Embedding MASLD Risk Stratification in Primary Care: Results from a Community Health Center Pilot

Embedding MASLD Risk Stratification in Primary Care: Results from a Community Health Center Pilot | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Embedding MASLD Risk Stratification in Primary Care: Results from a Community Health Center Pilot Ryan Wexler, Pavan Vuddanda, Panagiotis Trilianos, Arpan Mohanty This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9408563/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Metabolic dysfunction–associated steatotic liver disease (MASLD) is highly prevalent and frequently underdiagnosed in primary care, despite guideline recommendations for fibrosis risk stratification using noninvasive tests. Implementation of these strategies remains limited due to workflow, knowledge, and access barriers, particularly in safety-net settings. Methods We conducted a prospective 6-month pilot implementation study, compared with a historical control period, to evaluate whether a pragmatic, multifaceted strategy improved uptake of MASLD fibrosis risk stratification, detection of clinically significant fibrosis, and linkage to care in a community health center (CHC). Participants included adult patients at-risk for MASLD receiving care at a large, safety-net, urban CHC. The multifaceted screening initiative included (1) on-site VCTE access, (2) collaboration with a primary care provider (PCP) trained to perform and interpret VCTE, (3) development of a consensus MASLD screening pathway, and (4) pilot promotion. The primary outcome was uptake of fibrosis risk stratification (number of completed VCTEs). Secondary outcomes included the prevalence of clinically significant fibrosis (liver stiffness measurement [LSM] ≥ 8 kPa), downstream clinical actions, and performance of a FIB-4–first strategy. Results A total of 308 VCTEs were completed during the intervention period versus 58 referrals in the prior year (431% increase), with 91% technically valid studies. Clinically significant fibrosis was identified in 21% of patients. VCTE findings prompted management changes, including initiation of GLP-1 receptor agonists and hepatology referrals. Among patients with low-risk FIB-4 (< 1.3), 17% had LSM ≥ 8 kPa, indicating potential under-detection with a FIB-4–first approach. The FibroScan-AST score did not add discriminatory value beyond LSM. Conclusions A pragmatic, CHC-tailored implementation strategy substantially improved MASLD fibrosis risk stratification and enabled clinically actionable detection of advanced fibrosis. VCTE-first approaches may enhance identification of high-risk patients in safety-net populations, supporting timely intervention and linkage to care. MASLD MASH Liver fibrosis FIB-4 transient elastography implementation risk stratification Figures Figure 1 Figure 2 Introduction Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent chronic liver condition in the United States, affecting over one-third of adults and is projected to become the leading indication of liver transplantation in the coming decade. 1 Approximately 30% of individuals with MASLD develop the progressive form of metabolic dysfunction-associated steatohepatitis (MASH), which drives hepatic fibrosis. 2 Clinically significant hepatic fibrosis (histologic stage ≥ F2) is a key predictor of adverse liver outcomes, 3 cardiovascular events, 4,5 and increased mortality, 6,7 while regression of fibrosis has been associated with improved outcomes. 8 In populations at-risk for MASH, such as those with type 2 diabetes mellitus (T2DM) or overweight/obesity, clinically significant hepatic fibrosis is present in approximately 20–30% and 10% of individuals, respectively. 9 – 12 Multi-society guidelines recommend MASLD risk stratification in primary care for at-risk patients, to facilitate early identification of clinically significant fibrosis and timely intervention to prevent disease progression. 13 , 14 Despite this, MASLD remains substantially underdiagnosed in primary care, where most at-risk individuals routinely receive care. 15 – 17 This underscores the critical need to design, implement, and evaluate MASLD risk stratification pathways in real-world primary care practice. 18 – 20 Current guidance recommends a two-tiered approach to MASLD risk stratification using noninvasive tests (NITs) in primary care clinics. 14 , 21 – 23 This begins with Fibrosis-4 (FIB-4) index, a blood-based composite biomarker, chosen as the first-tier screening test due to its reliance on routinely obtained laboratory parameters and prognostic value. 24 However, emerging evidence suggests it has only modest diagnostic accuracy, with variable performance across age and racial groups. 25 , 26 Those with elevated FIB-4 proceed to confirmatory testing with liver stiffness measurement (LSM) by vibration-controlled transient elastography (VCTE, FibroScan ®, Echosens, Paris, France) or enhanced liver fibrosis test (ELF ®, Siemens Healthineers, Erlangen, Germany). Patients with elevated LSM (≥ 8 kPa) or ELF (≥ 9.8), suggestive of clinically significant liver fibrosis, should then be referred to hepatology for further evaluation. Despite this framework, successful implementation of NIT-based MASLD risk stratification in primary care remains uncommon. Barriers include limited provider knowledge, 27 poor integration of screening algorithms into existing clinical workflows, 28 and the perception that screening is either a lower clinical priority or another clinician’s responsibility. 29 In this study, we demonstrate the successful implementation of MASLD risk stratification in an urban community health center (CHC) with a federally qualified health center (FQHC) designation. We implemented a four-part strategy bundle with 1) On-site access to VCTE, 2) Collaboration with a CHC primary care provider (CHC-lead) who was trained to perform and interpret VCTE, 3) Development of a CHC-specific consensus MASLD risk stratification and linkage to care pathway, and 4) promotion through targeted presentations and email outreach. We aimed to assess whether this multifaceted approach enhanced MASLD risk stratification, increased detection of previously unrecognized clinically significant fibrosis, and facilitated timely linkage to care, leading to treatment changes based on the new diagnosis. Methods Setting This study was conducted at a large urban community health center (CHC) serving approximately 62,000 adult patients annually. The center serves a safety-net population, with 56% of patients living at or below the poverty line, 81% belonging to racial or ethnic minority groups (predominantly Hispanic), and 65% best served in a language other than English. 30 The study was conducted exclusively in the outpatient primary care clinic. Study Design We conducted a prospective 6-month evaluation of a pilot initiative to enhance MASLD risk stratification at the CHC, between October 2023 and April 2024. The study was approved by the local institutional review board. The goal was to generate pragmatic evidence to support CHC adoption and sustainment of the risk stratification pathway if it was feasible, and aligned with local workflow needs. Description of Intervention The pilot implemented a pre-defined bundle of four strategies chosen by the study team, as follows: On-site access to VCTE: Previously, CHC patients were referred to an external academic medical center for VCTE. Under the pilot program, on-site access was established using a portable FibroScan Mini + 430 device, which was transported to the CHC on scheduled scan days. This enabled point-of-care liver fibrosis assessment, reducing logistical barriers and streamlining access to diagnostic testing. Collaboration with a CHC lead primary care provider (PCP): We partnered with a PCP at the CHC (PT) who served as the local clinical lead for implementation of the MASLD risk stratification initiative. PT is an established PCP at the CHC with a clinical interest in hepatology. PT played a central role in the development of a CHC-specific MASLD risk stratification and linkage-to-care pathway (Strategy 3) and led pilot promotion efforts (Strategy 4) by engaging CHC clinicians, reinforcing pathway use during clinical care, and serving as a local champion for adoption. The CHC lead PCP completed manufacturer-recommended training to perform and interpret VCTE examinations. In addition, on-site support from an experienced VCTE technician (PV) was provided for the first 50 examinations to ensure procedural fidelity and quality assurance. Development of a CHC-specific consensus MASLD risk stratification and linkage-to-care pathway: In consensus with CHC leadership, the lead PCP developed and implemented a MASLD risk stratification protocol using VCTE as the first-line risk stratification tool, informed by contemporary literature and tailored to the CHC's specific patient population (Fig. 1 ). A single-step referral strategy using VCTE as the first-line risk stratification tool was selected due to concerns regarding the underperformance of FIB-4 in those who are Hispanic 26 or have diabetes. 31 PCPs were encouraged to refer patients for VCTE if they had hepatic steatosis, diabetes or prediabetes, BMI 30-39.9 kg/m², and ≥ 2 cardiometabolic risk factors. This recommendation was embedded in the electronic health record (EHR) Care Gaps tab, which flags guideline-based care needs that are due or overdue and links PCPs to recommended actions (e.g., orders or referrals). Clicking the MASLD risk stratification care gap opens a decision support tool that displays the algorithm (Fig. 1 ) and provides pre-populated orders for recommended laboratory evaluation, downstream testing, and referrals (Supplementary Fig. 1). VCTE was performed by the lead CHC primary care provider during scheduled visits; patients were asked to fast for at least 3 hours prior to the examination, and results were reviewed with the patient immediately after the assessment. To support interpretation, the lead PCP also ordered same-day laboratory testing (AST, ALT, and platelet count) when feasible. Risk stratification incorporated liver stiffness measurement (LSM) by VCTE and the FibroScan-AST (FAST) score. 