APOSCREEN-1 – a prospective implementation study for pharmacy-based screening for cardiovascular-kidney-metabolic risk factors in Schleswig-Holstein

APOSCREEN-1 – a prospective implementation study for pharmacy-based screening for cardiovascular-kidney-metabolic risk factors in Schleswig-Holstein | 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 Study protocol APOSCREEN-1 – a prospective implementation study for pharmacy-based screening for cardiovascular-kidney-metabolic risk factors in Schleswig-Holstein Eric Amelunxen, Amelie Kokot, Benedikt Kolbrink, Sarah-Yasmin Thomsen, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9039113/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 15 You are reading this latest preprint version Abstract Background: Cardiovascular-kidney-metabolic (CKM) syndrome has emerged as a major global health burden and driver of cardiovascular disease (CVD), the leading cause of death worldwide. Effective management of CKM depends on timely identification of underlying risk factors. Nevertheless, participation rates in primary care-based screenings are low. Consequently, CKM syndrome oftentimes remains undetected until organ damage is clinically present. Community pharmacies offer an accessible, yet underused, setting to enhance early detection. APOSCREEN-1 evaluates the feasibility and diagnostic yield of a pharmacy-based multi-parametric screening for cardiovascular-kidney-metabolic health. Methods: APOSCREEN-1 is a prospective implementation study conducted in 20 community pharmacies in the German state of Schleswig-Holstein. Adults (n = 1000) aged ≥ 40 years with predefined risk criteria are included. Participants undergo standardized point-of-care testing (glycated hemoglobin, lipid profile, urinary albumin, blood pressure). Additionally, clinical history is assessed and results are transmitted to the study center via an online platform. Patients meeting pre-defined thresholds of the tested parameters are followed up by confirmatory laboratory testing at the study center or at the participants’ general practitioner. Primary outcomes include completion rate, implementation metrics from the pharmacy perspective, and the number needed to screen to detect unknown or insufficiently managed cardiometabolic risk factors. Secondary outcomes comprise participant metrics, diagnostic metrics of the screening, evaluation of clinical impact. Discussion: This study addresses the unmet need for scalable prevention of CVD by identification of CKM syndrome risk factors outside traditional primary care settings. Evidence on feasibility, acceptance, and diagnostic benefit may support the use of community pharmacies as an additional access point for early CKM syndrome detection. Future interventional studies will be required to evaluate structured follow-up pathways and long-term effectiveness. Trial registration This study was registered with the German trial registry (Deutsches Register klinischer Studien) on 29.01.2026, under trial number DRKS00039149. prevention cardiovascular risk chronic kidney disease diabetes hypertension point-of-care testing community pharmacy public health Figures Figure 1 Background Cardiovascular disease (CVD) remains the leading cause of death worldwide ( 1 ). Its development is driven predominantly by cardiovascular risk factors, including obesity, smoking, arterial hypertension, diabetes mellitus, and hypercholesterolemia. Chronic kidney disease (CKD) further amplifies cardiovascular risk and represents an independent risk factor. The interplay and combination of these factors has been defined as cardiovascular-kidney-metabolic (CKM) syndrome ( 2 , 3 ). The prevalence of the CKM syndrome is rising substantially ( 4 ). In industrialized countries, 6–13% of the population are affected by advanced CKM syndrome stages involving organ damage, which corresponds to an estimated 5–11 million individuals in Germany. In the long-term, CKM syndrome shortens life expectancy, reduces social or occupational participation and is a major public health burden ( 5 , 6 ). Germany exemplifies this trend: despite the highest healthcare expenditure in the EU (11,7% of GDP in 2023), life expectancy remains below average due to CVD ( 7 , 8 ). Currently ranked only 17th out of 27 EU countries, Germany’s average life expectancy of 81.1 years has fallen below the EU average since 2010. This discrepancy suggests that Germany’s healthcare system fails to convert spending into better health outcomes. Early identification of CKM syndrome is essential to avoid organ damage ( 9 , 10 ). In Germany, a nationwide preventive health check-up program exists to facilitate early detection of CKM syndrome and its contributing conditions. However, despite the long-standing availability of such programs, participation rates remain low with only 50% of the German population participating regularly ( 11 ). Therefore, a large proportion of individuals remain undiagnosed until clinical manifestation occurs: approximately 49–84% of CKD cases, 20% of arterial hypertension, 22% of diabetes mellitus, and more than 50% of dyslipidemia cases remain undetected in the general population ( 12 – 14 ). This underscores the urgent need for more effective and accessible screening strategies. The approximately 16.800 German community pharmacies represent a trusted healthcare resource for the public, and are frequently visited with 64% of the population visiting at least once per month ( 15 ). Pharmacists are highly trained healthcare professionals that may support the healthcare system with clinical services but remain an underutilized resource in this regard. The APOSCREEN-1 concept, developed in collaboration with the Pharmacists’ Association of Schleswig-Holstein, aims to address this critical gap in secondary prevention by implementing a low-threshold, risk-based, and multi-parametric screening approach in community pharmacies. Methods/Design Study aim and design The APOSCREEN-1 study is designed as a prospective implementation study, conducted in community pharmacies located in the federal state of Schleswig-Holstein, Germany. The study aims to assess the feasibility and diagnostic yield of a multi-parametric, pharmacy-based, risk-adapted point-of-care (PoC) screening for CKM syndrome risk factors. It follows a two-step approach: first, a pragmatic point-of-care screening in pharmacies is conducted to triage patients with high cardiovascular risk who may benefit strongly from further diagnostic work-up. In a second step, these patients undergo detailed confirmatory testing and receive guideline-based therapeutic recommendations. The study takes place in the outpatient setting and is conducted in cooperation with the Pharmacists’ Association of Schleswig-Holstein. Ethical approval has been obtained from the local institutional review board (AZ D616/25). Primary outcomes The study will assess three primary outcomes: Participant-level feasibility is assessed by screening completion rates of participants. The outcome is defined as completion of the structured questionnaire, performance of blood pressure measurement and PoC-testing for dyslipidemia and HbA1c. The study aims for an overall completion rate ≥ 70% based on the reported use of primary care screenings and prospective feasibility on the national level in Germany. Implementation outcomes from the pharmacy perspective are evaluated for the implementation domains acceptability, feasibility, and sustainability. The outcome is evaluated using validated instruments, including the Acceptability of Intervention Measure (AIM), the Feasibility of Intervention Measure (FIM), and the Normalization Measure Development Questionnaire (NoMad) to assess sustainability (Supplementary Table 1 and NoMad Questionnaire) ( 16 – 19 ). These instruments use standardized self-administered questionnaires completed by pharmacy