{"doi":"10.26502/fccm.92920387","title":"A Signature of Pre-Operative Biomarkers of Cellular Senescence to Predict Risk of Cardiac and Kidney Adverse Events after Cardiac Surgery","abstract":"Importance: Improved pre-operative risk stratification methods are needed for targeted risk mitigation and optimization of care pathways for cardiac patients. This is the first report demonstrating pre-operative, aging-related biomarkers of cellular senescence and immune system function can predict risk of common and serious cardiac surgery-related adverse events. Design: Primary outcome was KDIGO-defined acute kidney injury (AKI). Secondary outcomes: decline in eGFR ≥25% at 30d and a composite of major adverse cardiac and kidney events at 30d (MACKE30). Biomarkers were assessed in blood samples collected prior to surgery. Results: A multivariate regression model of six senescence biomarkers (p16, p14, LAG3, CD244, CD28 and suPAR) identified patients at risk for AKI (NPV 86.6%, accuracy 78.6%), decline in eGFR (NPV 93.5%, accuracy 85.2%), and MACKE30 (NPV 91.4%, accuracy 79.9%). Patients in the top risk tertile had 7.8 (3.3-18.4) higher odds of developing AKI, 4.5 (1.6-12.6) higher odds of developing renal decline at 30d follow-up, and 5.7 (2.1-15.6) higher odds of developing MACKE30 versus patients in the bottom tertile. All models remained significant when adjusted for clinical variables. Conclusions: A network of senescence biomarkers, a fundamental mechanism of aging, can identify patients at risk for adverse kidney and cardiac events when measured pre-operatively. These findings lay the foundation to improve pre-surgical risk assessment with measures that capture heterogeneity of aging, thereby improving clinical outcomes and resource utilization in cardiac surgery.","journal":"Cardiology and Cardiovascular Medicine","year":2024,"id":480603,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9571,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":394916,"name":"Susan Walker","orcid":"0000-0001-9075-4655","position":1,"is_corresponding":false},{"id":913305,"name":"Anne K. Knecht","orcid":"0000-0003-4777-7880","position":2,"is_corresponding":false},{"id":913931,"name":"Susan L. Strum","orcid":null,"position":3,"is_corresponding":false},{"id":1169001,"name":"Asad A. Shah","orcid":"0000-0001-9323-9181","position":4,"is_corresponding":false},{"id":892717,"name":"Aliaksei Pustavoitau","orcid":"0000-0003-2104-3598","position":5,"is_corresponding":false},{"id":528939,"name":"Natalia Mitin","orcid":"0000-0003-4898-7716","position":6,"is_corresponding":false},{"id":389333,"name":"Judson B. Williams","orcid":null,"position":7,"is_corresponding":false},{"id":1169400,"name":"Amy Entwistle","orcid":null,"position":0,"is_corresponding":true}],"reference_count":29,"raw_metadata":null,"created_at":"2026-07-19T02:07:02.142014Z","pmid":"39328896","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":[]}