32 The FAST (FibroScan-AST) score is a noninvasive composite score combining LSM, controlled attenuation parameter (CAP) which is a marker of hepatic steatosis, and AST to identify patients with significant fibrosis (F ≥ 2) and active steatohepatitis. Patients with LSM ≥ 8 kPa and/or FAST > 0.64 were directly linked to care in a MASLD-focused hepatology clinic led by the same provider, with follow-up appointments scheduled before the patient left the clinic. Pilot Promotion: The lead PCP actively promoted the risk stratification initiative through clinic-wide presentations, targeted email communications, and educational outreach on MASLD. Data source, collection, and analysis Quantitative data were collected prospectively over 6 months from the CHC's EHR, including demographics (age, sex, ethnicity), medical history, relevant laboratory values, LSM, and FAST™ score. For patients deemed at-risk, findings from subsequent workup including magnetic resonance elastography (MRE) and/or liver biopsy were also captured. Retrospective chart review was conducted to determine the number of patients referred externally for VCTE for MASLD risk stratification during the same six-month period in the previous year. Patients who were referred to VCTE based on non-MASLD-related indications, such as alcohol use disorder or viral hepatitis, were excluded from the current analysis. FIB-4 scores were calculated as (Age [years] × AST [U/L]) / (Platelets [10⁹/L] × √ALT [U/L]) using laboratory values closest to the VCTE date, within a one-year window. FAST™ scores were calculated using the myFibroScan® mobile application. Results are presented descriptively. Outcomes The primary outcome was uptake of MASLD fibrosis risk stratification, measured as the number of patients who completed vibration-controlled transient elastography (VCTE) during the 6-month evaluation period, compared with the number of patients referred externally for VCTE for MASLD risk stratification during the same 6-month period in the prior year. Secondary outcomes included the clinical yield of risk stratification, defined as the proportion of patients with clinically significant fibrosis (liver stiffness measurement [LSM] ≥ 8 kPa), downstream clinical actions following VCTE, including changes in management, hepatology referral, or additional diagnostic testing, and linkage to specialty care or clinical trial enrollment when indicated. An additional secondary outcome was the performance of a FIB-4–first strategy, assessed by the proportion of patients with low-risk FIB-4 (< 1.3) who nonetheless had LSM ≥ 8 kPa on VCTE, and by confirmation of significant fibrosis on subsequent testing, defined as MRE liver stiffness ≥ 3.1 kPa or histologic fibrosis stage ≥F2 on liver biopsy. We also evaluated if use of FAST score changed risk stratification outcomes. Results Uptake of MASLD fibrosis risk stratification and clinical yield The pilot intervention was associated with a substantial increase in MASLD fibrosis risk stratification compared with the prior year. Over the 6-month intervention period, 308 VCTEs were completed on-site, compared with 58 external referrals for VCTE during the same 6-month period in the previous year (431% increase). Of the 308 examinations, 281 (91%) were technically valid. The clinical yield of VCTE was substantial: 60 of 281 patients (21%) had clinically significant fibrosis (LSM ≥ 8 kPa). Baseline demographic and clinical characteristics of patients who underwent successful VCTE, stratified by clinically significant fibrosis, are shown in Table 1 . Table 1 Descriptive characteristics of patients at-risk of MASLD who underwent successful VCTE, overall and stratified by liver stiffness measurement Characteristic n available Overall (n = 281) LSM < 8 kPa (n = 221) LSM ≥ 8 kPa (n = 60) p-value Age, years 281 49 (12) 48 (11) 53 (12) 0.010 Sex, n % 281 0.07 Women 132 (47%) 105 (48%) 27 (45%) Men 149 (53%) 116 (52%) 33 (60%) Ethnicity 281 0.5 Hispanic/ Latino 218 (78%) 174 (79%) 44 (73%) Not Hispanic/ Latino 57 (20%) 43 (19%) 14 (23%) Unknown 6 (2.1%) 4 (1.8%) 2 (3.3%) Race 281 0.059 Asian 2 (0.7%) 1 (0.5%) 1 (1.7%) Black 4 (1.4%) 3 (1.4%) 1 (1.7%) Native American /Pacific Islander 4 (1.4%) 1 (0.5%) 3 (5.0%) Other/ Unknown 12 (4.3%) 9 (4.1%) 3 (5.0%) White 259 (92%) 207 (94%) 52 (87%) BMI (kg/m 2 ) 281 32.3 (4.2) 32.2 (4.2) 32.6 (4.3) 0.5 Controlled attenuation parameter (dB/m) 281 284 (52) 279 (52) 305 (47) < 0.001 Liver stiffness measurement (kPa) 281 6.7 (4.5,7.4) 5.23 (4.4,6.0) 12.07 (8.8,12.1) < 0.001 AST, U/L 278 24 (19,35) 23 (19,32) 37 (24,47) < 0.001 ALT, U/L 278 36 (23,55) 33 (22,51) 54 (36,76) < 0.001 Platelets, x 10 9 /L 274 262 (63) 266 (62) 246 (64) 0.046 Albumin, g/dL 279 4.43 (0.42) 4.43 (0.41) 4.41 (0.43) 0.8 Total bilirubin, mg/dL 274 0.5 (0.4,0.7) 0.5 (0.4, 0.6) 0.5 (0.4,0.7) 0.056 Hemoglobin A1C (%) 220 6.86 (5.28) 6.76 (5.86) 7.26 (1.81) < 0.001 Fasting glucose, mg/dL 82 100 (29) 95 (28) 113 (28) < 0.001 Triglycerides, mg/dL 253 146 (109,191) 142 (108,191) 154 (112,243) 0.23 HDL cholesterol, mg/dL 261 45 (39,53) 46 (40,54) 45 (38,52) 0.24 FIB-4 risk, n% 273 2.67) 4 (1.5%) 2 (0.9%) 2 (3.4%) Indeterminate (1.3–2.67) 35 (13%) 18 (8.4%) 17 (29%) Low Risk (< 1.3) 234 (86%) 194 (91%) 40 (68%) Fibroscan-AST (FAST) score 278 0.15 (0.06,0.36) 0.12 (0.05, 0.24) 0.46 (0.28, 0.62) < 0.001 Prediabetes/T2DM, n% 281 162 (58%) 115 (52%) 47 (78%) < 0.001 Hypertension, n% 281 110 (39%) 75 (34%) 35 (58%) < 0.001 Hyperlipidemia, n% 281 107 (38%) 72 (33%) 35 (58%) < 0.001 Footnote: Values are mean (SD), median (IQR), or n (%), as indicated; the “n available” column reflects the number of patients with non-missing data for each variable. Comparisons between LSM < 8 and LSM ≥ 8 kPa were performed using the Wilcoxon rank-sum test for continuous variables and χ² or Fisher exact tests for categorical variables. Clinically significant fibrosis was defined as LSM ≥ 8 kPa. Downstream clinical actions and linkage to care following VCTE Table 2 demonstrates that VCTE results led to changes in clinical management in both liver stiffness groups. Among patients with clinically significant fibrosis (LSM ≥ 8 kPa; n = 60), management changes were common, including initiation of GLP-1 receptor agonists in 15 patients (25%), referral to weight loss programs in 4 (7%), and referral to MASLD-focused clinical trials in 13 (22%). In patients with LSM < 8 kPa (n = 221), management changes also occurred, most frequently initiation of GLP-1 receptor agonists in 30 patients (14%) and referral to weight loss clinics in 15 (7%). Overall, these findings indicate that VCTE-informed risk stratification prompted actionable changes in care across fibrosis risk categories, with a higher intensity of downstream interventions among patients with elevated liver stiffness. Table 2 Changes in clinical management following VCTE, stratified by liver stiffness Clinical action LSM ≥ 8 kPa (n = 60) LSM < 8 kPa (n = 221) GLP-1 receptor agonist initiation 15 (25%) 30 (14%) Weight loss clinic referral 4 (7%) 15 (7%) Vitamin E initiation 1 (2%) 0 SGLT2 inhibitor initiation 0 4 (2%) Clinical trial referral 13 (22%) 0 Any change in management 19 (32%) 41 (18%) Footnote : Values are n (%). Abbreviations: GLP-1 RA, glucagon-like peptide-1 receptor agonist; LSM, liver stiffness measurement; SGLT2i, sodium–glucose cotransporter-2 inhibitor; VCTE, vibration-controlled transient elastography. Performance of FIB-4 first strategy Among the 281 patients with successful VCTE, all parameters for FIB-4 calculation were available for 273 (97%) patients (Fig. 2 ). Of these, 234 (86%) had a low-risk FIB-4 score (< 1.3). Despite low-risk FIB-4 classification, 40 of 234 patients (17%) were found to have clinically significant fibrosis on VCTE (LSM ≥ 8 kPa). Among these 40 patients, further evaluation included liver biopsy in 4 patients, confirming stage ≥F2 fibrosis in 3, and MRE in 6 patients, of whom 1 had liver stiffness ≥ 3.1 kPa. In contrast, among patients with FIB-4 ≥ 1.3 (n = 39), 19 (49%) had LSM ≥ 8 kPa, with confirmatory testing demonstrating advanced fibrosis in a subset. Overall, these findings indicate that reliance on a FIB-4–first strategy would have missed a substantial proportion of patients with clinically significant fibrosis identified by VCTE. Value of FAST Score for Risk Stratification Among the 60 patients with LSM ≥ 8 kPa, all had FAST scores ≥ 0.67. Conversely, all patients with FAST ≥ 0.67 also had LSM ≥ 8 kPa. Therefore, in this cohort, the FAST score did not provide additional discriminatory value beyond LSM for fibrosis risk stratification. Adoption and early sustainment signals Across the evaluation period, PCPs consistently referred patients into the pathway, suggesting good workflow fit and alignment with CHC needs. VCTE results were clinically actionable, with management changes and downstream referrals observed across fibrosis risk strata (Table 2 ). In addition, on-site VCTE was reimbursed through routine clinical billing, supporting the financial feasibility of ongoing implementation and pathway sustainment. Discussion As MASLD continues to emerge as a major public health challenge, there is an increasing need for practical, effective strategies to operationalize fibrosis risk stratification in real-world healthcare settings. 