staff that are rated on Likert scales. Validated German versions will be used. For each domain, the outcome criterion is defined as a median Likert-scale score ≥ 3, with the corresponding null hypothesis that this threshold is met for all implementation domains, indicating sufficient implementation performance under routine practice conditions. The medical screening yield is quantified by the number needed to screen (NNS) to identify at least one previously unknown or insufficiently managed CKM syndrome risk factor. A NNS ≤ 5 will be considered as indicative of a clinically meaningful detection efficiency of the multi-parametric screening approach. Exploratory outcomes Exploratory outcomes comprise rates of GP consultation and medication changes, diagnostic metrics of multiparametric testing and individual test categories. Diagnostic metrics are assessed including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, workload and screening metrics will be assessed (Supplementary Table 2). Inclusion and exclusion criteria Eligible participants are adults aged 40 years or older who visit a participating pharmacy and meet at least one of the criteria summarized in Table 1 . Recruitment will start on 1 April 2026 and is scheduled to run for 6 months. Participating pharmacies will be instructed to aim for an approximately equal distribution of male and female participants. Table 1 Inclusion and exclusion criteria Inclusion criteria Exclusion criteria Age ≥ 40 and Participation in another study Established regular GP and Inability to give informed consent Active smoking or Prescription of antihypertensive, antidiabetic, or lipid-lowering medication or Obesity (BMI ≥ 30kg/m²; waist circumference ≥ 88cm for women or ≥ 102cm for men) Procedures, data source and collection Participant selection Pharmacy customers will either be approached during a regular visit or contacted in advance, if they have authorized their pharmacy to inform them of health improvement projects. Potential participants based on the identification of risk medications will be identified via the pharmacy’s dispensing software. These medications will be pre-labelled within the internal inventory system, prompting a targeted invitation to the screening. A list of the drugs that are considered for the inclusion criteria is provided in Supplementary Table 3. Additionally, individuals may proactively ask for inclusion if they self-report to be active smokers, have a body mass index (BMI) ≥ 30 kg/m², or increased waist circumference (≥ 88cm for women and ≥ 102cm for men). Implementation An encompassing on-boarding concept will be applied to optimize implementation of the multi-parametric screening in pharmacies. This will include on-site training on the general project workflow, the use of the PoC tests and correct documentation. Informational videos and hand-outs demonstrating the correct application of the PoC tests will be made available to the pharmacies and participants. Technical support provided by the study center will be available to pharmacists and participants throughout the entire duration of the study. The screening is conceptually based on established and reimbursed clinical pharmacy services. Within APOSCREEN-1, community pharmacies receive a reimbursement of €50 per screened patient ( 20 ). Data source and collection at screening In the initial visit, written informed consent will be obtained by the pharmacist and relevant data will be documented using an electronic case report form (eCRF). Table 2 displays the data collected at different time points. Subsequently, participants undergo standardized PoC assessments including microalbuminuria, blood pressure, HbA1c and full lipid profile (total cholesterol, non-high-density lipoprotein (non-HDL-C) cholesterol, HDL cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglycerides). In pharmacies equipped for on-site PoC testing of microalbuminuria and blood pressure, all tests will be conducted at the initial visit. Alternatively, patients will be provided with validated home blood pressure monitors and semi-quantitative microalbuminuria tests along with instructions for testing. Results will be assessed in a second appointment in the pharmacy, together with PoC testing for HbA1c and lipid profile. Table 2 Data collected within the eCRF Category Data collected At Screening At Confirmatory Testing At Follow-Up Demographic data • age • gender • postal code • educational level • employment status • type of health insurance History • currently prescribed medication list • pre-existing medical conditions • family history of cardiovascular disease • smoking status • last participation in a health check-up • medication • visits to the general practitioner after screening findings and therapy • changes from the GP visit • satisfaction with screening / self-assessed benefit of the project Clinical • home blood pressure (3x) • body weight • body height • calculated BMI • waist circumference • 24-hour blood pressure Laboratory • HbA1c • lipid profile (total cholesterol, non-HDL-cholesterol, calculated LDL, HDL, triglycerides) • albuminuria (microalbumin test or urine-albumin-creatinine-ratio) • HbA1c • fasting plasma glucose • lipid profile (total cholesterol, LDL, HDL, triglycerides, lipoprotein(a)) • creatinine, cystatin c • urine-albumin-creatinine-ratio GP, General Practitioner; HDL, high-density lipoprotein; LDL, low-density lipoprotein, BMI, body mass index Methods and equipment used for screening All invasive PoC tests are conducted by pharmacists who have received structured training and technical guidance from the study team. Blood pressure is recorded using a validated automatic device and participants will take three consecutive seated measurements after a five-minute rest period. For screening the following equipment will be used: Home-based microalbuminuria testing: Siegmund Care GmbH, Oberottmarshausen, Germany Pharmacy based microalbuminuria testing (UACR): Afinion 2, Abbott Rapid Diagnostics Germany GmbH, Köln, Germany HbA1c: A1CNow+, Medic-SH, Reinfeld, Germany LDL-C: PTS CardioCheck Plus, Reinfeld, Germany Blood pressure: M2 Comfort, OMRON, Kyōto, Japan Data transmission and analysis The collected screening parameters are securely transmitted to the study center. The digital study platform, including the eCRF and data infrastructure, is provided by meditrova GmbH (Rostock, Germany). It is accessible via smartphone, tablet or personal computer through the internet and an application ( App ). After screening completion, a physician at the study center will review and analyze the data. Based on the results, patients are further stratified: For negative screening results a digital follow-up is conducted. For positive screening results a confirmatory testing and digital follow-up is conducted Confirmatory testing Participants are invited to undergo confirmatory assessment if screening results meet at least one of the following predefined thresholds: systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg, positive urine microalbuminuria test (corresponding to an albuminuria ≥A2 and urine-albumin-creatinine-ratio of ≥ 30 mg/g), a non-HDL-C ≥ 5.7 mmol/l or HbA1c ≥ 5.7% for patients without and ≥ 8.5% for patients with known diabetes (Supplementary Figures S1 - 4 ( 21 – 25 ). In case of critical values at screening, pharmacists will be guided by an emergency algorithm (Supplementary Figure S5 ). Confirmatory testing will be performed at the study center to enable standardized reassessment. To ensure representative coverage of both urban and rural regions, participating pharmacies will be selected accordingly, allowing the study to capture regional heterogeneity in patient presentation and healthcare access. In this context, ten pharmacies located at greater geographical distance (> 30km) from the study center will be included. At the