 to $6,315 (66% savings) for AS, $21,023 to $12,230 (42% savings) for CA, $9,883 to $6,175 (38% savings) for HCM, and $4,019 to $3,582 (11% savings) for LVD.Conclusions:Stepwise screening for SHD with AI-ECG followed by POCUS significantly reduces costs compared to AI-ECG alone. We also suggest a model for parallel screening for multiple SHDs, which is likely more cost-effective than screening for individual SHDs.

Circulation, Volume 150, Issue Suppl_1, Page A4143538-A4143538, November 12, 2024. Introduction:Transthyretin amyloid cardiomyopathy (ATTR-CM) is an underdiagnosed disease that may result in heart failure (HF), arrhythmias, and valvular disease. Our aim was to develop (1) screening criteria to identify high-risk patients for ATTR-CM and (2) our own predictive tool of ATTR-CM.Methods:This was a prospective observational registry at 2 academic sites in Canada. We designed screening criteria to identify high-risk patients in HF, atrial fibrillation, transcatheter valve clinics, and in cardiologist’s offices from January 2019-December 2022. Patients >60 years were included if one of several screening criteria was met and they were referred for pyrophosphate scan by the cardiologist. Univariate and multivariate logistic regression were used to identify predictive clinical, imaging, and biochemical characteristics.Results:In total, 2500 patients were screened, and 200 patients were enrolled with a follow-up duration of 3 years. The mean age was 78 years and 65% were male. Forty-six (23%) had a diagnosis of ATTR-CM and 7 (4%) were diagnosed with AL-amyloidosis. ATTR-CM patients were older (83±7 vs. 77±8; p

Circulation, Volume 150, Issue Suppl_1, Page A4131622-A4131622, November 12, 2024. Introduction:Chronic liver disease affects more than 1.5 billion adults worldwide, but the majority of cases are asymptomatic and undiagnosed. Echocardiography is broadly performed and visualizes the liver; however, this information is not diagnostically leveraged.Hypothesis and Aims:We hypothesized that a deep-learning algorithm can detect chronic liver diseases using subcostal echocardiography images that contains hepatic tissue. To develop and evaluate a deep learning algorithm on subcostal echocardiography videos to enable opportunistic screening for chronic liver disease.Methods:We identified adult patients who received echocardiography and abdominal imaging (either abdominal ultrasound or abdominal magnetic resonance imaging) with ≤30 days between tests. A convolutional neural network pipeline was developed to predict the presence of cirrhosis or steatotic liver disease (SLD) using echocardiogram images. The model performance was evaluated in a held-out test dataset, dataset in which diagnosis was made by magnetic resonance imaging, and external dataset.Results:A total of 2,083,932 echocardiography videos (51,608 studies) from Cedars-Sinai Medical Center (CSMC) were used to develop EchoNet-Liver, an automated pipeline that identifies high quality subcostal images from echocardiogram studies and detects presence of cirrhosis or SLD. In a total of 11,419 quality-controlled subcostal videos from 4,849 patients, a chronic liver disease detection model was able to detect the presence of cirrhosis with an AUC of 0.837 (0.789 – 0.880) and SLD with an AUC of 0.799 (0.758 – 0.837). In a separate test cohort with paired abdominal MRIs, cirrhosis was detected with an AUC of 0.726 (0.659-0.790) compared to MR elastography and SLD was detected with an AUC of 0.704 (0.689-0.718). In the external test cohort of 66 patients (n = 130 videos), the model detected cirrhosis with an AUC of 0.830 (0.738 – 0.909) and SLD with an AUC of 0.768 (0.652 – 0.875).Conclusions:Deep learning assessment of clinically indicated echocardiography enables opportunistic screening of SLD and cirrhosis. Application of this algorithm may identify patients who may benefit from further diagnostic testing and treatment for hepatic disease.

Circulation, Volume 150, Issue Suppl_1, Page A4147292-A4147292, November 12, 2024. Background:Tissue hypoxia and chronic anemia associated with sickle cell disease (SCD) leads to structural and physiological alterations in the heart. Early detection of heart failure (HF) in patients with SCD can assist with timely interventions, but current methods (e.g., echocardiogram and heart MRI) are not easily accessible in resource-deprived settings. The integration of artificial intelligence (AI)-powered tools utilizing low-cost ECG data to increase the power to detect more patients eligible for early treatment, thus improving patient outcomes, and needs to be validated.Hypothesis:We hypothesize that ECG-AI models developed to detect incident HF in the general population can detect HF in SCD patients.Methods/Approach:We previously developed an ECG-AI model employing convolutional neural networks to classify patients with HF using a large ECG-repository at Wake Forest Baptist Health (WFBH). This model was developed using 1,078,198 digital ECGs from 165,243 patients, 73% White, 19% Black, and 52% female individuals, with a mean age (SD) of 58 (15) years. The hold-out AUC of this previous model in distinguishing ECGs of HF patients from controls was 0.87. In this study, we externally validated this ECG-AI model using SCD patients’ data from the University of Tennessee Health Science Center (UTHSC). Additionally, a logistic regression (LR) model was constructed in the UTHSC cohort by incorporating other simple demographic variables with the outcome of ECG-AI model.Results/Data:The UTHSC external validation cohort included data from 2,107 SCD patients (188 HF and 1,919 SCD patients with no HF), 98% were Black, 72% were female, with a mean age of 39 (14) years. Despite demographic differences between the validation (more Blacks) and derivation cohorts (lower age), our ECG-AI model accurately identified HF with an AUC of 0.80 (0.77-0.82) in the UTHSC SCD cohort. When incorporating ECG-AI outcome (an ECG-based risk value between 0 and 1), age, sex, and race in a LR model, the AUC significantly improved (DeLong Test, p