33 In this prospective pilot study conducted in a large, urban, community, federally qualified health center, we demonstrate that a pragmatic, bundled implementation strategy substantially increased uptake of MASLD fibrosis risk stratification in primary care and enabled clinically actionable detection of previously unrecognized liver fibrosis. By combining on-site access to vibration-controlled transient elastography (VCTE), a locally embedded primary care champion, and an EHR-enabled risk stratification and linkage-to-care pathway, the intervention resulted in a more than fourfold increase in completed fibrosis assessments compared with the prior year. Notably, one in five patients undergoing VCTE had clinically significant fibrosis, highlighting both the burden of advanced liver disease in this safety-net population and the importance of systematic risk stratification in primary care. Beyond increasing screening uptake, the intervention demonstrated meaningful clinical yield. Identification of clinically significant fibrosis was associated with downstream changes in management and linkage to specialty care across fibrosis risk strata. Patients with elevated liver stiffness were more likely to undergo intensified management, including hepatology referral and enrollment in MASLD-focused clinical trials, while patients without clinically significant fibrosis still experienced management changes centered on cardiometabolic risk reduction. Consistent with the goals of the intervention, these findings suggest that VCTE-based risk stratification generated actionable information that supported clinical decision-making within primary care workflows. This is particularly relevant given the recent approval of resmetirom 34 and semaglutide 35 for F2–F3 MASH fibrosis, both unavailable during the study period. Our findings also warrant consideration in the context of current guidance recommending a FIB-4–first approach to MASLD risk stratification. Importantly, existing recommendations represent expert guidance rather than high-grade, evidence-based guidelines, reflecting a need for pragmatic, implementable strategies in the setting of evolving evidence. In this predominantly Hispanic, safety-net population with high prevalence of diabetes, a proportion of patients classified as low risk by FIB-4 were found to have clinically significant fibrosis on VCTE, with advanced fibrosis confirmed in a small subset through further testing. Although these observations are based on limited numbers and should be interpreted cautiously, they suggest that reliance on a FIB-4–first strategy may fail to identify advanced liver disease in higher-than-average risk patients. This is particularly relevant in institutions serving Hispanics, who experience a disproportionate burden of MASLD, 36 are more likely to carry pathogenic PNPLA3 variants, 37,38 develop MASH at a significantly younger age, 39 and tend to exhibit the most aggressive histology of all ethnic groups, with their clinical course and prognosis further compounded by social determinants of health such as food insecurity. 40 In response to these contextual factors, our pathway intentionally adapted existing guidance by prioritizing VCTE as the initial risk stratification tool. This approach addressed several practical barriers encountered in routine primary care, including incomplete laboratory data needed to calculate FIB-4, variable clinician familiarity or confidence with interpreting FIB-4 results, and cognitive burden associated with multi-step algorithms. By shifting the operational responsibility for fibrosis assessment to a designated, trained PCP and embedding decision support within the EHR, the pathway reduced reliance on individual PCP knowledge of complex algorithms while preserving access to noninvasive fibrosis assessment. In addition, on-the-spot absorption of patients with abnormal VCTE results to the CHC Hepatology clinic provided a streamlined connection to specialty care, ensuring timely evaluation and management of at-risk liver disease, while relieving both patients and PCPs of the burden of scheduling additional visits for education and counseling. A central feature of this model was the role of a lead PCP who evolved into a CHC-based MASLD expert. While this individual had a prior clinical interest and training in hepatology, MASLD care is rapidly evolving, with an expanding therapeutic landscape increasingly resembling the management of chronic conditions such as diabetes or hypertension. In this context, PCPs are often well-positioned to initiate and manage therapies such as semaglutide (glucagon-like peptide 1 agonists), given their familiarity with diabetes mellitus, medication titration, and insurance authorization processes. Future care models may increasingly support PCP-led management of patients with F0-F2 fibrosis, with hepatology referral reserved for advanced fibrosis and cirrhosis, diagnostic uncertainty, or lack of treatment response. These models will require dedicated educational pathways and institutional support to enable PCPs who wish to develop and maintain expertise in MASLD care. A key strength of this study is its focus on real-world implementation in a CHC setting. Consistent PCP referral throughout the evaluation period suggests strong alignment with clinic workflows and perceived clinical value. On-site VCTE was reimbursed through routine clinical billing, addressing a common sustainability barrier and supporting long-term integration into standard CHC practice. Notably, the intervention was designed with an explicit a priori goal of adoption and sustainment, subsequently achieved through indefinite lease of VCTE equipment by the CHC and continued utilization of the risk stratification pathway to date. Limitations include a relatively short evaluation period of 6 months and a higher-than-average risk population which may limit generalizability to other populations. Conclusions Our study suggests context-specific adaptation of MASLD risk stratification, grounded in local population needs, workflow realities, and resource constraints, is feasible and can lead to increased diagnosis and treatment of MASLD and related comorbidities in community health centers. Larger, multi-site studies with longer follow-up will be needed to confirm these findings, evaluate provider and patient-centered outcomes, and define the optimal roles of primary care providers and specialists in the evolving MASLD care continuum. We hope this study serves as a practical example of effective MASLD risk stratification implementation in a primary care setting. As disease-modifying therapies continue to emerge, timely and accurate fibrosis staging at the point of first contact will be increasingly critical to ensure high-risk patients are identified and linked to treatment before irreversible disease progression occurs. Abbreviations MASLD, metabolic dysfunction–associated steatotic liver disease; MASH, metabolic dysfunction–associated steatohepatitis; CHC, community health center; FQHC, federally qualified health center; PCP, primary care provider; T2DM, type 2 diabetes mellitus; BMI, body mass index; NITs, noninvasive tests; FIB-4, Fibrosis-4 index; VCTE, vibration-controlled transient elastography; LSM, liver stiffness measurement; CAP, controlled attenuation parameter; FAST, FibroScan–aspartate aminotransferase score; AST, aspartate aminotransferase; ELF, enhanced liver fibrosis test; MRE, magnetic resonance elastography; EHR, electronic health record; GLP-1, glucagon-like peptide-1. Declarations Prior Presentation : This work was previously presented as a poster at Digestive Disease Week (DDW), May 2025, and published in abstract form in Gastroenterology (2025;160(1S):Sa1581). Ethics Approval and Consent to Participate: This study was reviewed and approved by the Boston University Medical Campus Institutional Review Board (reference number H-45628), and all procedures were conducted in accordance with the ethical standards outlined in the Declaration of Helsinki. Informed consent was obtained from all participants upon enrollment in the pilot study. Consent for Publication: Not applicable. Availability of Data and Materials: The data underlying this study consist of patient-level clinical information and are not publicly available due to privacy and confidentiality considerations. De-identified data may be made available from the corresponding author upon reasonable request and with appropriate institutional approvals. Competing Interests: Dr. Arpan Mohanty is supported by the Robert A. Winn Career Development Award (Bristol Myers Squibb Foundation) and received an investigator-initiated grant from Inventiva Pharma for this study. She serves as a site principal investigator for clinical trials funded to her institution by Madrigal, Novo Nordisk, Atea, Takeda, and Kowa, and has served as a consultant for Medpace, Novo Nordisk, and Takeda. Funding: Dr. Arpan Mohanty is supported by the Robert A. Winn Career Development Award. This investigator-initiated study was supported by Inventiva Pharma. The sponsor had no role in study design, data collection, analysis, interpretation, or manuscript preparation . Authors’ Contributions: AM conceived and led all aspects of the study, including study design, oversight, and interpretation. PT was responsible for pilot implementation at the community health center. RW and PV contributed to data collection. RW, PV, and AM performed data analysis. All authors contributed substantially to the writing and revision of the manuscript, and all authors reviewed and approved the final version. 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Diabetes Care. 2025;48(7):1057–82. 10.2337/dci24- . 0094 PubMed PMID: 40434108. Rinella ME, Neuschwander-Tetri BA, Siddiqui MS, Abdelmalek MF, Caldwell S, Barb D, et al. AASLD Practice Guidance on the clinical assessment and management of nonalcoholic fatty liver disease. Hepatol Baltim Md. 2023;77(5):1797–835. 10.1097/HEP.0000000000000323 . PubMed PMID: 36727674; PubMed Central PMCID: PMC10735173. Alexander M, Loomis AK, Fairburn-Beech J, van der Lei J, Duarte-Salles T, Prieto-Alhambra D, et al. Real-world data reveal a diagnostic gap in non-alcoholic fatty liver disease. BMC Med. 2018;16(1):130. 10.1186/s12916-018-1103-x . Nielsen EM, Anderson KP, Marsden J, Zhang J, Schreiner AD. Nonalcoholic Fatty Liver Disease Underdiagnosis in Primary Care: What Are We Missing? J Gen Intern Med. 2022;37(10):2587–90. 10.1007/s11606-021-07197-3 . PubMed PMID: 34816326; PubMed Central PMCID: PMC9360350. Budd J, Cusi K. Nonalcoholic Fatty Liver Disease: What Does the Primary Care Physician Need to Know? Am J Med. 2020;133(5):536–43. 10.1016/j.amjmed.2020.01 . .007 PubMed PMID: 32017891. Lazarus JV, Anstee QM, Hagström H, Cusi K, Cortez-Pinto H, Mark HE, et al. Defining comprehensive models of care for NAFLD. Nat Rev Gastroenterol Hepatol. 2021;18(10):717–29. 