study center visit, all previously screened parameters will be reassessed using standardized laboratory methods. In addition, 24-hour ambulatory blood pressure monitoring (ABPM) or 7-day home-based blood pressure monitoring (HBPM) will be conducted. Blood measurement of creatinine, CRP and lipoprotein(a) will be taken for improved precision of the risk assessment. The patient pathway is visualized in Fig. 1 . Patient risk report and digital follow-up Based on medical history, screening results, and, if available, confirmatory testing outcomes, an individualized risk report based on current national and international guidelines is generated and made accessible for the participant through the digital study platform. The report will provide a structured summary of all relevant findings, with a particular focus on estimated cardiovascular risk (SCORE2, SCORE2-OP, SCORE2-Diabetes, SMART2-Score, depending on applicability) and risk of kidney replacement therapy (kidney failure risk equation), and will serve as a structured basis for follow-up consultations within the primary care system ( 26 – 29 ). Participants with negative screening results will receive a report providing general evidence-based recommendations for general CKM syndrome risk reduction. Data storage Data will be pseudonymized and stored on servers of the study center for 10 years after study completion. Study investigators will have access to information for re-identification. Participants may withdraw consent at any time and request deletion of their data. All data shared for research will be anonymized. Sample size and statistical analysis The planned sample size is a total of 1000 participants from 20 pharmacies (50 participants each). This allows for reliable estimation of key outcomes with 95% confidence intervals with an estimation error of approximately ± 5%. With an expected prevalence of 50% of risk factors and 10 pharmacies in the region of the study center, we expect 250 participants with indication for confirmatory testing at the study center. At an expected attrition of 20%, 200 participants should complete confirmatory testing. This allows for determination of false-positive rates and test specificity with acceptable precision (± 10%). Attrition in the GP-confirmatory testing arm is expected to be higher, but will be minimized through repeated digital or telephone prompts and, if necessary, invitation for testing at the study center. Statistical analysis will be descriptive, using frequency tables, means (± standard deviation), medians (± interquartile range), and proportions with 95% confidence intervals. Diagnostic metrics (sensitivity, specificity, PPV, NPV) will be determined. Sensitivity of the PoC test can be assessed based on the results from the confirmatory testing. Scale scores for implementation metrics will be calculated as the mean of the respective items, with higher values indicating greater agreement and lower values indicating greater disagreement. Discussion The burden of CKM syndrome remains a major, yet increasing, challenge for healthcare systems worldwide ( 30 ). In Germany, one of the key reasons are the low participation-rates of population-based screening programs that aim to identify CKM syndrome risk factors ( 8 ). Therefore, innovation for a more effective prevention is urgently needed. The APOSCREEN-1 study investigates the potential of community pharmacies to help redefine preventive care strategies. A comprehensive dataset will be generated, capturing key metrics such as patient reach, acceptance and completion rates, as well as medical outcomes and pharmacist-reported applicability of the screening workflow. On this basis, primary outcomes were chosen to evaluate the overall applicability of the screening in community pharmacies, encompassing implementability, feasibility, and potential clinical benefit, which are key considerations for future scalability. Our multi-parametric screening-approach will triage patients for further testing who are at significant risk for cardiovascular events or kidney disease while avoiding an overly low threshold for further work-up in order to maintain health care resource feasibility. Through this, the study is designed to address three central challenges of the German healthcare system: First, a significant proportion of individuals remain excluded from prevention strategies due to logistical, social, and systemic barriers ( 31 ). Primary care relies on voluntary check-ups, which limits general practitioners (GP) ability to reach those who do not attend medical practices. In contrast, the APOSCREEN-1 study proposes a widely accessible and frequently used care setting. By collecting screening data, the study will evaluate whether low-threshold screening in pharmacies may enhance access to screening. Second, the incidence of CKM syndrome risk factors is increasing and a considerable number of medically underserved individuals can reasonably be assumed. Based on the obtained data, the APOSCREEN-1 study will enable the assessment of: (I) the prevalence of pathological findings in this at-risk population, (II) the proportion of individuals whose CKM syndrome risk factors remain untreated or suboptimally managed and (III) the number needed to screen for a previously unobserved risk factor (NNS) along with the positive predictive value of the PoC testing. As an exploratory endpoint, the study will assess whether pathological findings translate into changes in prescription and management by primary care providers. Third, Germany faces an aging population with limited personnel resources ( 32 – 34 ). Scalable, decentralized models are warranted. Community pharmacies are professionally staffed locations and well-positioned to support preventive care. Their increasing responsibilities in patient education, vaccination, and the use of diagnostic tools underline the health-serving potential. Feasibility analysis will provide insight into the applicability of a large-scale implementation and its challenges. Limitations: Participants receive an individualized, guideline-based risk report and recommendation tailored to the participant’s screening, nevertheless therapy induction relies on GP consultation. Healthy volunteer bias may limit generalizability of patient-level findings, as participants opting into screening might be more health-conscious than the general population. Further, pharmacy-level selection bias cannot be excluded, as participating pharmacies were self-selected and may not be representative of all community pharmacies. In conclusion, APOSCREEN-1 addresses a timely and structural need to strengthen early CKM syndrome risk identification by leveraging community pharmacies. The findings will be essential to guide the development of large-scale screening efforts and policy discussions around integrating pharmacies into national prevention frameworks. Abbreviations CKD chronic kidney disease CKM syndrome cardiovascular–kidney–metabolic syndrome CVD cardiovascular disease eCRF electronic case report form EU European Union GDP gross domestic product GP general practitioner BMI Body mass index HBPM home blood pressure monitoring HDL C–high–density lipoprotein cholesterol LDL C–low–density lipoprotein cholesterol NNS number needed to screen NPV negative predictive value PPV positive predictive value PoC point–of–care SD standard deviation TC total cholesterol Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of the Christian‑Albrecht University of Kiel under the sign AZ D616/25. The study is conducted according to the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials Data will be shared with researchers who provide a methodically sound proposal. Individual deidentified data collected during the trial will be made accessible. Proposals should be directed to the corresponding author. Data requestors must sign a data access agreement to gain access. Data will be shared to achieve the aims in the approved proposal. Competing interests S.Y.T. reports honoraria from AstraZeneca GmbH and travel support from Lilly GmbH. M.L. and K.S. report honoraria from Lilly GmbH, Boehringer Ingelheim International GmbH, AstraZeneca GmbH and Novo Nordisk Pharma GmbH. L.K.S. received honoraria and travel support from Boehringer Ingelheim, Lilly GmbH and AstraZeneca GmbH. R.S. reports honoraria from Lilly GmbH and AstraZeneca GmbH. F.A.v.S.