{ "id": "AIBD-0269", "title": "EchoNext ECG flag finds severe aortic stenosis in an asymptomatic 84-year-old; TAVR after hip-fracture surgery", "summary": "At Columbia/NewYork-Presbyterian, EchoNext flagged a pre-operative ECG (score 0.6094), triggering an EHR advisory; echo showed LVEF 40% and severe aortic stenosis; cardiac anesthesia, surgery, TAVR.", "date_observed": "2026-03-04", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Tat et al. (JACC: Case Reports, 4 March 2026) describe an 84-year-old woman with hypertension. She was admitted after a fall with a left femur fracture. Her electrocardiogram (ECG) was analyzed by EchoNext, a deep-learning model for structural heart disease developed at Columbia. The score was 0.6094; the model's high-risk cutoff is 0.6. The score triggered an electronic health record (EHR) advisory. Transthoracic echocardiography showed an ejection fraction of 40% and severe aortic stenosis. The valve area was 0.6 cm2, peak velocity 3.9 m/s and mean gradient 34 mm Hg. Cardiology recommended cardiac anesthesia for the orthopedic operation. The patient then underwent transcatheter aortic valve replacement (TAVR), on day 8. The authors call it the first report of an AI-ECG algorithm detecting undiagnosed severe aortic stenosis that led to TAVR. EchoNext's development paper (Poterucha et al., Nature, July 2025) reports training on more than 1 million ECG and imaging records. It reports that 73% of patients flagged high-risk in prospective evaluation had newly diagnosed disease. All authors are the Columbia developers. Dr Elias reports research support including from Edwards.", "category": "benefit created", "unexpected": false, "purpose": "Screen ECGs for unrecognised structural heart disease and alert clinicians through the electronic health record", "system": "EchoNext deep-learning ECG model integrated into the electronic health record", "model_version": "second-generation EchoNext model", "developer": "Columbia University (Poterucha, Elias and colleagues)", "deployer": "Columbia University Irving Medical Center / NewYork-Presbyterian", "location": "New York, New York, USA", "domain": "Health; cardiology", "segment": "Hospital patients with an ECG and no known structural heart disease", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 3, "reason": "Severe aortic stenosis found before non-cardiac surgery, changing perioperative management and leading to TAVR." }, { "group": "organizational", "depth": null, "reason": "No organizational outcome reported for this case." }, { "group": "societal", "depth": null, "reason": "Single case." } ], "ai_role": { "status": "assessed", "statement": "The EHR advisory from EchoNext prompted the echocardiogram in an asymptomatic patient, according to the developers' own case report. The evidence is developer-authored: the case report and the Nature development paper, with disclosed industry research support. No independent account was found." }, "evidence": [ { "kind": "case report", "citation": "Tat E, Oliver FR, Malta P, Castillo M, Poterucha TJ, Elias P. JACC Case Rep 31(14):107184 (2026)", "title": "Artificial Intelligence-Enabled Electrocardiographic Detection of Severe Aortic Stenosis Leading to Transcatheter Aortic Valve Replacement", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13080615/", "date": "2026-03-04", "written_by": "makers", "quote": "To our knowledge, this is the first report of an artificial intelligence-enabled ECG algorithm detecting undiagnosed severe AS that led to transcatheter aortic valve replacement." }, { "kind": "peer-reviewed study", "citation": "Poterucha TJ, Jing L, Ricart RP, et al. Nature (2025)", "title": "Detecting structural heart disease from electrocardiograms using AI", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12328201/", "date": "2025-07-16", "written_by": "makers", "quote": "we introduce a deep learning model, EchoNext, trained on more than 1 million heart rhythm and imaging records across a large and diverse health system" }, { "kind": "press release", "citation": "NewYork-Presbyterian (23 Jun 2026). AI Tool Developed at NewYork-Presbyterian and Columbia to Detect Hidden Heart Disease Receives FDA Approval.", "title": "AI Tool Developed at NewYork-Presbyterian and Columbia to Detect Hidden Heart Disease Receives FDA Approval", "url": "https://www.nyp.org/news/ai-tool-developed-at-newyork-presbyterian-and-columbia-to-detect-hidden-heart-disease-receives-fda-approval", "date": "2026-06-23", "written_by": "makers", "quote": null } ], "replication": { "produced_by": [ "EchoNext scoring of a routine ECG", "EHR-integrated advisory to the primary team", "Cardiology consultation and echocardiography" ], "must_hold": [ "Model deployed with alerts in the health record", "Clinicians act on the advisory before surgery" ], "echoes": [ { "date": "2025-07-16", "setting": "Prospective evaluation in the development paper: 73% of patients flagged high-risk had newly diagnosed disease", "model_version": null, "outcome": "recurred", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12328201/" } ] }, "paired_incident": null, "headline": "EchoNext ECG flag finds severe aortic stenosis, leads to TAVR", "metric": "echo showed LVEF 40% and severe aortic stenosis", "comparator": { "against": "cardiologists reading the same ECGs without AI assistance (1,600 reviews of 150 ECGs)", "before": "the accuracy of the cardiologists was 64.0% (95% CI 61.6–66.4%) with sensitivity 61.1% (95% CI 57.3–64.8%) and specificity 66.1% (95% CI 63.0–69.1%)", "after": "an accuracy of 77.3%, sensitivity 72.6% and specificity of 80.7%", "basis": "measured", "quote": "In the set of 150 ECGs, the EchoNext model had an accuracy of 77.3%, sensitivity 72.6% and specificity of 80.7%.