10.1038/s41575-021-00477-7 . PubMed PMID: 34172937. Lazarus JV, Mark HE, Allen AM, Arab JP, Carrieri P, Noureddin M, et al. A global research priority agenda to advance public health responses to fatty liver disease. J Hepatol. 2023;79(3):618–34. 10.1016/j.jhep.2023.04.035 . PubMed PMID: 37353401. Lazarus JV, Mark HE, Allen AM, Arab JP, Carrieri P, Noureddin M, et al. A global action agenda for turning the tide on fatty liver disease. Hepatology. 2024;79(2):502–23. 10.1097/HEP.0000000000000545 . PubMed PMID: 37540183; PubMed Central PMCID: PMC10789386. Cusi K, Isaacs S, Barb D, Basu R, Caprio S, Garvey WT, et al. American Association of Clinical Endocrinology Clinical Practice Guideline for the Diagnosis and Management of Nonalcoholic Fatty Liver Disease in Primary Care and Endocrinology Clinical Settings: Co-Sponsored by the American Association for the Study of Liver Diseases (AASLD). Endocr Pract Off J Am Coll Endocrinol Am Assoc Clin Endocrinol. 2022;28(5):528–62. 10.1016/j.eprac.2022.03.010 . PubMed PMID: 35569886. European Association for the Study of the Liver (EASL), European Association for the Study of Diabetes (EASD). European Association for the Study of Obesity (EASO). EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). J Hepatol. 2024;81(3):492–542. 10.1016/j.jhep.2024.04 . .031 PubMed PMID: 38851997. Wattacheril JJ, Abdelmalek MF, Lim JK, Sanyal AJ. AGA Clinical Practice Update on the Role of Noninvasive Biomarkers in the Evaluation and Management of Nonalcoholic Fatty Liver Disease. Expert Rev Gastroenterol. 2023;165(4):1080–8. 10.1053/j.gastro . .2023.06.013 PubMed PMID: 37542503. Vieira Barbosa J, Milligan S, Frick A, Broestl J, Younossi Z, Afdhal NH, et al. Fibrosis-4 Index as an Independent Predictor of Mortality and Liver-Related Outcomes in NAFLD. Hepatol Commun. 2022;6(4):765–79. 10.1002/hep4 . 1841 PubMed PMID: 34970870; PubMed Central PMCID: PMC8948572. van Kleef LA, Strandberg R, Pustjens J, Hammar N, Janssen HLA, Hagström H, et al. FIB-4-based Referral Pathways Have Suboptimal Accuracy to Identify Increased Liver Stiffness and Incident Advanced Liver Disease. Clin Gastroenterol Hepatol Off Clin Pract J Am Gastroenterol Assoc. 2026;24(3):733–42. 10.1016/j.cgh.2025.06 . .036 PubMed PMID: 40712713. Tincopa MA, Díaz LA, Huang DQ, Arab JP, Arrese M, Gadano A, et al. Disparities in screening and risk stratification for Hispanic adults with metabolic dysfunction-associated steatotic liver disease. Hepatology. 2025;81(6):1792–804. 10.1097/HEP.0000000000001121 . PubMed PMID: 39423341; PubMed Central PMCID: PMC12006453. Fantasia KL, Austad K, Mohanty A, Long MT, Walkey A, Drainoni ML. Safety-Net Primary Care and Endocrinology Clinicians’ Knowledge and Perspectives on Screening for Nonalcoholic Fatty Liver Disease: A Mixed-Methods Evaluation. Endocr Pract Off J Am Coll Endocrinol Am Assoc Clin Endocrinol. 2024;30(3):270–7. 10.1016/j.eprac.2023 . 12.016 PubMed PMID: 38184239. Process mapping in. healthcare: a systematic review | BMC Health Services Research | Full Text [Internet]. [cited 2025 Apr 30]. Available from: https://bmchealthservres.biomedcentral.com/articles/ 10.1186/s12913-021-06254-1 Kumar S, Mohanty A, Mantry P, Schwartz RE, Haff M, Therapondos G, et al. Deploying a metabolic dysfunction-associated steatohepatitis consensus care pathway: findings from an educational pilot in three health systems. BMC Prim Care. 2024;25(1):265. 10.1186/s12875-024-02517-y . PubMed PMID: 39033284; PubMed Central PMCID: PMC11265102. Health Center Program Uniform Data System. (UDS) Data Overview [Internet]. [cited 2026 Mar 31]. Available from: https://data.hrsa.gov/topics/healthcenters/uds/overview?grantNum=H80CS00058 Pennisi G, Enea M, Falco V, Aithal GP, Palaniyappan N, Yilmaz Y, et al. Noninvasive assessment of liver disease severity in patients with nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes. Hepatology. 2023;78(1):195–211. 10. 1097/HEP.0000000000000351 PubMed PMID: 36924031. Newsome PN, Sasso M, Deeks JJ, Paredes A, Boursier J, Chan WK, et al. FibroScan-AST (FAST) score for the non-invasive identification of patients with non-alcoholic steatohepatitis with significant activity and fibrosis: a prospective derivation and global validation study. Lancet Gastroenterol Hepatol. 2020;5(4):362–73. 10.1016/S2468-1253( . 19)30383-8 PubMed PMID: 32027858; PubMed Central PMCID: PMC7066580. Le P, Tatar M, Dasarathy S, Alkhouri N, Herman WH, Taksler GB, et al. Estimated Burden of Metabolic Dysfunction-Associated Steatotic Liver Disease in US Adults, 2020 to 2050. JAMA Netw Open. 2025;8(1):e2454707. 10.1001/jamanetworkopen.2024.54707 . PubMed PMID: 39821400; PubMed Central PMCID: PMC11742522. Harrison SA, Bedossa P, Guy CD, Schattenberg JM, Loomba R, Taub R, et al. A Phase 3, Randomized, Controlled Trial of Resmetirom in NASH with Liver Fibrosis. N Engl J Med. 2024;390(6):497–509. doi:10.1056/NEJMoa2309000 PubMed PMID: 38324483. Sanyal AJ, Newsome PN, Kliers I, Østergaard LH, Long MT, Kjær MS, et al. Phase 3 Trial of Semaglutide in Metabolic Dysfunction-Associated Steatohepatitis. N Engl J Med. 2025;392(21):2089–99. doi:10.1056/NEJMoa2413258 PubMed PMID: 40305708. Tesfai K, Pace J, El-Newihi N, Martinez ME, Tincopa MA, Loomba R. Disparities for Hispanic Adults With Metabolic Dysfunction-associated Steatotic Liver Disease in the United States: A Systematic Review and Meta-analysis. Clin Gastroenterol Hepatol Off Clin Pract J Am Gastroenterol Assoc. 2025;23(2):236–49. 10.1016/j.cgh.2024.06 . .038 PubMed PMID: 39025254. Martínez LA, Larrieta E, Kershenobich D, Torre A. The Expression of PNPLA3 Polymorphism could be the Key for Severe Liver Disease in NAFLD in Hispanic Population. Ann Hepatol. 2017;16(6):909–15. 10.5604/01.3001.0010.5282 . PubMed PMID: 29055919. Browning JD, Szczepaniak LS, Dobbins R, Nuremberg P, Horton JD, Cohen JC, et al. Prevalence of hepatic steatosis in an urban population in the United States: impact of ethnicity. Hepatology. 2004;40(6):1387–95. 10.1002/hep.20466 . PubMed PMID: 15565570. Cumpian NA, Gutierrez JA, Wu W, Saab S. Targeting MASLD and MASH in the US Hispanic/Latino Population: A Review. JAMA Intern Med. 2025;185(11):1376–86. 10.1001/jamainternmed.2025.4769 . PubMed PMID: 40982275. Maxwell SL, Price JC, Perito ER, Rosenthal P, Wojcicki JM. Food insecurity is a risk factor for metabolic dysfunction-associated steatotic liver disease in Latinx children. Pediatr Obes. 2024;19(6):e13109. 10.1111/ijpo.13109 . PubMed PMID: 38453472; PubMed Central PMCID: PMC11146202. Additional Declarations Competing interest reported. Dr. Arpan Mohanty is supported by the Robert A. Winn Career Development Award (Bristol Myers Squibb Foundation) and received an investigator-initiated grant from Inventiva Pharma for this study. She serves as a site principal investigator for clinical trials funded to her institution by Madrigal, Novo Nordisk, Atea, Takeda, and Kowa, and has served as a consultant for Medpace, Novo Nordisk, and Takeda. Supplementary Files floatimage3.png Supplementary figure 1. Electronic Health Record (EHR) Based MASLD Care Gap tool to support risk-stratification and referral Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 11 May, 2026 Reviewers agreed at journal 10 May, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 23 Apr, 2026 Editor assigned by journal 17 Apr, 2026 Editor invited by journal 17 Apr, 2026 Submission checks completed at journal 17 Apr, 2026 First submitted to journal 17 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9408563","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633587512,"identity":"71aeabd2-91f9-4e14-af76-c2b72f2923e6","order_by":0,"name":"Ryan Wexler","email":"","orcid":"","institution":"Boston University Chobanian \u0026 Avedisian School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ryan","middleName":"","lastName":"Wexler","suffix":""},{"id":633587513,"identity":"a640deea-52df-49db-bcd6-5de6130e5897","order_by":1,"name":"Pavan Vuddanda","email":"","orcid":"","institution":"Boston Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Pavan","middleName":"","lastName":"Vuddanda","suffix":""},{"id":633587514,"identity":"44ae40f0-7778-4033-86dc-6b35d305bdd7","order_by":2,"name":"Panagiotis Trilianos","email":"","orcid":"","institution":"NeighborHealth","correspondingAuthor":false,"prefix":"","firstName":"Panagiotis","middleName":"","lastName":"Trilianos","suffix":""},{"id":633587515,"identity":"9cf6262f-64d0-4d90-97b5-00033ddadfbb","order_by":3,"name":"Arpan Mohanty","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYHACNjDJL4FgE6XFgEFyBslaDG4Qq4V/2uFjD37u+JO4+XaPAcOHssOEtUjcTks37D1jkLjtzhkDxhnniNDCcDvHTIK3DajlRo4BM28bEVrkgVok/wK1bJ4B1PKXGC0GQC3SIFs2SAC1MBKjxfB2Wpq0bJux8Yw7xwoO9pxLJ6xF7nbyMcm3bXKy/bObNz74UWZNWAsMODYAiQPEqwcCe5JUj4JRMApGwcgCAKZIPBGUWtoWAAAAAElFTkSuQmCC","orcid":"","institution":"Boston Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Arpan","middleName":"","lastName":"Mohanty","suffix":""}],"badges":[],"createdAt":"2026-04-13 22:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9408563/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9408563/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108734641,"identity":"9f6768eb-5913-4704-9ec5-f34bf8fbc60b","added_by":"auto","created_at":"2026-05-07 19:54:40","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":437460,"visible":true,"origin":"","legend":"\u003cp\u003eCHC-specific MASLD risk stratification and linkage-to-care pathway\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9408563/v1/8e92984b13f09e4ab9908b42.jpeg"},{"id":108734643,"identity":"5afec8df-84ff-4e03-88cf-4b4e8149f8d9","added_by":"auto","created_at":"2026-05-07 19:54:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33115,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance of FIB-4–First Strategy Compared with VCTE-Based Risk Stratification\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9408563/v1/a8321e4c6704b77304619431.png"},{"id":108809730,"identity":"de3a41c6-9d78-40d2-99a8-aa9e152626ec","added_by":"auto","created_at":"2026-05-08 15:55:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":830243,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9408563/v1/4cc3c308-7b3f-475c-af44-914e0e82e76b.pdf"},{"id":108806798,"identity":"79790d75-6341-4f18-a3b8-788ee21972d7","added_by":"auto","created_at":"2026-05-08 15:29:30","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1082445,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary figure 1.