-H. reports honoraria and travel support from Lilly GmbH and AstraZeneca GmbH. F.A.v.S.-H. and B.K. are supported by the Medical Faculty of the Christian-Albrechts-University Kiel. The other authors declare no conflicts of interest. Funding The study is supported by a grant from the Medical Faculty of the Christian-Albrechts-University of Kiel and by Lilly GmbH, Boehringer Ingelheim International GmbH, AstraZeneca GmbH and Novo Nordisk Pharma GmbH. Neither funding source has had any influence over the design of the study. Acknowledgment The authors thank the Apothekerverband Schleswig-Holstein e.V. for their ongoing support of the project. Authors' contributions FVS, EA, BK and KS developed the APOSCREEN-1 project and acquired funding. FVS, EA and KS are responsible for study management. EA and FVS wrote the initial draft of the paper. All authors contributed substantially to the study design and administration and revised the paper. All authors approved submitting this study protocol for publication. References Naghavi M, Kyu HH, Aalipour AB, Aalruz MA, Ababneh H. u. a. 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Eur Heart J 1 Juli. 2021;42(25):2455–67. 10.1093/eurheartj/ehab312 . Chang R, Parekh T, Hagan KK, Javed Z, Ostrominski JW. JACC Adv 1 Mai. 2025;4(6):101754. 10.1016/j.jacadv.2025.101754 . PubMed PMID: 40315565; PubMed Central PMCID: PMC12084494. State-Level Prevalence of Cardiovascular-Kidney-Metabolic Syndrome Stages in the United States, 2011 to 2023. Hoebel J, Richter M, Lampert T. Social Status and Participation in Health Checks in Men and Women in Germany. Dtsch Arztebl Int 11 Oktober. 2013;110(41):679–85. Haß L, Knippschild S, Tönnies T, Hoyer A, Palm R, Voß S. u. a. Projected number of people in need for long-term care in Germany until 2050. Front Public Health. 2024;12:1456320. 10.3389/fpubh.2024.1456320 . PubMed PMID: 39540091; PubMed Central PMCID: PMC11558338. Nowossadeck E, Prütz F, Teti A. Population change and the burden of hospitalization in Germany 2000–2040: Decomposition analysis and projection. PLoS ONE. 2020;15(12):e0243322. 10.1371/journal.pone.0243322 . PubMed PMID: 33306705; PubMed Central PMCID: PMC7732063. van den Bussche H. Die Zukunftsprobleme der hausärztlichen Versorgung in Deutschland: Aktuelle Trends und notwendige Maßnahmen. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz 1 September. 2019;62(9):1129–37. 10.1007/s00103-019-02997-9 . Additional Declarations Competing interest reported. S.Y.T. reports honoraria from AstraZeneca GmbH and travel support from Lilly GmbH. M.L. and K.S. report honoraria from Lilly GmbH, Boehringer Ingelheim International GmbH, AstraZeneca GmbH and Novo Nordisk Pharma GmbH. L.K.S. received honoraria and travel support from Boehringer Ingelheim, Lilly GmbH and AstraZeneca GmbH. R.S. reports honoraria from Lilly GmbH and AstraZeneca GmbH. F.A.v.S.-H. reports honoraria and travel support from Lilly GmbH and AstraZeneca GmbH. F.A.v.S.-H. and B.K. are supported by the Medical Faculty of the Christian-Albrechts-University Kiel. The other authors declare no conflicts of interest. Supplementary Files NoMadQuestionnaire.docx SupplementaryTable1Measurementofimplementationoutcomesforevaluationofthepharmacyperspective.docx SupplementaryTable3RiskMedication.xlsx SupplementaryFiguresS16.docx SupplementaryTable2WorkloadandScreeningmetrics.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 13 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviews received at journal 09 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviews received at journal 06 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviews received at journal 25 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers invited by journal 16 Mar, 2026 Editor assigned by journal 09 Mar, 2026 Submission checks completed at journal 09 Mar, 2026 First submitted to journal 05 Mar, 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. 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S.Y.T. reports honoraria from AstraZeneca GmbH and travel support from Lilly GmbH. M.L. and K.S. report honoraria from Lilly GmbH, Boehringer Ingelheim International GmbH, AstraZeneca GmbH and Novo Nordisk Pharma GmbH. L.K.S. received honoraria and travel support from Boehringer Ingelheim, Lilly GmbH and AstraZeneca GmbH. R.S. reports honoraria from Lilly GmbH and AstraZeneca GmbH. F.A.v.S.-H. reports honoraria and travel support from Lilly GmbH and AstraZeneca GmbH. F.A.v.S.-H. and B.K. are supported by the Medical Faculty of the Christian-Albrechts-University Kiel. The other authors declare no conflicts of interest.","formattedTitle":"APOSCREEN-1 – a prospective implementation study for pharmacy-based screening for cardiovascular-kidney-metabolic risk factors in Schleswig-Holstein","fulltext":[{"header":"Background","content":"\u003cp\u003eCardiovascular disease (CVD) remains the leading cause of death worldwide (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Its development is driven predominantly by cardiovascular risk factors, including obesity, smoking, arterial hypertension, diabetes mellitus, and hypercholesterolemia. Chronic kidney disease (CKD) further amplifies cardiovascular risk and represents an independent risk factor. The interplay and combination of these factors has been defined as cardiovascular-kidney-metabolic (CKM) syndrome (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe prevalence of the CKM syndrome is rising substantially (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In industrialized countries, 6\u0026ndash;13% of the population are affected by advanced CKM syndrome stages involving organ damage, which corresponds to an estimated 5\u0026ndash;11\u0026nbsp;million individuals in Germany. In the long-term, CKM syndrome shortens life expectancy, reduces social or occupational participation and is a major public health burden (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGermany exemplifies this trend: despite the highest healthcare expenditure in the EU (11,7% of GDP in 2023), life expectancy remains below average due to CVD (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Currently ranked only 17th out of 27 EU countries, Germany\u0026rsquo;s average life expectancy of 81.1 years has fallen below the EU average since 2010. This discrepancy suggests that Germany\u0026rsquo;s healthcare system fails to convert spending into better health outcomes.\u003c/p\u003e \u003cp\u003eEarly identification of CKM syndrome is essential to avoid organ damage (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In Germany, a nationwide preventive health check-up program exists to facilitate early detection of CKM syndrome and its contributing conditions. However, despite the long-standing availability of such programs, participation rates remain low with only 50% of the German population participating regularly (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Therefore, a large proportion of individuals remain undiagnosed until clinical manifestation occurs: approximately 49\u0026ndash;84% of CKD cases, 20% of arterial hypertension, 22% of diabetes mellitus, and more than 50% of dyslipidemia cases remain undetected in the general population (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This underscores the urgent need for more effective and accessible screening strategies.