{"doi":"10.64898/2026.02.03.26345370","title":"Repurposing cardiovascular disease prediction models for cancer","abstract":"<jats:title>Abstract</jats:title>\n                <jats:sec>\n                  <jats:title>Background</jats:title>\n                  <jats:p>Population cancer screening detects the presence of early-stage disease rather than assessing future disease risk. We evaluated whether widely implemented cardiovascular disease (CVD) risk models can predict 10-year cancer risk and compared them with a less widely used cancer risk model (QCancer).</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Methods</jats:title>\n                  <jats:p>We evaluated four CVD prediction models: QRISK3, the Pooled Cohort Equations (PCE), SCORE2 and SCORE2-OP. All models were recalibrated using 20% of the UK Biobank (UKB) cohort and tested in the remainder, as well as in the Clinical Practice Research Datalink (CPRD). We gauged model performance using c-statistics for discrimination and evaluated the fidelity of calibration. We also identified the most influential risk factors in the QRISK3 model.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Findings</jats:title>\n                  <jats:p>In the UKB test set, the c-statistics for incident CVD ranged from 0·71 to 0·74 (11,022 events). All CVD models achieved a c-statistic of 0·63 for any cancer (23,010 events) and showed CVD-equivalent discrimination for gastro-oesophageal, liver and biliary tree, laryngeal, renal tract, and lung cancers (c-statistic range: 0·70;0·81). Overall, the discrimination of the CVD models was comparable that of the QCancer models (median difference in c-statistic: -0·01 (95%CI -0·03;0·00). The recalibrated CVD models showed near-perfect calibration (median intercept 0·01, Q1;Q3 -0·05;0·03 and slope 1·00, Q1;Q3 0·93;1·15). Performance in CPRD (393,658 cancer events) was similar: the median c-statistic, calibration intercept, and slope were 0·01 (95%CI 0·00;0·02), 0·05 (95%CI 0·02;0·17), and 0·04 (95%CI 0·01;0·15) higher, respectively, in CPRD than in UKB. After age, smoking status and systolic blood pressure were the most influential predictors of cancer risk.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Interpretation</jats:title>\n                  <jats:p>Widely implemented CVD prediction models perform similarly to the QCancer models in the prediction of incident cancers. They may be used to inform cancer prevention and guide risk-stratified monitoring. The recalibrated models are available through an API.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Funding</jats:title>\n                  <jats:p>Health Data Research UK, British Heart Foundation and UK Research and Innovation.</jats:p>\n                </jats:sec>","journal":null,"year":null,"id":663014,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":21889,"name":"Aroon D. Hingorani","orcid":"0000-0001-8365-0081","position":1,"is_corresponding":false},{"id":39404,"name":"Nish Chaturvedi","orcid":"0000-0002-6211-2775","position":2,"is_corresponding":false},{"id":590064,"name":"Amand F. Schmidt","orcid":"0000-0003-1327-0424","position":3,"is_corresponding":false},{"id":1730948,"name":"Sam Quill","orcid":"0009-0002-0493-9106","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Repurposing cardiovascular disease prediction models for cancer","abstract":"<jats:title>Abstract</jats:title>\n                <jats:sec>\n                  <jats:title>Background</jats:title>\n                  <jats:p>Population cancer screening detects the presence of early-stage disease rather than assessing future disease risk. We evaluated whether widely implemented cardiovascular disease (CVD) risk models can predict 10-year cancer risk and compared them with a less widely used cancer risk model (QCancer).</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Methods</jats:title>\n                  <jats:p>We evaluated four CVD prediction models: QRISK3, the Pooled Cohort Equations (PCE), SCORE2 and SCORE2-OP. All models were recalibrated using 20% of the UK Biobank (UKB) cohort and tested in the remainder, as well as in the Clinical Practice Research Datalink (CPRD). We gauged model performance using c-statistics for discrimination and evaluated the fidelity of calibration. We also identified the most influential risk factors in the QRISK3 model.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Findings</jats:title>\n                  <jats:p>In the UKB test set, the c-statistics for incident CVD ranged from 0·71 to 0·74 (11,022 events). All CVD models achieved a c-statistic of 0·63 for any cancer (23,010 events) and showed CVD-equivalent discrimination for gastro-oesophageal, liver and biliary tree, laryngeal, renal tract, and lung cancers (c-statistic range: 0·70;0·81). Overall, the discrimination of the CVD models was comparable that of the QCancer models (median difference in c-statistic: -0·01 (95%CI -0·03;0·00). The recalibrated CVD models showed near-perfect calibration (median intercept 0·01, Q1;Q3 -0·05;0·03 and slope 1·00, Q1;Q3 0·93;1·15). Performance in CPRD (393,658 cancer events) was similar: the median c-statistic, calibration intercept, and slope were 0·01 (95%CI 0·00;0·02), 0·05 (95%CI 0·02;0·17), and 0·04 (95%CI 0·01;0·15) higher, respectively, in CPRD than in UKB. After age, smoking status and systolic blood pressure were the most influential predictors of cancer risk.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Interpretation</jats:title>\n                  <jats:p>Widely implemented CVD prediction models perform similarly to the QCancer models in the prediction of incident cancers. They may be used to inform cancer prevention and guide risk-stratified monitoring. The recalibrated models are available through an API.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Funding</jats:title>\n                  <jats:p>Health Data Research UK, British Heart Foundation and UK Research and Innovation.</jats:p>\n                </jats:sec>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19162232","pmcid":null,"openalex_id":"https://openalex.org/W7127571481","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"green","license":"other-oa","oa_locations":[{"url":"https://www.medrxiv.org/content/medrxiv/early/2026/02/04/2026.02.03.26345370.full.pdf","host_type":"repository"},{"url":"https://www.medrxiv.org/content/medrxiv/early/2026/02/04/2026.02.03.26345370.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.64898/2026.02.03.26345370","host_type":"publisher"},{"url":"https://doi.org/10.64898/2026.02.03.26345370","host_type":"repository"}],"fields_of_study":["Cardiovascular Disease and Adiposity","Cardiovascular Health and Risk Factors","GDF15 and Related Biomarkers"],"mesh_terms":[],"keywords":["Cancer","Disease","Predictive modelling","Calibration","Lung cancer","Cohort","Clinical Practice","Prostate cancer","Risk assessment"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Peace, Justice and strong institutions"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-12T19:00:43.882118Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}