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12328201/" }, "later": { "status": "scaled", "date": "2026-06-23", "note": "In June 2026 NewYork-Presbyterian announced that EchoNext, commercialized by the spinout Pathway Labs, had received FDA clearance for AI detection of structural heart disease from ECGs, allowing broader deployment beyond the Columbia pilot.", "quote": "EchoNext is the world's first FDA-approved AI detection tool that analyzes electrocardiograms (ECGs) to detect high-risk structural heart disease.", "url": "https://www.nyp.org/news/ai-tool-developed-at-newyork-presbyterian-and-columbia-to-detect-hidden-heart-disease-receives-fda-approval" }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the model version from the cited sources.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12328201/; https://www.nyp.org/news/ai-tool-developed-at-newyork-presbyterian-and-columbia-to-detect-hidden-heart-disease-receives-fda-approval; https://pmc.ncbi.nlm.nih.gov/articles/PMC12328201/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Columbia_University; https://en.wikipedia.org/wiki/Columbia_University_Irving_Medical_Center", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The makers' claim", "claim_basis": "Sources: the makers' peer-reviewed study, the makers' case report and the makers' press release. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0269", "catalog_version": "1.0.0", "source_fingerprint": "ded723cc3c3b65962632c70b0673f5cffda0c064d6cc9a402c53108b5cfbc366", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0269-source-0001", "AIBD-0269-source-0002", "AIBD-0269-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Columbia University (Poterucha, Elias and colleagues)", "deployer": "Columbia University Irving Medical Center / NewYork-Presbyterian" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The EHR advisory from EchoNext prompted the echocardiogram in an asymptomatic patient, according to the developers' own case report. 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(JACC: Case Reports, 4 March 2026) describe an 84-year-old woman with hypertension. She was admitted after a fall with a left femur fracture. Her electrocardiogram (ECG) was analyzed by EchoNext, a deep-learning model for structural heart disease developed at Columbia. The score was 0.6094; the model's high-risk cutoff is 0.6. The score triggered an electronic health record (EHR) advisory. Transthoracic echocardiography showed an ejection fraction of 40% and severe aortic stenosis. The valve area was 0.6 cm2, peak velocity 3.9 m/s and mean gradient 34 mm Hg. Cardiology recommended cardiac anesthesia for the orthopedic operation. The patient then underwent transcatheter aortic valve replacement (TAVR), on day 8. The authors call it the first report of an AI-ECG algorithm detecting undiagnosed severe aortic stenosis that led to TAVR. EchoNext's development paper (Poterucha et al., Nature, July 2025) reports training on more than 1 million ECG and imaging records. It reports that 73% of patients flagged high-risk in prospective evaluation had newly diagnosed disease. All authors are the Columbia developers. Dr Elias reports research support including from Edwards.", "benefit_type": "Improved Physical Health" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "Severe aortic stenosis found before non-cardiac surgery, changing perioperative management and leading to TAVR." }, { "group": "organizational", "reason": "No organizational outcome reported for this case." }, { "group": "societal", "reason": "Single case." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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(JACC: Case Reports, 4 March 2026) describe an 84-year-old woman with hypertension. She was admitted after a fall with a left femur fracture. Her electrocardiogram (ECG) was analyzed by EchoNext, a deep-learning model for structural heart disease developed at Columbia. The score was 0.6094; the model's high-risk cutoff is 0.6. The score triggered an electronic health record (EHR) advisory. Transthoracic echocardiography showed an ejection fraction of 40% and severe aortic stenosis. The valve area was 0.6 cm2, peak velocity 3.9 m/s and mean gradient 34 mm Hg. Cardiology recommended cardiac anesthesia for the orthopedic operation. The patient then underwent transcatheter aortic valve replacement (TAVR), on day 8. The authors call it the first report of an AI-ECG algorithm detecting undiagnosed severe aortic stenosis that led to TAVR. EchoNext's development paper (Poterucha et al., Nature, July 2025) reports training on more than 1 million ECG and imaging records. 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(JACC: Case Reports, 4 March 2026) describe an 84-year-old woman with hypertension. She was admitted after a fall with a left femur fracture. Her electrocardiogram (ECG) was analyzed by EchoNext, a deep-learning model for structural heart disease developed at Columbia. The score was 0.6094; the model's high-risk cutoff is 0.6. The score triggered an electronic health record (EHR) advisory. Transthoracic echocardiography showed an ejection fraction of 40% and severe aortic stenosis. The valve area was 0.6 cm2, peak velocity 3.9 m/s and mean gradient 34 mm Hg. Cardiology recommended cardiac anesthesia for the orthopedic operation. The patient then underwent transcatheter aortic valve replacement (TAVR), on day 8. The authors call it the first report of an AI-ECG algorithm detecting undiagnosed severe aortic stenosis that led to TAVR. EchoNext's development paper (Poterucha et al., Nature, July 2025) reports training on more than 1 million ECG and imaging records. 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