\u003c/p\u003e \u003cp\u003eThe approximately 16.800 German community pharmacies represent a trusted healthcare resource for the public, and are frequently visited with 64% of the population visiting at least once per month (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Pharmacists are highly trained healthcare professionals that may support the healthcare system with clinical services but remain an underutilized resource in this regard.\u003c/p\u003e \u003cp\u003eThe APOSCREEN-1 concept, developed in collaboration with the Pharmacists\u0026rsquo; Association of Schleswig-Holstein, aims to address this critical gap in secondary prevention by implementing a low-threshold, risk-based, and multi-parametric screening approach in community pharmacies.\u003c/p\u003e"},{"header":"Methods/Design","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy aim and design\u003c/h2\u003e \u003cp\u003eThe APOSCREEN-1 study is designed as a prospective implementation study, conducted in community pharmacies located in the federal state of Schleswig-Holstein, Germany. The study aims to assess the feasibility and diagnostic yield of a multi-parametric, pharmacy-based, risk-adapted point-of-care (PoC) screening for CKM syndrome risk factors. It follows a two-step approach: first, a pragmatic point-of-care screening in pharmacies is conducted to triage patients with high cardiovascular risk who may benefit strongly from further diagnostic work-up. In a second step, these patients undergo detailed confirmatory testing and receive guideline-based therapeutic recommendations.\u003c/p\u003e \u003cp\u003eThe study takes place in the outpatient setting and is conducted in cooperation with the Pharmacists\u0026rsquo; Association of Schleswig-Holstein. Ethical approval has been obtained from the local institutional review board (AZ D616/25).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrimary outcomes\u003c/h3\u003e\n\u003cp\u003eThe study will assess three primary outcomes:\u003c/p\u003e \u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eParticipant-level feasibility is assessed by screening completion rates of participants. The outcome is defined as completion of the structured questionnaire, performance of blood pressure measurement and PoC-testing for dyslipidemia and HbA1c. The study aims for an overall completion rate\u0026thinsp;\u0026ge;\u0026thinsp;70% based on the reported use of primary care screenings and prospective feasibility on the national level in Germany.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eImplementation outcomes from the pharmacy perspective are evaluated for the implementation domains acceptability, feasibility, and sustainability. The outcome is evaluated using validated instruments, including the Acceptability of Intervention Measure (AIM), the Feasibility of Intervention Measure (FIM), and the Normalization Measure Development Questionnaire (NoMad) to assess sustainability (Supplementary Table\u0026nbsp;1 and NoMad Questionnaire) (\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). These instruments use standardized self-administered questionnaires completed by pharmacy staff that are rated on Likert scales. Validated German versions will be used. For each domain, the outcome criterion is defined as a median Likert-scale score\u0026thinsp;\u0026ge;\u0026thinsp;3, with the corresponding null hypothesis that this threshold is met for all implementation domains, indicating sufficient implementation performance under routine practice conditions.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe medical screening yield is quantified by the number needed to screen (NNS) to identify at least one previously unknown or insufficiently managed CKM syndrome risk factor. A NNS\u0026thinsp;\u0026le;\u0026thinsp;5 will be considered as indicative of a clinically meaningful detection efficiency of the multi-parametric screening approach.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\n\u003ch3\u003eExploratory outcomes\u003c/h3\u003e\n\u003cp\u003eExploratory outcomes comprise rates of GP consultation and medication changes, diagnostic metrics of multiparametric testing and individual test categories. Diagnostic metrics are assessed including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, workload and screening metrics will be assessed (Supplementary Table\u0026nbsp;2).\u003c/p\u003e\n\u003ch3\u003eInclusion and exclusion criteria\u003c/h3\u003e\n\u003cp\u003eEligible participants are adults aged 40 years or older who visit a participating pharmacy and meet at least one of the criteria summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Recruitment will start on 1 April 2026 and is scheduled to run for 6 months. Participating pharmacies will be instructed to aim for an approximately equal distribution of male and female participants.\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInclusion and exclusion criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInclusion criteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExclusion criteria\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;40 \u003cb\u003eand\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticipation in another study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstablished regular GP \u003cb\u003eand\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInability to give informed consent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive smoking \u003cb\u003eor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrescription of antihypertensive, \u003c/p\u003e \u003cp\u003eantidiabetic, or lipid-lowering medication \u003cb\u003eor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity (BMI \u0026ge;\u0026thinsp;30kg/m\u0026sup2;; waist circumference\u003c/p\u003e \u003cp\u003e\u0026thinsp;\u0026ge;\u0026thinsp;88cm for women or \u0026ge;\u0026thinsp;102cm for men)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eProcedures, data source and collection\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eParticipant selection\u003c/h2\u003e \u003cp\u003ePharmacy customers will either be approached during a regular visit or contacted in advance, if they have authorized their pharmacy to inform them of health improvement projects. Potential participants based on the identification of risk medications will be identified via the pharmacy\u0026rsquo;s dispensing software. These medications will be pre-labelled within the internal inventory system, prompting a targeted invitation to the screening. A list of the drugs that are considered for the inclusion criteria is provided in Supplementary Table\u0026nbsp;3.\u003c/p\u003e \u003cp\u003eAdditionally, individuals may proactively ask for inclusion if they self-report to be active smokers, have a body mass index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;, or increased waist circumference (\u0026ge;\u0026thinsp;88cm for women and \u0026ge;\u0026thinsp;102cm for men).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImplementation\u003c/h3\u003e\n\u003cp\u003eAn encompassing on-boarding concept will be applied to optimize implementation of the multi-parametric screening in pharmacies. This will include on-site training on the general project workflow, the use of the PoC tests and correct documentation. Informational videos and hand-outs demonstrating the correct application of the PoC tests will be made available to the pharmacies and participants. Technical support provided by the study center will be available to pharmacists and participants throughout the entire duration of the study. The screening is conceptually based on established and reimbursed clinical pharmacy services. Within APOSCREEN-1, community pharmacies receive a reimbursement of \u0026euro;50 per screened patient (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eData source and collection at screening\u003c/h3\u003e\n\u003cp\u003eIn the initial visit, written informed consent will be obtained by the pharmacist and relevant data will be documented using an electronic case report form (eCRF). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the data collected at different time points. Subsequently, participants undergo standardized PoC assessments including microalbuminuria, blood pressure, HbA1c and full lipid profile (total cholesterol, non-high-density lipoprotein (non-HDL-C) cholesterol, HDL cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglycerides). In pharmacies equipped for on-site PoC testing of microalbuminuria and blood pressure, all tests will be conducted at the initial visit. Alternatively, patients will be provided with validated home blood pressure monitors and semi-quantitative microalbuminuria tests along with instructions for testing. Results will be assessed in a second appointment in the pharmacy, together with PoC testing for HbA1c and lipid profile.\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData collected within the eCRF\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eData collected\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAt Screening\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAt Confirmatory Testing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAt Follow-Up\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographic data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; age\u003c/p\u003e \u003cp\u003e\u0026bull; gender\u003c/p\u003e \u003cp\u003e\u0026bull; postal code\u003c/p\u003e \u003cp\u003e\u0026bull; educational level\u003c/p\u003e \u003cp\u003e\u0026bull; employment status\u003c/p\u003e \u003cp\u003e\u0026bull; type of health insurance\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; currently prescribed medication list\u003c/p\u003e \u003cp\u003e\u0026bull; pre-existing medical conditions\u003c/p\u003e \u003cp\u003e\u0026bull; family history of cardiovascular disease\u003c/p\u003e \u003cp\u003e\u0026bull; smoking status\u003c/p\u003e \u003cp\u003e\u0026bull; last participation in a health check-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; medication\u003c/p\u003e \u003cp\u003e\u0026bull; visits to the general practitioner after screening findings and therapy\u003c/p\u003e \u003cp\u003e\u0026bull; changes from the GP visit\u003c/p\u003e \u003cp\u003e\u0026bull; satisfaction with screening / self-assessed benefit of the project\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; home blood pressure (3x)\u003c/p\u003e \u003cp\u003e\u0026bull; body weight\u003c/p\u003e \u003cp\u003e\u0026bull; body height\u003c/p\u003e \u003cp\u003e\u0026bull; calculated BMI\u003c/p\u003e \u003cp\u003e\u0026bull; waist circumference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026bull; 24-hour blood pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; HbA1c\u003c/p\u003e \u003cp\u003e\u0026bull; lipid profile (total cholesterol, non-HDL-cholesterol, calculated LDL, HDL, triglycerides)\u003c/p\u003e \u003cp\u003e\u0026bull; albuminuria (microalbumin test or urine-albumin-creatinine-ratio)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026bull; HbA1c\u003c/p\u003e \u003cp\u003e\u0026bull; fasting plasma glucose\u003c/p\u003e \u003cp\u003e\u0026bull; lipid profile (total cholesterol, LDL, HDL, triglycerides, lipoprotein(a))\u003c/p\u003e \u003cp\u003e\u0026bull; creatinine, cystatin c\u003c/p\u003e \u003cp\u003e\u0026bull; urine-albumin-creatinine-ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eGP, General Practitioner; HDL, high-density lipoprotein; LDL, low-density lipoprotein, BMI, body mass index\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\u003eMethods and equipment used for screening\u003c/h2\u003e \u003cp\u003eAll invasive PoC tests are conducted by pharmacists who have received structured training and technical guidance from the study team. Blood pressure is recorded using a validated automatic device and participants will take three consecutive seated measurements after a five-minute rest period.\u003c/p\u003e \u003cp\u003eFor screening the following equipment will be used:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHome-based microalbuminuria testing: Siegmund Care GmbH, Oberottmarshausen, Germany\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePharmacy based microalbuminuria testing (UACR): Afinion 2, Abbott Rapid Diagnostics Germany GmbH, K\u0026ouml;ln, Germany\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHbA1c: A1CNow+, Medic-SH, Reinfeld, Germany\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLDL-C: PTS CardioCheck Plus, Reinfeld, Germany\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eBlood pressure: M2 Comfort, OMRON, Kyōto, Japan\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData transmission and analysis\u003c/h2\u003e \u003cp\u003eThe collected screening parameters are securely transmitted to the study center. The digital study platform, including the eCRF and data infrastructure, is provided by meditrova GmbH (Rostock, Germany). It is accessible via smartphone, tablet or personal computer through the internet and an application (\u003cem\u003eApp\u003c/em\u003e). After screening completion, a physician at the study center will review and analyze the data. Based on the results, patients are further stratified:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFor negative screening results a digital follow-up is conducted.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFor positive screening results a confirmatory testing and digital follow-up is conducted\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eConfirmatory testing\u003c/h2\u003e \u003cp\u003eParticipants are invited to undergo confirmatory assessment if screening results meet at least one of the following predefined thresholds: systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg and/or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, positive urine microalbuminuria test (corresponding to an albuminuria \u0026ge;A2 and urine-albumin-creatinine-ratio of \u0026ge;\u0026thinsp;30 mg/g), a non-HDL-C\u0026thinsp;\u0026ge;\u0026thinsp;5.7 mmol/l or HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;5.7% for patients without and \u0026ge;\u0026thinsp;8.5% for patients with known diabetes (Supplementary Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-\u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). In case of critical values at screening, pharmacists will be guided by an emergency algorithm (Supplementary Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConfirmatory testing will be performed at the study center to enable standardized reassessment. To ensure representative coverage of both urban and rural regions, participating pharmacies will be selected accordingly, allowing the study to capture regional heterogeneity in patient presentation and healthcare access. In this context, ten pharmacies located at greater geographical distance (\u0026gt;\u0026thinsp;30km) from the study center will be included.