\u003c/strong\u003e Electronic Health Record (EHR) Based MASLD Care Gap tool to support risk-stratification and referral\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9408563/v1/755c6af3c9273c70c6a776d4.png"}],"financialInterests":"Competing interest reported. Dr. Arpan Mohanty is supported by the Robert A. Winn Career Development Award (Bristol Myers Squibb Foundation) and received an investigator-initiated grant from Inventiva Pharma for this study. She serves as a site principal investigator for clinical trials funded to her institution by Madrigal, Novo Nordisk, Atea, Takeda, and Kowa, and has served as a consultant for Medpace, Novo Nordisk, and Takeda.","formattedTitle":"Embedding MASLD Risk Stratification in Primary Care: Results from a Community Health Center Pilot","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent chronic liver condition in the United States, affecting over one-third of adults and is projected to become the leading indication of liver transplantation in the coming decade.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Approximately 30% of individuals with MASLD develop the progressive form of metabolic dysfunction-associated steatohepatitis (MASH), which drives hepatic fibrosis.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Clinically significant hepatic fibrosis (histologic stage \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003eF2) is a key predictor of adverse liver outcomes,\u003csup\u003e3\u003c/sup\u003e cardiovascular events,\u003csup\u003e4,5\u003c/sup\u003e and increased mortality,\u003csup\u003e6,7\u003c/sup\u003e while regression of fibrosis has been associated with improved outcomes.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e In populations at-risk for MASH, such as those with type 2 diabetes mellitus (T2DM) or overweight/obesity, clinically significant hepatic fibrosis is present in approximately 20\u0026ndash;30% and 10% of individuals, respectively.\u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Multi-society guidelines recommend MASLD risk stratification in primary care for at-risk patients, to facilitate early identification of clinically significant fibrosis and timely intervention to prevent disease progression.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Despite this, MASLD remains substantially underdiagnosed in primary care, where most at-risk individuals routinely receive care.\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e This underscores the critical need to design, implement, and evaluate MASLD risk stratification pathways in real-world primary care practice.\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCurrent guidance recommends a two-tiered approach to MASLD risk stratification using noninvasive tests (NITs) in primary care clinics.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e This begins with Fibrosis-4 (FIB-4) index, a blood-based composite biomarker, chosen as the first-tier screening test due to its reliance on routinely obtained laboratory parameters and prognostic value.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e However, emerging evidence suggests it has only modest diagnostic accuracy, with variable performance across age and racial groups.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Those with elevated FIB-4 proceed to confirmatory testing with liver stiffness measurement (LSM) by vibration-controlled transient elastography (VCTE, FibroScan \u0026reg;, Echosens, Paris, France) or enhanced liver fibrosis test (ELF \u0026reg;, Siemens Healthineers, Erlangen, Germany). Patients with elevated LSM (\u0026ge;\u0026thinsp;8 kPa) or ELF (\u0026ge;\u0026thinsp;9.8), suggestive of clinically significant liver fibrosis, should then be referred to hepatology for further evaluation. Despite this framework, successful implementation of NIT-based MASLD risk stratification in primary care remains uncommon. Barriers include limited provider knowledge,\u003csup\u003e27\u003c/sup\u003e poor integration of screening algorithms into existing clinical workflows,\u003csup\u003e28\u003c/sup\u003e and the perception that screening is either a lower clinical priority or another clinician\u0026rsquo;s responsibility.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn this study, we demonstrate the successful implementation of MASLD risk stratification in an urban community health center (CHC) with a federally qualified health center (FQHC) designation. We implemented a four-part strategy bundle with 1) On-site access to VCTE, 2) Collaboration with a CHC primary care provider (CHC-lead) who was trained to perform and interpret VCTE, 3) Development of a CHC-specific consensus MASLD risk stratification and linkage to care pathway, and 4) promotion through targeted presentations and email outreach. We aimed to assess whether this multifaceted approach enhanced MASLD risk stratification, increased detection of previously unrecognized clinically significant fibrosis, and facilitated timely linkage to care, leading to treatment changes based on the new diagnosis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSetting\u003c/h2\u003e \u003cp\u003eThis study was conducted at a large urban community health center (CHC) serving approximately 62,000 adult patients annually. The center serves a safety-net population, with 56% of patients living at or below the poverty line, 81% belonging to racial or ethnic minority groups (predominantly Hispanic), and 65% best served in a language other than English.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e The study was conducted exclusively in the outpatient primary care clinic.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Design\u003c/h3\u003e\n\u003cp\u003eWe conducted a prospective 6-month evaluation of a pilot initiative to enhance MASLD risk stratification at the CHC, between October 2023 and April 2024. The study was approved by the local institutional review board. The goal was to generate pragmatic evidence to support CHC adoption and sustainment of the risk stratification pathway if it was feasible, and aligned with local workflow needs.\u003c/p\u003e\n\u003ch3\u003eDescription of Intervention\u003c/h3\u003e\n\u003cp\u003eThe pilot implemented a pre-defined bundle of four strategies chosen by the study team, as follows:\u003c/p\u003e \u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eOn-site access to VCTE: Previously, CHC patients were referred to an external academic medical center for VCTE. Under the pilot program, on-site access was established using a portable FibroScan Mini\u0026thinsp;+\u0026thinsp;430 device, which was transported to the CHC on scheduled scan days. This enabled point-of-care liver fibrosis assessment, reducing logistical barriers and streamlining access to diagnostic testing.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eCollaboration with a CHC lead primary care provider (PCP): We partnered with a PCP at the CHC (PT) who served as the local clinical lead for implementation of the MASLD risk stratification initiative. PT is an established PCP at the CHC with a clinical interest in hepatology. PT played a central role in the development of a CHC-specific MASLD risk stratification and linkage-to-care pathway (Strategy 3) and led pilot promotion efforts (Strategy 4) by engaging CHC clinicians, reinforcing pathway use during clinical care, and serving as a local champion for adoption. The CHC lead PCP completed manufacturer-recommended training to perform and interpret VCTE examinations. In addition, on-site support from an experienced VCTE technician (PV) was provided for the first 50 examinations to ensure procedural fidelity and quality assurance.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e Development of a CHC-specific consensus MASLD risk stratification and linkage-to-care pathway: In consensus with CHC leadership, the lead PCP developed and implemented a MASLD risk stratification protocol using VCTE as the first-line risk stratification tool, informed by contemporary literature and tailored to the CHC's specific patient population (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A single-step referral strategy using VCTE as the first-line risk stratification tool was selected due to concerns regarding the underperformance of FIB-4 in those who are Hispanic\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e or have diabetes.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e PCPs were encouraged to refer patients for VCTE if they had hepatic steatosis, diabetes or prediabetes, BMI 30-39.9 kg/m\u0026sup2;, and \u0026ge;\u0026thinsp;2 cardiometabolic risk factors. This recommendation was embedded in the electronic health record (EHR) Care Gaps tab, which flags guideline-based care needs that are due or overdue and links PCPs to recommended actions (e.g., orders or referrals). Clicking the MASLD risk stratification care gap opens a decision support tool that displays the algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and provides pre-populated orders for recommended laboratory evaluation, downstream testing, and referrals (Supplementary Fig.\u0026nbsp;1). VCTE was performed by the lead CHC primary care provider during scheduled visits; patients were asked to fast for at least 3 hours prior to the examination, and results were reviewed with the patient immediately after the assessment. To support interpretation, the lead PCP also ordered same-day laboratory testing (AST, ALT, and platelet count) when feasible. Risk stratification incorporated liver stiffness measurement (LSM) by VCTE and the FibroScan-AST (FAST) score.