{"doi":"10.1016/j.hrthm.2024.02.015","title":"Enhancing transvenous lead extraction risk prediction: Integrating imaging biomarkers into machine learning models","abstract":"BACKGROUND: Machine learning (ML) models have been proposed to predict risk related to transvenous lead extraction (TLE). OBJECTIVE: The purpose of this study was to test whether integrating imaging data into an existing ML model increases its ability to predict major adverse events (MAEs; procedure-related major complications and procedure-related deaths) and lengthy procedures (≥100 minutes). METHODS: We hypothesized certain features-(1) lead angulation, (2) coil percentage inside the superior vena cava (SVC), and (3) number of overlapping leads in the SVC-detected from a pre-TLE plain anteroposterior chest radiograph (CXR) would improve prediction of MAE and long procedural times. A deep-learning convolutional neural network was developed to automatically detect these CXR features. RESULTS: A total of 1050 cases were included, with 24 MAEs (2.3%) . The neural network was able to detect (1) heart border with 100% accuracy; (2) coils with 98% accuracy; and (3) acute angle in the right ventricle and SVC with 91% and 70% accuracy, respectively. The following features significantly improved MAE prediction: (1) ≥50% coil within the SVC; (2) ≥2 overlapping leads in the SVC; and (3) acute lead angulation. Balanced accuracy (0.74-0.87), sensitivity (68%-83%), specificity (72%-91%), and area under the curve (AUC) (0.767-0.962) all improved with imaging biomarkers. Prediction of lengthy procedures also improved: balanced accuracy (0.76-0.86), sensitivity (75%-85%), specificity (63%-87%), and AUC (0.684-0.913). CONCLUSION: Risk prediction tools integrating imaging biomarkers significantly increases the ability of ML models to predict risk of MAE and long procedural time related to TLE.","journal":"Heart Rhythm","year":2024,"id":439727,"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":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9627,"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":1251035,"name":"YingLiang Ma","orcid":"0000-0001-5770-5843","position":1,"is_corresponding":false},{"id":891997,"name":"Nadeev Wijesuriya","orcid":"0000-0001-7893-0708","position":2,"is_corresponding":false},{"id":1137361,"name":"Felicity De Vere","orcid":"0000-0002-5588-4451","position":3,"is_corresponding":false},{"id":1129518,"name":"Sandra Howell","orcid":"0000-0002-3925-2913","position":4,"is_corresponding":false},{"id":713809,"name":"Mark K. Elliott","orcid":"0000-0003-2805-3043","position":5,"is_corresponding":false},{"id":1251457,"name":"Nilanka N. Mannkakara","orcid":null,"position":6,"is_corresponding":false},{"id":1137362,"name":"Tatiana Hamakarim","orcid":"0000-0003-1948-0094","position":7,"is_corresponding":false},{"id":1059543,"name":"Tom Wong","orcid":"0000-0002-6484-4961","position":8,"is_corresponding":false},{"id":1251036,"name":"Hugh O’Brien","orcid":"0000-0002-6829-5306","position":9,"is_corresponding":false},{"id":328101,"name":"Steven Niederer","orcid":"0000-0002-4612-6982","position":10,"is_corresponding":false},{"id":1137363,"name":"Reza Razavi","orcid":"0000-0003-1065-3008","position":11,"is_corresponding":false},{"id":715275,"name":"Christopher A. Rinaldi","orcid":"0000-0002-3930-1957","position":12,"is_corresponding":false},{"id":715273,"name":"Vishal Mehta","orcid":"0000-0001-6140-068X","position":0,"is_corresponding":true}],"reference_count":27,"raw_metadata":null,"created_at":"2026-07-19T02:00:52.633010Z","pmid":"38354872","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":[]}