\u003c/p\u003e \u003cp\u003eAt the study center visit, all previously screened parameters will be reassessed using standardized laboratory methods. In addition, 24-hour ambulatory blood pressure monitoring (ABPM) or 7-day home-based blood pressure monitoring (HBPM) will be conducted. Blood measurement of creatinine, CRP and lipoprotein(a) will be taken for improved precision of the risk assessment. The patient pathway is visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePatient risk report and digital follow-up\u003c/h2\u003e \u003cp\u003e Based on medical history, screening results, and, if available, confirmatory testing outcomes, an individualized risk report based on current national and international guidelines is generated and made accessible for the participant through the digital study platform. The report will provide a structured summary of all relevant findings, with a particular focus on estimated cardiovascular risk (SCORE2, SCORE2-OP, SCORE2-Diabetes, SMART2-Score, depending on applicability) and risk of kidney replacement therapy (kidney failure risk equation), and will serve as a structured basis for follow-up consultations within the primary care system (\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Participants with negative screening results will receive a report providing general evidence-based recommendations for general CKM syndrome risk reduction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eData storage\u003c/h2\u003e \u003cp\u003eData will be pseudonymized and stored on servers of the study center for 10 years after study completion. Study investigators will have access to information for re-identification. Participants may withdraw consent at any time and request deletion of their data. All data shared for research will be anonymized.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSample size and statistical analysis\u003c/h2\u003e \u003cp\u003eThe planned sample size is a total of 1000 participants from 20 pharmacies (50 participants each). This allows for reliable estimation of key outcomes with 95% confidence intervals with an estimation error of approximately\u0026thinsp;\u0026plusmn;\u0026thinsp;5%. With an expected prevalence of 50% of risk factors and 10 pharmacies in the region of the study center, we expect 250 participants with indication for confirmatory testing at the study center. At an expected attrition of 20%, 200 participants should complete confirmatory testing. This allows for determination of false-positive rates and test specificity with acceptable precision (\u0026plusmn;\u0026thinsp;10%). Attrition in the GP-confirmatory testing arm is expected to be higher, but will be minimized through repeated digital or telephone prompts and, if necessary, invitation for testing at the study center. Statistical analysis will be descriptive, using frequency tables, means (\u0026plusmn;\u0026thinsp;standard deviation), medians (\u0026plusmn;\u0026thinsp;interquartile range), and proportions with 95% confidence intervals. Diagnostic metrics (sensitivity, specificity, PPV, NPV) will be determined. Sensitivity of the PoC test can be assessed based on the results from the confirmatory testing. Scale scores for implementation metrics will be calculated as the mean of the respective items, with higher values indicating greater agreement and lower values indicating greater disagreement.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe burden of CKM syndrome remains a major, yet increasing, challenge for healthcare systems worldwide (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In Germany, one of the key reasons are the low participation-rates of population-based screening programs that aim to identify CKM syndrome risk factors (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Therefore, innovation for a more effective prevention is urgently needed.\u003c/p\u003e \u003cp\u003eThe APOSCREEN-1 study investigates the potential of community pharmacies to help redefine preventive care strategies. A comprehensive dataset will be generated, capturing key metrics such as patient reach, acceptance and completion rates, as well as medical outcomes and pharmacist-reported applicability of the screening workflow. On this basis, primary outcomes were chosen to evaluate the overall applicability of the screening in community pharmacies, encompassing implementability, feasibility, and potential clinical benefit, which are key considerations for future scalability.\u003c/p\u003e \u003cp\u003eOur multi-parametric screening-approach will triage patients for further testing who are at significant risk for cardiovascular events or kidney disease while avoiding an overly low threshold for further work-up in order to maintain health care resource feasibility. Through this, the study is designed to address three central challenges of the German healthcare system:\u003c/p\u003e \u003cp\u003eFirst, a significant proportion of individuals remain excluded from prevention strategies due to logistical, social, and systemic barriers (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Primary care relies on voluntary check-ups, which limits general practitioners (GP) ability to reach those who do not attend medical practices. In contrast, the APOSCREEN-1 study proposes a widely accessible and frequently used care setting. By collecting screening data, the study will evaluate whether low-threshold screening in pharmacies may enhance access to screening.\u003c/p\u003e \u003cp\u003eSecond, the incidence of CKM syndrome risk factors is increasing and a considerable number of medically underserved individuals can reasonably be assumed. Based on the obtained data, the APOSCREEN-1 study will enable the assessment of: (I) the prevalence of pathological findings in this at-risk population, (II) the proportion of individuals whose CKM syndrome risk factors remain untreated or suboptimally managed and (III) the number needed to screen for a previously unobserved risk factor (NNS) along with the positive predictive value of the PoC testing. As an exploratory endpoint, the study will assess whether pathological findings translate into changes in prescription and management by primary care providers.\u003c/p\u003e \u003cp\u003eThird, Germany faces an aging population with limited personnel resources (\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Scalable, decentralized models are warranted. Community pharmacies are professionally staffed locations and well-positioned to support preventive care. Their increasing responsibilities in patient education, vaccination, and the use of diagnostic tools underline the health-serving potential. Feasibility analysis will provide insight into the applicability of a large-scale implementation and its challenges.\u003c/p\u003e \u003cp\u003e Limitations: Participants receive an individualized, guideline-based risk report and recommendation tailored to the participant\u0026rsquo;s screening, nevertheless therapy induction relies on GP consultation. Healthy volunteer bias may limit generalizability of patient-level findings, as participants opting into screening might be more health-conscious than the general population. Further, pharmacy-level selection bias cannot be excluded, as participating pharmacies were self-selected and may not be representative of all community pharmacies.\u003c/p\u003e \u003cp\u003eIn conclusion, APOSCREEN-1 addresses a timely and structural need to strengthen early CKM syndrome risk identification by leveraging community pharmacies. The findings will be essential to guide the development of large-scale screening efforts and policy discussions around integrating pharmacies into national prevention frameworks.