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

  • Mehta, Vishal S.Kings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London, England.;Guys & St Thomas Hosp, Cardiol Dept, London, England. (författare)

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

  • Artikel/kapitelEngelska2022

Förlag, utgivningsår, omfång ...

  • Elsevier,2022
  • electronicrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:oai:DiVA.org:uu-497099
  • https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-497099URI
  • https://doi.org/10.1016/j.hrthm.2021.12.036DOI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:ref swepub-contenttype
  • Ämneskategori:art swepub-publicationtype

Anmärkningar

  • 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 och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • O'Brien, HughKings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London, England. (författare)
  • Elliott, Mark K.Kings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London, England.;Guys & St Thomas Hosp, Cardiol Dept, London, England. (författare)
  • Wijesuriya, NadeevKings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London, England.;Guys & St Thomas Hosp, Cardiol Dept, London, England. (författare)
  • Auricchio, AngeloFdn Cardioctr Ticino, Div Cardiol, Lugano, Switzerland. (författare)
  • Ayis, SalmaKings Coll London, Sch Populat Hlth & Environm Sci, London, England. (författare)
  • Blomström-Lundqvist, CarinaUppsala universitet,Kardiologi-arrytmi(Swepub:uu)carinabl (författare)
  • Bongiorni, Maria GraziaAzienda Osped Univ Pisana, Cardiol Dept, Direttore UO Cardiol 2 SSN, Pisa, Italy. (författare)
  • Butter, ChristianHeart Ctr Brandenburg Bernau Berlin, Dept Cardiol, Bernau, Germany.;Brandenburg Med Sch, Bernau, Germany. (författare)
  • Deharo, Jean-ClaudeCHU La Timone, Serv Prof Deharo, Cardiol, Dept Cardiol, Marseille, France. (författare)
  • Gould, JustinKings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London, England.;Guys & St Thomas Hosp, Cardiol Dept, London, England. (författare)
  • Kennergren, CharlesSahlgrens Univ Hosp, Dept Cardiothorac Surg, Sahlgrenska SU, Gothenburg, Sweden. (författare)
  • Kuck, Karl-HeinzAsklepios Klin St Georg, Dept Cardiol, Hamburg, Germany. (författare)
  • Kutarski, AndrzejMed Univ Lublin, Dept Cardiol, Lublin, Poland. (författare)
  • Leclercq, ChristopheDept Ordensklinikum Linz Elisabethinen, Linz, Austria. (författare)
  • Maggioni, Aldo P.Maria Cecilia Hosp, GVM Care & Res, Cotignola, Italy.;European Soc Cardiol, EORP, Biot, Sophia Antipoli, France. (författare)
  • Sidhu, Baldeep S.Kings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London, England.;Guys & St Thomas Hosp, Cardiol Dept, London, England. (författare)
  • Wong, TomRoyal Brompton & Harefield Natl Hlth Serv Fdn Tru, London, England.;Imperial Coll London, Natl Heart & Lung Inst, London, England. (författare)
  • Niederer, StevenKings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London, England. (författare)
  • Rinaldi, Christopher A.Kings Coll London, Sch Biomed Engn & Imaging Sci, St Thomas Hosp, London, England.;Guys & St Thomas Hosp, Cardiol Dept, London, England. (författare)
  • 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)

Sammanhörande titlar

  • Ingår i:Heart Rhythm: Elsevier19:6, s. 885-8931547-52711556-3871

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