In nove anni utenti raddoppiati, in lieve calo i suicidi

Introduction
The prevalence of mental health problems is increasing worldwide, particularly in the vulnerable group of older people. The limited availability of therapists, long wait periods and increasing shortage of healthcare resources limit adequate care. As a result, digital applications are becoming more commonplace as an alternative to human therapists. However, these tend to be used by younger people with higher education, digital health literacy and experience. In Germany, applications that are approved by the health authorities, so-called digital health applications (DiGAs), can be prescribed by physicians and psychotherapists. It remains unclear to the extent older people are experienced with, are willing and can use a DiGA. Therefore, this research aims to identify specific challenges of older people’s accessibility, user experience and engagement with DiGA for depressive disorders. The DiGA4Aged project consists of: (1) a pilot study on usability, (2) a screening study on potential participants for a randomised controlled trial (RCT) evaluating the digital experience of the target population and (3) an RCT to test the effectiveness of a digital nurse as individualised user support in the intervention group. This paper focuses on the pilot study and the screening study.

Methods and analysis
The instrumental components in preparing for the RCT are a mixed-method pilot and observational quantitative screening study, which are described in this manuscript. The pilot study includes questionnaires (covering sociodemographic data, user experience, health literacy, electronic health literacy, media affinity, severity of depression and perceived usability of DiGA), a concurrent think aloud method and a semistructured interview to evaluate two applications with regard to their usability for, acceptance by and needs of older people. The observational screening study collects data of older patients consecutively admitted to an acute care geriatric hospital ward using various questionnaires to identify which clinical and medical factors are associated with the access to, experience with and (non-)use of digital media. Data from the comprehensive geriatric assessment is collected as well as data on their digital media experience and digital health literacy.

Ethics and dissemination
The overall project DiGA4Aged received ethical approval on 17 November 2023 from the ethics committee of the Medical Faculty of Ruhr-University Bochum (registration number 23-7901). Results will be disseminated within the scientific community via publication in peer-reviewed journals as well as presentation at national conferences. The findings from the pilot study and the observational screening study will determine the selection of the DiGA and the recruitment strategy for the subsequent RCT.

Trial registration numbers
The pilot study has been prospectively registered in the German Clinical Trials Register (DRKS00033640, registered on 18 March 2024, available from https://drks.de/search/de/trial/DRKS00033640). Likewise, the observational screening study has been prospectively registered in the German Clinical Trials Register (DRKS00032931, registered on 29 November 2023, available from https://drks.de/search/de/trial/DRKS00032931).

Objectives
To identify and map the available evidence for whether a test of handgrip strength (HGS) and/or the chair stand test (CST) have been used as screening tools for the detection of sarcopenia in elderly individuals within primary care settings.

Design
This study was designed as a scoping review, in accordance with the methodological framework for scoping reviews, developed by Arksey and O’Malley, and using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews.

Data sources
A literature search was conducted to identify relevant articles listed in PubMed and Scopus databases up to 16 October 2023.

Eligibility criteria
Studies including individuals aged ≥60 years who had undergone assessments of HGS and/or the CST in primary care settings were included.

Data extraction and synthesis
Following the article selection process, based on predetermined criteria for inclusion and exclusion, the selected articles were analysed regarding population demographics, the setting in which the research was conducted, the study design, used diagnostic tools and reported results.

Results
The search yielded 282 unique articles, of which 7 were included in the final analysis. All seven included articles had a cross-sectional study design, whereof one also had a longitudinal 3-year follow-up. The number of participants ranged from 75 to 719. Three of the articles used the diagnostic criteria presented by European Working Group on Sarcopenia in Older People (EWGSOP), two used the criteria by Asian Working Group for Sarcopenia 2019, one used the EWGSOP2 criteria and one applied both the EWGSOP and EWGSOP2 criteria to their data. All the articles used HGS to assess muscle strength. CST was not used for this purpose in any of the articles, although it was used to estimate physical performance or as part of the short physical performance battery. The prevalence of sarcopenia in the included articles was 12.0%–20.7%, while the prevalence of possible sarcopenia was 69.9%–73.3% and that of probable sarcopenia was 25.5%–94%.

Conclusion
None of the included articles aimed to study a test of HGS and/or the CST as screening tools for the detection of sarcopenia. However, four of the articles diagnosed possible or probable sarcopenia by using a test of HGS and/or the CST among elderly patients in a primary care setting. There is a need for more research to elucidate whether a test of HGS and/or the CST might be used for screening of sarcopenia in primary care.