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e The FAST (FibroScan-AST) score is a noninvasive composite score combining LSM, controlled attenuation parameter (CAP) which is a marker of hepatic steatosis, and AST to identify patients with significant fibrosis (F\u0026thinsp;\u0026ge;\u0026thinsp;2) and active steatohepatitis. Patients with LSM\u0026thinsp;\u0026ge;\u0026thinsp;8 kPa and/or FAST\u0026thinsp;\u0026gt;\u0026thinsp;0.64 were directly linked to care in a MASLD-focused hepatology clinic led by the same provider, with follow-up appointments scheduled before the patient left the clinic.\u003c/p\u003e\u003c/li\u003e \u003cli\u003e \u003cp\u003ePilot Promotion: The lead PCP actively promoted the risk stratification initiative through clinic-wide presentations, targeted email communications, and educational outreach on MASLD.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e\n\u003ch3\u003eData source, collection, and analysis\u003c/h3\u003e\n\u003cp\u003eQuantitative data were collected prospectively over 6 months from the CHC's EHR, including demographics (age, sex, ethnicity), medical history, relevant laboratory values, LSM, and FAST\u0026trade; score. For patients deemed at-risk, findings from subsequent workup including magnetic resonance elastography (MRE) and/or liver biopsy were also captured. Retrospective chart review was conducted to determine the number of patients referred externally for VCTE for MASLD risk stratification during the same six-month period in the previous year. Patients who were referred to VCTE based on non-MASLD-related indications, such as alcohol use disorder or viral hepatitis, were excluded from the current analysis. FIB-4 scores were calculated as (Age [years] \u0026times; AST [U/L]) / (Platelets [10⁹/L] \u0026times; \u0026radic;ALT [U/L]) using laboratory values closest to the VCTE date, within a one-year window. FAST\u0026trade; scores were calculated using the myFibroScan\u0026reg; mobile application. Results are presented descriptively.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was uptake of MASLD fibrosis risk stratification, measured as the number of patients who completed vibration-controlled transient elastography (VCTE) during the 6-month evaluation period, compared with the number of patients referred externally for VCTE for MASLD risk stratification during the same 6-month period in the prior year. Secondary outcomes included the clinical yield of risk stratification, defined as the proportion of patients with clinically significant fibrosis (liver stiffness measurement [LSM]\u0026thinsp;\u0026ge;\u0026thinsp;8 kPa), downstream clinical actions following VCTE, including changes in management, hepatology referral, or additional diagnostic testing, and linkage to specialty care or clinical trial enrollment when indicated. An additional secondary outcome was the performance of a FIB-4\u0026ndash;first strategy, assessed by the proportion of patients with low-risk FIB-4 (\u0026lt;\u0026thinsp;1.3) who nonetheless had LSM\u0026thinsp;\u0026ge;\u0026thinsp;8 kPa on VCTE, and by confirmation of significant fibrosis on subsequent testing, defined as MRE liver stiffness\u0026thinsp;\u0026ge;\u0026thinsp;3.1 kPa or histologic fibrosis stage \u0026ge;F2 on liver biopsy. We also evaluated if use of FAST score changed risk stratification outcomes.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eUptake of MASLD fibrosis risk stratification and clinical yield\u003c/h2\u003e \u003cp\u003eThe pilot intervention was associated with a substantial increase in MASLD fibrosis risk stratification compared with the prior year. Over the 6-month intervention period, 308 VCTEs were completed on-site, compared with 58 external referrals for VCTE during the same 6-month period in the previous year (431% increase). Of the 308 examinations, 281 (91%) were technically valid. The clinical yield of VCTE was substantial: 60 of 281 patients (21%) had clinically significant fibrosis (LSM\u0026thinsp;\u0026ge;\u0026thinsp;8 kPa). Baseline demographic and clinical characteristics of patients who underwent successful VCTE, stratified by clinically significant fibrosis, are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive characteristics of patients at-risk of MASLD who underwent successful VCTE, overall and stratified by liver stiffness measurement\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003cp\u003eavailable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;281)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLSM\u0026thinsp;\u0026lt;\u0026thinsp;8 kPa (n\u0026thinsp;=\u0026thinsp;221)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLSM\u0026thinsp;\u0026ge;\u0026thinsp;8 kPa (n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic/ Latino\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e218 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e174 (79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Hispanic/ Latino\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative American\u003c/p\u003e \u003cp\u003e/Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther/ Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e259 (92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e207 (94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52 (87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.3 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.2 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.6 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControlled attenuation parameter (dB/m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e284 (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e279 (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e305 (47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver stiffness measurement (kPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7 (4.5,7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.23\u003c/p\u003e \u003cp\u003e(4.4,6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.07 (8.8,12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (19,35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (19,32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (24,47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (23,55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (22,51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54 (36,76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets, x 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e262 (63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e266 (62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e246 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.43 (0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.43 (0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.41 (0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 (0.4,0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003cp\u003e(0.4, 0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003cp\u003e(0.4,0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin A1C (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.86 (5.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.76 (5.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.26 (1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting glucose, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146 (109,191)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (108,191)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e154 (112,243)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL cholesterol, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (39,53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (40,54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45 (38,52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIB-4 risk, n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Risk (\u0026gt;\u0026thinsp;2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndeterminate (1.3\u0026ndash;2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (8.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Risk (\u0026lt;\u0026thinsp;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234 (86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e194 (91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibroscan-AST (FAST) score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15 (0.06,0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003cp\u003e(0.05, 0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003cp\u003e(0.28, 0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrediabetes/T2DM, n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia, n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFootnote: Values are mean (SD), median (IQR), or n (%), as indicated; the \u0026ldquo;n available\u0026rdquo; column reflects the number of patients with non-missing data for each variable. Comparisons between LSM\u0026thinsp;\u0026lt;\u0026thinsp;8 and LSM\u0026thinsp;\u0026ge;\u0026thinsp;8 kPa were performed using the Wilcoxon rank-sum test for continuous variables and χ\u0026sup2; or Fisher exact tests for categorical variables. Clinically significant fibrosis was defined as LSM\u0026thinsp;\u0026ge;\u0026thinsp;8 kPa.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDownstream clinical actions and linkage to care following VCTE\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates that VCTE results led to changes in clinical management in both liver stiffness groups. Among patients with clinically significant fibrosis (LSM\u0026thinsp;\u0026ge;\u0026thinsp;8 kPa; n\u0026thinsp;=\u0026thinsp;60), management changes were common, including initiation of GLP-1 receptor agonists in 15 patients (25%), referral to weight loss programs in 4 (7%), and referral to MASLD-focused clinical trials in 13 (22%). In patients with LSM\u0026thinsp;\u0026lt;\u0026thinsp;8 kPa (n\u0026thinsp;=\u0026thinsp;221), management changes also occurred, most frequently initiation of GLP-1 receptor agonists in 30 patients (14%) and referral to weight loss clinics in 15 (7%). Overall, these findings indicate that VCTE-informed risk stratification prompted actionable changes in care across fibrosis risk categories, with a higher intensity of downstream interventions among patients with elevated liver stiffness.