{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T07:54:17Z","timestamp":1790927657393,"version":"4.1.0"},"reference-count":87,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Science Reports"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Cardiovascular disease (CVD) remains the leading cause of global morbidity and mortality. This study assesses the agreement between non\u2010laboratory\u2010based and laboratory\u2010based CVD risk equations across diverse settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>PubMed, Scopus, Web of Science, ProQuest Dissertations and Theses Global, and Google Scholar were systematically searched for studies published up to March 4, 2025. The protocol was registered with PROSPERO (CRD42021291936). Studies comparing laboratory\u2010based and non\u2010laboratory\u2010based CVD risk equations were included, excluding those with participants who had CVD at baseline. A meta\u2010analysis was conducted using mixed\u2010effects meta\u2010regression. The agreements for each study item (unit of analysis) were measured using the Spearman correlation coefficient and Kappa statistics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 33 studies, including 243,587 participants and nine CVD risk equations, were identified. The pooled Spearman correlation between the non\u2010laboratory\u2010based and laboratory\u2010based equations was 0.954 (95% CI: 0.928\u20130.971, I\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u2009100%,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001), and the pooled kappa was 0.64 (95% CI: 0.61\u20130.67, I\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u200999%,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001). Correlation was higher in studies conducted before 2000 (0.974; 95% CI: 0.970\u20130.978) compared to those conducted after 2000 (0.953; 95% CI: 0.948\u20130.958;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001). Studies from high\u2010income settings had higher correlations (0.967; 95% CI: 0.963\u20130.970) than those from low\u2010income settings (0.945; 95% CI: 0.933\u20130.955). Equations predicting fatal outcomes had higher correlations (0.979; 95% CI: 0.977\u20130.982) than those predicting both fatal and non\u2010fatal outcomes (0.942; 95% CI: 0.937\u20130.947).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Non\u2010laboratory\u2010based CVD risk equations demonstrate strong correlation and substantial agreement with laboratory\u2010based equations. Non\u2010laboratory\u2010based CVD risk equations show strong concordance with laboratory\u2010based equations in many settings; however, strong correlation and substantial agreement do not necessarily indicate predictive equivalence, and their interchangeability and implementation should be considered context\u2010specific and require external validation and recalibration.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/hsr2.72931","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T12:23:01Z","timestamp":1785500581000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparing Non\u2010Laboratory\u2010Based and Laboratory\u2010Based Cardiovascular Risk Predictions: Systematic Review and Meta\u2010Analysis"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3145-3494","authenticated-orcid":false,"given":"Yihun Mulugeta","family":"Alemu","sequence":"first","affiliation":[{"name":"National Centre for Epidemiology and Population Health, College of Health and Medicine Australian National University Canberra Australia"},{"name":"Department of Epidemiology and Biostatistics, School of Public Health, College of Medicine and Health Science Bahir Dar University Bahir Dar Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9270-9281","authenticated-orcid":false,"given":"Sisay M.","family":"Alemu","sequence":"additional","affiliation":[{"name":"German Cancer Research Centre (DKFZ) Heidelberg, Division Policy and Implementation Research for Cancer Prevention Heidelberg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1097-2797","authenticated-orcid":false,"given":"Nasser","family":"Bagheri","sequence":"additional","affiliation":[{"name":"National Centre for Epidemiology and Population Health, College of Health and Medicine Australian National University Canberra Australia"},{"name":"Health Research Institute University of Canberra Canberra Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8857-2665","authenticated-orcid":false,"given":"Kinley","family":"Wangdi","sequence":"additional","affiliation":[{"name":"National Centre for Epidemiology and Population Health, College of Health and Medicine Australian National University Canberra Australia"},{"name":"HEAL Global Research Centre, Health Research Institute University of Canberra Canberra Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2215-820X","authenticated-orcid":false,"given":"Dan","family":"Chateau","sequence":"additional","affiliation":[{"name":"National Centre for Epidemiology and Population Health, College of Health and Medicine Australian National University Canberra Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajpc.2024.100633"},{"issue":"3","key":"e_1_2_12_3_1","article-title":"Risk Scoring for the Primary Prevention of Cardiovascular Disease","volume":"3","author":"Karmali K. 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