Background
Child maltreatment (CM) encompasses physical, emotional or sexual abuse, physical or emotional/psychological neglect or intimate partner (or domestic) violence and is associated with adverse cognitive, behavioural, physical and social outcomes that often continue shaping adulthood. The early and valid detection of CM is essential to initiate treatment and intervention as well as to avoid continued violence against the child. Various occupational groups, such as healthcare providers, teachers, social workers, psychotherapists and others, encounter maltreated children in their professional settings. Systematic reviews on instruments to assess suspected CM often report on retrospective measurement via caregiver’s or child’s self-report and are frequently limited to the health system as a setting. The purpose of this Preferred Reporting Items for Systematic Reviews and Meta-Analyses-compliant systematic review is to synthesise the evidence on psychometric properties of instruments to assess suspected CM at the presentation to a broad range of different occupational groups who work with children inside and outside the healthcare system.

Method
A systematic search will be performed in Scopus, PsycInfo, Medline and Web of Science with no limit on the earliest publication until January 2022. Eligibility criteria include studies that investigate psychometric properties of instruments to assess suspected CM in children and adolescents under 18 years by a professional proxy. After the independent screening of studies by two reviewers, quality assessment and data extraction will be performed using an adaptation of the COnsensus-based Standards for the selection of health Measurement INstruments Risk of Bias checklist, Strengthening the Reporting of Observational Studies in Epidemiology: Explanation and Elaboration report and Downs and Black checklist for measuring study quality. Screening, quality assessment and data extraction will be done using Covidence. The results will be presented in narrative form and, if adequate, a meta-analysis will be performed.

Discussion
This review aims to give an overview of the psychometric properties of different instruments designed to screen suspected CM by professional proxies. The results will be of interest to different occupational groups who need information about methodological quality and characteristics of instruments to make decisions about the best-suited tool for a specific purpose. Furthermore, the results of this review will support the development of novel instruments and might improve the existing ones.

Ethics and dissemination
Ethics approval will not be required. The results of this systematic review will be submitted for publication in a peer-reviewed journal.

PROSPERO registration number
CRD42022297997.

Objective
To evaluate the cost-effectiveness of risk-stratified hepatocellular carcinoma (HCC) screening in diabetic patients with metabolic dysfunction-associated steatotic liver disease (MASLD).

Design
A state-transition model from a healthcare payer perspective on a lifetime horizon.

Setting
Japan.

Population
A hypothetical cohort of 50-year-old diabetic patients with MASLD risk-stratified according to degree of obesity and progression to cirrhosis. Metabolic dysfunction-associated steatotic liver (MASL), metabolic dysfunction-associated steatohepatitis (MASH) and MASH cirrhosis are progressive manifestations of this specific type of liver disease.

Intervention
Abdominal ultrasound (US), US with alpha-fetoprotein (AFP), US with AFP and lectin-reactive alpha-fetoprotein (AFP-L3), CT, extracellular contrast-media-enhanced MRI (ECCM-MRI), gadoxetic acid-enhanced MRI (EOB-MRI) and no screening.

Main outcome measure
Costs, quality-adjusted life-years (QALYs), incremental cost-effectiveness ratios (ICERs), early-stage HCC cases, advanced-stage HCC cases and HCC-related deaths.

Results
EOB-MRI is the most cost-effective screening method for non-obese diabetic patients with MASH cirrhosis and for obese diabetic patients with MASH and MASH cirrhosis. Cost-effectiveness was sensitive to HCC incidence in non-obese diabetic patients with MASH cirrhosis and obese diabetic patients with MASH, and the adherence rate of HCC screening in obese diabetic patients with MASH. When the semiannual HCC incidence was between 0.008 and 0.0138 in non-obese diabetic patients with MASH cirrhosis, US with AFP was more cost-effective than EOB-MRI. Cost-effectiveness acceptability curves showed that EOB-MRI was 50.7%, 96.0% and 99.9% cost-effective in obese diabetic patients with MASH and non-obese diabetic patients with MASH cirrhosis, and obese diabetic patients with MASH cirrhosis at a willingness-to-pay level of $50 000 per QALY gained. Compared with no screening in 100 000 non-obese diabetic patients with MASH cirrhosis and obese diabetic patients with MASH cirrhosis, EOB-MRI reduced total costs by US$69 million and by US$142 million, increased lifetime effectiveness by 12 546 QALYs and by 15 815 QALYs, detected 17 873 and 21 014 early-stage HCC cases, and averted 2068 and 2471 HCC-related deaths, respectively.

Conclusions
Of all HCC screening methods for diabetic patients with MASH cirrhosis, EOB-MRI yields the greatest cost-saving with the highest QALYs, detects the greatest number of early-stage HCC cases and averts the greatest number of advanced-stage HCC cases and HCC-related deaths. The findings provide important insights for the precise implementation of risk-stratified HCC surveillance to reduce morbidity and mortality and improve the quality of life in diabetic patients with MASLD.

Presentato alla Camera il policy brief di Europa Donna Italia

Universal screening of syphilis in emergency departments (EDs) could increase case detection, a recent investigation in Open Forum Infectious Diseases suggests.