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in clinical management following VCTE, stratified by liver stiffness\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical action\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLSM\u0026thinsp;\u0026ge;\u0026thinsp;8 kPa\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLSM\u0026thinsp;\u0026lt;\u0026thinsp;8 kPa\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;221)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLP-1 receptor agonist initiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight loss clinic referral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin E initiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSGLT2 inhibitor initiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical trial referral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny change in management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eFootnote\u003c/b\u003e: Values are n (%). Abbreviations: GLP-1 RA, glucagon-like peptide-1 receptor agonist; LSM, liver stiffness measurement; SGLT2i, sodium\u0026ndash;glucose cotransporter-2 inhibitor; VCTE, vibration-controlled transient elastography.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePerformance of FIB-4 first strategy\u003c/h2\u003e \u003cp\u003eAmong the 281 patients with successful VCTE, all parameters for FIB-4 calculation were available for 273 (97%) patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Of these, 234 (86%) had a low-risk FIB-4 score (\u0026lt;\u0026thinsp;1.3). Despite low-risk FIB-4 classification, 40 of 234 patients (17%) were found to have clinically significant fibrosis on VCTE (LSM\u0026thinsp;\u0026ge;\u0026thinsp;8 kPa). Among these 40 patients, further evaluation included liver biopsy in 4 patients, confirming stage \u0026ge;F2 fibrosis in 3, and MRE in 6 patients, of whom 1 had liver stiffness\u0026thinsp;\u0026ge;\u0026thinsp;3.1 kPa. In contrast, among patients with FIB-4\u0026thinsp;\u0026ge;\u0026thinsp;1.3 (n\u0026thinsp;=\u0026thinsp;39), 19 (49%) had LSM\u0026thinsp;\u0026ge;\u0026thinsp;8 kPa, with confirmatory testing demonstrating advanced fibrosis in a subset. Overall, these findings indicate that reliance on a FIB-4\u0026ndash;first strategy would have missed a substantial proportion of patients with clinically significant fibrosis identified by VCTE.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eValue of FAST Score for Risk Stratification\u003c/h2\u003e \u003cp\u003eAmong the 60 patients with LSM\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;8 kPa, all had FAST scores\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.67. Conversely, all patients with FAST\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.67 also had LSM\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;8 kPa. Therefore, in this cohort, the FAST score did not provide additional discriminatory value beyond LSM for fibrosis risk stratification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAdoption and early sustainment signals\u003c/h2\u003e \u003cp\u003eAcross the evaluation period, PCPs consistently referred patients into the pathway, suggesting good workflow fit and alignment with CHC needs. VCTE results were clinically actionable, with management changes and downstream referrals observed across fibrosis risk strata (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In addition, on-site VCTE was reimbursed through routine clinical billing, supporting the financial feasibility of ongoing implementation and pathway sustainment.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs MASLD continues to emerge as a major public health challenge, there is an increasing need for practical, effective strategies to operationalize fibrosis risk stratification in real-world healthcare settings.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e In this prospective pilot study conducted in a large, urban, community, federally qualified health center, we demonstrate that a pragmatic, bundled implementation strategy substantially increased uptake of MASLD fibrosis risk stratification in primary care and enabled clinically actionable detection of previously unrecognized liver fibrosis. By combining on-site access to vibration-controlled transient elastography (VCTE), a locally embedded primary care champion, and an EHR-enabled risk stratification and linkage-to-care pathway, the intervention resulted in a more than fourfold increase in completed fibrosis assessments compared with the prior year. Notably, one in five patients undergoing VCTE had clinically significant fibrosis, highlighting both the burden of advanced liver disease in this safety-net population and the importance of systematic risk stratification in primary care.\u003c/p\u003e \u003cp\u003eBeyond increasing screening uptake, the intervention demonstrated meaningful clinical yield. Identification of clinically significant fibrosis was associated with downstream changes in management and linkage to specialty care across fibrosis risk strata. Patients with elevated liver stiffness were more likely to undergo intensified management, including hepatology referral and enrollment in MASLD-focused clinical trials, while patients without clinically significant fibrosis still experienced management changes centered on cardiometabolic risk reduction. Consistent with the goals of the intervention, these findings suggest that VCTE-based risk stratification generated actionable information that supported clinical decision-making within primary care workflows. This is particularly relevant given the recent approval of resmetirom\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e and semaglutide\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e for F2\u0026ndash;F3 MASH fibrosis, both unavailable during the study period.\u003c/p\u003e \u003cp\u003eOur findings also warrant consideration in the context of current guidance recommending a FIB-4\u0026ndash;first approach to MASLD risk stratification. Importantly, existing recommendations represent expert guidance rather than high-grade, evidence-based guidelines, reflecting a need for pragmatic, implementable strategies in the setting of evolving evidence. In this predominantly Hispanic, safety-net population with high prevalence of diabetes, a proportion of patients classified as low risk by FIB-4 were found to have clinically significant fibrosis on VCTE, with advanced fibrosis confirmed in a small subset through further testing. Although these observations are based on limited numbers and should be interpreted cautiously, they suggest that reliance on a FIB-4\u0026ndash;first strategy may fail to identify advanced liver disease in higher-than-average risk patients. This is particularly relevant in institutions serving Hispanics, who experience a disproportionate burden of MASLD,\u003csup\u003e36\u003c/sup\u003e are more likely to carry pathogenic PNPLA3 variants,\u003csup\u003e37,38\u003c/sup\u003e develop MASH at a significantly younger age,\u003csup\u003e39\u003c/sup\u003e and tend to exhibit the most aggressive histology of all ethnic groups, with their clinical course and prognosis further compounded by social determinants of health such as food insecurity.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn response to these contextual factors, our pathway intentionally adapted existing guidance by prioritizing VCTE as the initial risk stratification tool. This approach addressed several practical barriers encountered in routine primary care, including incomplete laboratory data needed to calculate FIB-4, variable clinician familiarity or confidence with interpreting FIB-4 results, and cognitive burden associated with multi-step algorithms. By shifting the operational responsibility for fibrosis assessment to a designated, trained PCP and embedding decision support within the EHR, the pathway reduced reliance on individual PCP knowledge of complex algorithms while preserving access to noninvasive fibrosis assessment. In addition, on-the-spot absorption of patients with abnormal VCTE results to the CHC Hepatology clinic provided a streamlined connection to specialty care, ensuring timely evaluation and management of at-risk liver disease, while relieving both patients and PCPs of the burden of scheduling additional visits for education and counseling.\u003c/p\u003e \u003cp\u003eA central feature of this model was the role of a lead PCP who evolved into a CHC-based MASLD expert. While this individual had a prior clinical interest and training in hepatology, MASLD care is rapidly evolving, with an expanding therapeutic landscape increasingly resembling the management of chronic conditions such as diabetes or hypertension. In this context, PCPs are often well-positioned to initiate and manage therapies such as semaglutide (glucagon-like peptide 1 agonists), given their familiarity with diabetes mellitus, medication titration, and insurance authorization processes. Future care models may increasingly support PCP-led management of patients with F0-F2 fibrosis, with hepatology referral reserved for advanced fibrosis and cirrhosis, diagnostic uncertainty, or lack of treatment response. These models will require dedicated educational pathways and institutional support to enable PCPs who wish to develop and maintain expertise in MASLD care.\u003c/p\u003e \u003cp\u003eA key strength of this study is its focus on real-world implementation in a CHC setting. Consistent PCP referral throughout the evaluation period suggests strong alignment with clinic workflows and perceived clinical value. On-site VCTE was reimbursed through routine clinical billing, addressing a common sustainability barrier and supporting long-term integration into standard CHC practice. Notably, the intervention was designed with an explicit a priori goal of adoption and sustainment, subsequently achieved through indefinite lease of VCTE equipment by the CHC and continued utilization of the risk stratification pathway to date. Limitations include a relatively short evaluation period of 6 months and a higher-than-average risk population which may limit generalizability to other populations.