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Sökning: L773:1556 3871 OR L773:1547 5271 > (2020-2024) > Machine learning-de...

Machine learning-derived major adverse event prediction of patients undergoing transvenous lead extraction : Using the ESC EHRA EORP European lead extraction ConTRolled ELECTRa registry

Mehta, Vishal S. (författare)
Kings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London, England.;Guys & St Thomas Hosp, Cardiol Dept, London, England.
O'Brien, Hugh (författare)
Kings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London, England.
Elliott, Mark K. (författare)
Kings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London, England.;Guys & St Thomas Hosp, Cardiol Dept, London, England.
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Wijesuriya, Nadeev (författare)
Kings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London, England.;Guys & St Thomas Hosp, Cardiol Dept, London, England.
Auricchio, Angelo (författare)
Fdn Cardioctr Ticino, Div Cardiol, Lugano, Switzerland.
Ayis, Salma (författare)
Kings Coll London, Sch Populat Hlth & Environm Sci, London, England.
Blomström-Lundqvist, Carina (författare)
Uppsala universitet,Kardiologi-arrytmi
Bongiorni, Maria Grazia (författare)
Azienda Osped Univ Pisana, Cardiol Dept, Direttore UO Cardiol 2 SSN, Pisa, Italy.
Butter, Christian (författare)
Heart Ctr Brandenburg Bernau Berlin, Dept Cardiol, Bernau, Germany.;Brandenburg Med Sch, Bernau, Germany.
Deharo, Jean-Claude (författare)
CHU La Timone, Serv Prof Deharo, Cardiol, Dept Cardiol, Marseille, France.
Gould, Justin (författare)
Kings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London, England.;Guys & St Thomas Hosp, Cardiol Dept, London, England.
Kennergren, Charles (författare)
Sahlgrens Univ Hosp, Dept Cardiothorac Surg, Sahlgrenska SU, Gothenburg, Sweden.
Kuck, Karl-Heinz (författare)
Asklepios Klin St Georg, Dept Cardiol, Hamburg, Germany.
Kutarski, Andrzej (författare)
Med Univ Lublin, Dept Cardiol, Lublin, Poland.
Leclercq, Christophe (författare)
Dept Ordensklinikum Linz Elisabethinen, Linz, Austria.
Maggioni, Aldo P. (författare)
Maria Cecilia Hosp, GVM Care & Res, Cotignola, Italy.;European Soc Cardiol, EORP, Biot, Sophia Antipoli, France.
Sidhu, Baldeep S. (författare)
Kings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London, England.;Guys & St Thomas Hosp, Cardiol Dept, London, England.
Wong, Tom (författare)
Royal Brompton & Harefield Natl Hlth Serv Fdn Tru, London, England.;Imperial Coll London, Natl Heart & Lung Inst, London, England.
Niederer, Steven (författare)
Kings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London, England.
Rinaldi, Christopher A. (författare)
Kings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London, England.;Guys & St Thomas Hosp, Cardiol Dept, London, England.
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Kings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London, England;Guys & St Thomas Hosp, Cardiol Dept, London, England. Kings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London, England. (creator_code:org_t)
Elsevier, 2022
2022
Engelska.
Ingår i: Heart Rhythm. - : Elsevier. - 1547-5271 .- 1556-3871. ; 19:6, s. 885-893
Relaterad länk:
https://doi.org/10.1...
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https://uu.diva-port... (primary) (Raw object)
https://urn.kb.se/re...
https://doi.org/10.1...
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  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • BACKGROUND Transvenous lead extraction (TLE) remains a high-risk procedure. OBJECTIVE The purpose of this study was to develop a machine learning (ML)-based risk stratification system to predict the risk of major adverse events (MAEs) after TLE. A MAE was defined as procedure-related major complication and procedure-related death. METHODS We designed and evaluated an ML-based risk stratification system trained using the European Lead Extraction ConTRolled (ELECTRa) registry to predict the risk of MAEs in 3555 patients undergoing TLE and tested this on an independent registry of 1171 patients. ML models were developed, including a self-normalizing neural network (SNN), stepwise logistic regression model ("stepwise model"), support vector machines, and random forest model. These were compared with the ELECTRa Registry Outcome Score (EROS) for MAEs. RESULTS There were 53 MAEs (1.7%) in the training cohort and 24 (2.4%) in the test cohort. Thirty-two clinically important features were used to train the models. ML techniques were similar to EROS by balanced accuracy (stepwise model: 0.74 vs EROS: 0.70) and superior by area under the curve (support vector machines: 0.764 vs EROS: 0.677). The SNN provided a finite risk for MAE and accurately identified MAE in 14 of 169 "high (<80%) risk" patients (8.3%) and no MAEs in all 198 "low (,20%) risk" patients (100%). CONCLUSION ML models incrementally improved risk prediction for identifying those at risk of MAEs. The SNN has the additional advantage of providing a personalized finite risk assessment for patients. This may aid patient decision making and allow better preoperative risk assessment and resource allocation.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Kardiologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)

Nyckelord

Machine learning
Artificial intelligence
Transvenous lead extraction
Outcomes
Risk stratification
ELECTRa registry

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