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCKD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echronic kidney disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCKM syndrome\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecardiovascular\u0026ndash;kidney\u0026ndash;metabolic syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecardiovascular disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eeCRF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eelectronic case report form\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEuropean Union\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGDP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egross domestic product\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egeneral practitioner\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHBPM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehome blood pressure monitoring\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC\u0026ndash;high\u0026ndash;density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC\u0026ndash;low\u0026ndash;density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNNS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enumber needed to screen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enegative predictive value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epositive predictive value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePoC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epoint\u0026ndash;of\u0026ndash;care\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etotal cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the Christian‑Albrecht University of Kiel under the sign AZ D616/25. The study is conducted according to the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be shared with researchers who provide a methodically sound proposal. Individual deidentified data collected during the trial will be made accessible. Proposals should be directed to the corresponding author. Data requestors must sign a data access agreement to gain access. Data will be shared to achieve the aims in the approved proposal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.Y.T. reports honoraria from AstraZeneca GmbH and travel support from Lilly GmbH. M.L. and K.S. report honoraria from Lilly GmbH, Boehringer Ingelheim International GmbH, AstraZeneca GmbH and Novo Nordisk Pharma GmbH. L.K.S. received honoraria and travel support from Boehringer Ingelheim, Lilly GmbH and AstraZeneca GmbH. R.S. reports honoraria from Lilly GmbH and AstraZeneca GmbH. F.A.v.S.-H. reports honoraria and travel support from Lilly GmbH and AstraZeneca GmbH. F.A.v.S.-H. and B.K. are supported by the Medical Faculty of the Christian-Albrechts-University Kiel. The other authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study is supported by a grant from the Medical Faculty of the Christian-Albrechts-University of Kiel and by Lilly GmbH, Boehringer Ingelheim International GmbH, AstraZeneca GmbH and\u0026nbsp;Novo Nordisk Pharma GmbH. Neither funding source has had any influence over the design of the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Apothekerverband Schleswig-Holstein e.V. for their ongoing support of the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFVS, EA, BK and KS developed the APOSCREEN-1 project and acquired funding. FVS, EA and KS are responsible for study management. EA and FVS wrote the initial draft of the paper. All authors contributed substantially to the study design and administration and revised the paper. All authors approved submitting this study protocol for publication.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNaghavi M, Kyu HH, Aalipour AB, Aalruz MA, Ababneh H. u. a. 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Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz 1 September. 2019;62(9):1129\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00103-019-02997-9\u003c/span\u003e\u003cspan address=\"10.1007/s00103-019-02997-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"prevention, cardiovascular risk, chronic kidney disease, diabetes, hypertension, point-of-care testing, community pharmacy, public health","lastPublishedDoi":"10.21203/rs.3.rs-9039113/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9039113/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eCardiovascular-kidney-metabolic (CKM) syndrome has emerged as a major global health burden and driver of cardiovascular disease (CVD), the leading cause of death worldwide. Effective management of CKM depends on timely identification of underlying risk factors. Nevertheless, participation rates in primary care-based screenings are low. Consequently, CKM syndrome oftentimes remains undetected until organ damage is clinically present. Community pharmacies offer an accessible, yet underused, setting to enhance early detection. APOSCREEN-1 evaluates the feasibility and diagnostic yield of a pharmacy-based multi-parametric screening for cardiovascular-kidney-metabolic health.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eAPOSCREEN-1 is a prospective implementation study conducted in 20 community pharmacies in the German state of Schleswig-Holstein. Adults (n\u0026thinsp;=\u0026thinsp;1000) aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years with predefined risk criteria are included. Participants undergo standardized point-of-care testing (glycated hemoglobin, lipid profile, urinary albumin, blood pressure). Additionally, clinical history is assessed and results are transmitted to the study center via an online platform. Patients meeting pre-defined thresholds of the tested parameters are followed up by confirmatory laboratory testing at the study center or at the participants\u0026rsquo; general practitioner. Primary outcomes include completion rate, implementation metrics from the pharmacy perspective, and the number needed to screen to detect unknown or insufficiently managed cardiometabolic risk factors. Secondary outcomes comprise participant metrics, diagnostic metrics of the screening, evaluation of clinical impact.\u003c/p\u003e\u003ch2\u003eDiscussion:\u003c/h2\u003e \u003cp\u003eThis study addresses the unmet need for scalable prevention of CVD by identification of CKM syndrome risk factors outside traditional primary care settings. Evidence on feasibility, acceptance, and diagnostic benefit may support the use of community pharmacies as an additional access point for early CKM syndrome detection. Future interventional studies will be required to evaluate structured follow-up pathways and long-term effectiveness.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eThis study was registered with the German trial registry (Deutsches Register klinischer Studien) on 29.01.2026, under trial number DRKS00039149.\u003c/p\u003e","manuscriptTitle":"APOSCREEN-1 – a prospective implementation study for pharmacy-based screening for cardiovascular-kidney-metabolic risk factors in Schleswig-Holstein","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 16:10:36","doi":"10.21203/rs.3.rs-9039113/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-13T13:36:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T05:28:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T04:16:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326654374719724672981760339357975846412","date":"2026-04-08T21:57:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156543379773479223011735185457941368436","date":"2026-04-07T07:23:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-06T06:42:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70372750756104621679536215519448658616","date":"2026-04-05T20:44:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81024747269549749018063429302205298929","date":"2026-04-03T23:09:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222491964895712365993386962883489445711","date":"2026-04-03T21:00:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T18:47:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97742965067769006036260115971936615041","date":"2026-03-18T17:39:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-16T17:05:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-09T05:46:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-09T05:46:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2026-03-05T10:32:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2be7c574-bd51-4eb8-ac65-27ea2a917e09","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T19:38:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 16:10:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9039113","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9039113","identity":"rs-9039113","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}