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study suggests context-specific adaptation of MASLD risk stratification, grounded in local population needs, workflow realities, and resource constraints, is feasible and can lead to increased diagnosis and treatment of MASLD and related comorbidities in community health centers. Larger, multi-site studies with longer follow-up will be needed to confirm these findings, evaluate provider and patient-centered outcomes, and define the optimal roles of primary care providers and specialists in the evolving MASLD care continuum. We hope this study serves as a practical example of effective MASLD risk stratification implementation in a primary care setting. As disease-modifying therapies continue to emerge, timely and accurate fibrosis staging at the point of first contact will be increasingly critical to ensure high-risk patients are identified and linked to treatment before irreversible disease progression occurs.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eMASLD, metabolic dysfunction\u0026ndash;associated steatotic liver disease; MASH, metabolic dysfunction\u0026ndash;associated steatohepatitis; CHC, community health center; FQHC, federally qualified health center; PCP, primary care provider; T2DM, type 2 diabetes mellitus; BMI, body mass index; NITs, noninvasive tests; FIB-4, Fibrosis-4 index; VCTE, vibration-controlled transient elastography; LSM, liver stiffness measurement; CAP, controlled attenuation parameter; FAST, FibroScan\u0026ndash;aspartate aminotransferase score; AST, aspartate aminotransferase; ELF, enhanced liver fibrosis test; MRE, magnetic resonance elastography; EHR, electronic health record; GLP-1, glucagon-like peptide-1.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003ePrior Presentation\u003c/strong\u003e: This work was previously presented as a poster at Digestive Disease Week (DDW), May 2025, and published in abstract form in \u003cem\u003eGastroenterology\u003c/em\u003e (2025;160(1S):Sa1581).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate:\u003c/strong\u003e This study was reviewed and approved by the Boston University Medical Campus Institutional Review Board (reference number H-45628), and all procedures were conducted in accordance with the ethical standards outlined in the Declaration of Helsinki. Informed consent was obtained from all participants upon enrollment in the pilot study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials:\u0026nbsp;\u003c/strong\u003eThe data underlying this study consist of patient-level clinical information and are not publicly available due to privacy and confidentiality considerations. De-identified data may be made available from the corresponding author upon reasonable request and with appropriate institutional approvals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eDr. Arpan Mohanty is supported by the Robert A. Winn Career Development Award (Bristol Myers Squibb Foundation) and received an investigator-initiated grant from Inventiva Pharma for this study. She serves as a site principal investigator for clinical trials funded to her institution by Madrigal, Novo Nordisk, Atea, Takeda, and Kowa, and has served as a consultant for Medpace, Novo Nordisk, and Takeda.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eDr. Arpan Mohanty is supported by the Robert A. Winn Career Development Award. This investigator-initiated study was supported by Inventiva Pharma. The sponsor had no role in study design, data collection, analysis, interpretation, or manuscript preparation\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u003c/strong\u003e AM conceived and led all aspects of the study, including study design, oversight, and interpretation. PT was responsible for pilot implementation at the community health center. RW and PV contributed to data collection. RW, PV, and AM performed data analysis. All authors contributed substantially to the writing and revision of the manuscript, and all authors reviewed and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe authors would like to thank Celia Bora, DNP, AGNP-PC, Research Liaison for Boston HealthNet/NeighborHealth, for her invaluable support in facilitating this research at NeighborHealth.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLe P, Tatar M, Dasarathy S, Alkhouri N, Herman WH, Taksler GB, et al. Estimated Burden of Metabolic Dysfunction-Associated Steatotic Liver Disease in US Adults, 2020 to 2050. JAMA Netw Open. 2025;8(1):e2454707. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamanetworkopen.2024.54707\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2024.54707\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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JAMA Intern Med. 2025;185(11):1376\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamainternmed.2025.4769\u003c/span\u003e\u003cspan address=\"10.1001/jamainternmed.2025.4769\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PubMed PMID: 40982275.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxwell SL, Price JC, Perito ER, Rosenthal P, Wojcicki JM. Food insecurity is a risk factor for metabolic dysfunction-associated steatotic liver disease in Latinx children. Pediatr Obes. 2024;19(6):e13109. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/ijpo.13109\u003c/span\u003e\u003cspan address=\"10.1111/ijpo.13109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PubMed PMID: 38453472; PubMed Central PMCID: PMC11146202.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-primary-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"famp","sideBox":"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12875","title":"BMC Primary Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"MASLD, MASH, Liver fibrosis, FIB-4, transient elastography, implementation, risk stratification","lastPublishedDoi":"10.21203/rs.3.rs-9408563/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9408563/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003e Metabolic dysfunction\u0026ndash;associated steatotic liver disease (MASLD) is highly prevalent and frequently underdiagnosed in primary care, despite guideline recommendations for fibrosis risk stratification using noninvasive tests. Implementation of these strategies remains limited due to workflow, knowledge, and access barriers, particularly in safety-net settings.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e We conducted a prospective 6-month pilot implementation study, compared with a historical control period, to evaluate whether a pragmatic, multifaceted strategy improved uptake of MASLD fibrosis risk stratification, detection of clinically significant fibrosis, and linkage to care in a community health center (CHC). Participants included adult patients at-risk for MASLD receiving care at a large, safety-net, urban CHC. The multifaceted screening initiative included (1) on-site VCTE access, (2) collaboration with a primary care provider (PCP) trained to perform and interpret VCTE, (3) development of a consensus MASLD screening pathway, and (4) pilot promotion. The primary outcome was uptake of fibrosis risk stratification (number of completed VCTEs). Secondary outcomes included the prevalence of clinically significant fibrosis (liver stiffness measurement [LSM]\u0026thinsp;\u0026ge;\u0026thinsp;8 kPa), downstream clinical actions, and performance of a FIB-4\u0026ndash;first strategy.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 308 VCTEs were completed during the intervention period versus 58 referrals in the prior year (431% increase), with 91% technically valid studies. Clinically significant fibrosis was identified in 21% of patients. VCTE findings prompted management changes, including initiation of GLP-1 receptor agonists and hepatology referrals. Among patients with low-risk FIB-4 (\u0026lt;\u0026thinsp;1.3), 17% had LSM\u0026thinsp;\u0026ge;\u0026thinsp;8 kPa, indicating potential under-detection with a FIB-4\u0026ndash;first approach. The FibroScan-AST score did not add discriminatory value beyond LSM.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eA pragmatic, CHC-tailored implementation strategy substantially improved MASLD fibrosis risk stratification and enabled clinically actionable detection of advanced fibrosis. VCTE-first approaches may enhance identification of high-risk patients in safety-net populations, supporting timely intervention and linkage to care.\u003c/p\u003e","manuscriptTitle":"Embedding MASLD Risk Stratification in Primary Care: Results from a Community Health Center Pilot","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-07 19:54:24","doi":"10.21203/rs.3.rs-9408563/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-11T10:53:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65883117228652329853581565144514632312","date":"2026-05-11T01:23:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42381913303577676968442178977549467745","date":"2026-04-23T12:21:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-23T10:27:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-17T23:05:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-17T16:03:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-17T14:57:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Primary Care","date":"2026-04-17T12:22:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-primary-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"famp","sideBox":"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12875","title":"BMC Primary Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cfbcc064-b4c2-4ea1-8a8e-7945445fc385","owner":[],"postedDate":"May 7th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-11T10:53:16+00:00","index":57,"fulltext":""},{"type":"reviewerAgreed","content":"65883117228652329853581565144514632312","date":"2026-05-11T01:23:03+00:00","index":56,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T19:54:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-07 19:54:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9408563","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9408563","identity":"rs-9408563","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}