Literature DB >> 33598557

A nomogram to predict unfavourable outcome in patients receiving oral anticoagulants for atrial fibrillation after stroke.

Manuel Cappellari1, David J Seiffge2,3,4, Masatoshi Koga5, Maurizio Paciaroni6, Stefano Forlivesi1, Gianni Turcato7, Paolo Bovi1, Sohei Yoshimura5, Kanta Tanaka5, Masayuki Shiozawa5, Takeshi Yoshimoto8, Kaori Miwa5,8, Masahito Takagi5, Manabu Inoue5, Hiroshi Yamagami5, Valeria Caso6, Georgios Tsivgoulis9,10, Michele Venti6, Monica Acciarresi6, Andrea Alberti6, Danilo Toni11, Alexandros Polymeris2, Bruno Bonetti1, Giancarlo Agnelli6, Kazunori Toyoda5, Stefan T Engelter2,12, Gian Marco De Marchis2.   

Abstract

INTRODUCTION: It is unknown whether the type of treatment (direct oral anticoagulant versus vitamin K antagonist) and the time of treatment introduction (early versus late) may affect the functional outcome in stroke patients with atrial fibrillation. We aimed to develop and validate a nomogram model including direct oral anticoagulant/vitamin K antagonist and early/late oral anticoagulant introduction for predicting the probability of unfavourable outcome after stroke in atrial fibrillation-patients. PATIENTS AND METHODS: We conducted an individual patient data analysis of four prospective studies. Unfavourable functional outcome was defined as three-month modified Rankin Scale score 3 -6. To generate the nomogram, five independent predictors including age (<65 years, reference; 65--79; or 80), National Institutes of Health Stroke Scale score (0--5 points, reference; 6--15; 16--25; or >25), acute revascularisation treatments (yes, reference, or no), direct oral anticoagulant (reference) or vitamin K antagonist, and early (7 days, reference) or late (8--30) anticoagulant introduction entered into a final logistic regression model. The discriminative performance of the model was assessed by using the area under the receiver operating characteristic curve.
RESULTS: A total of 2102 patients with complete data for generating the nomogram was randomly dichotomised into training (n = 1553) and test (n = 549) sets. The area under the receiver operating characteristic curve was 0.822 (95% confidence interval, CI: 0.800--0.844) in the training set and 0.803 (95% CI: 0.764--0.842) in the test set. The model was adequately calibrated (9.852; p = 0.276 for the Hosmer--Lemeshow test). DISCUSSION AND
CONCLUSION: Our nomogram is the first model including type of oral anticoagulant and time of treatment introduction to predict the probability of three-month unfavourable outcome in a large multicentre cohort of stroke patients with atrial fibrillation. © European Stroke Organisation 2020.

Entities:  

Keywords:  Stroke; atrial fibrillation; direct oral anticoagulant; nomogram; outcome; vitamin K antagonist

Year:  2020        PMID: 33598557      PMCID: PMC7856583          DOI: 10.1177/2396987320945840

Source DB:  PubMed          Journal:  Eur Stroke J        ISSN: 2396-9873


  18 in total

1.  Rivaroxaban versus warfarin in nonvalvular atrial fibrillation.

Authors:  Manesh R Patel; Kenneth W Mahaffey; Jyotsna Garg; Guohua Pan; Daniel E Singer; Werner Hacke; Günter Breithardt; Jonathan L Halperin; Graeme J Hankey; Jonathan P Piccini; Richard C Becker; Christopher C Nessel; John F Paolini; Scott D Berkowitz; Keith A A Fox; Robert M Califf
Journal:  N Engl J Med       Date:  2011-08-10       Impact factor: 91.245

2.  The iScore predicts effectiveness of thrombolytic therapy for acute ischemic stroke.

Authors:  Gustavo Saposnik; Jiming Fang; Moira K Kapral; Jack V Tu; Muhammad Mamdani; Peter Austin; S Claiborne Johnston
Journal:  Stroke       Date:  2012-02-03       Impact factor: 7.914

3.  An integer-based score to predict functional outcome in acute ischemic stroke: the ASTRAL score.

Authors:  G Ntaios; M Faouzi; J Ferrari; W Lang; K Vemmos; P Michel
Journal:  Neurology       Date:  2012-05-30       Impact factor: 9.910

4.  Predicting outcome of IV thrombolysis-treated ischemic stroke patients: the DRAGON score.

Authors:  D Strbian; A Meretoja; F J Ahlhelm; J Pitkäniemi; P Lyrer; M Kaste; S Engelter; T Tatlisumak
Journal:  Neurology       Date:  2012-02-07       Impact factor: 9.910

5.  Antithrombotic treatment for secondary prevention of stroke and other thromboembolic events in patients with stroke or transient ischemic attack and non-valvular atrial fibrillation: A European Stroke Organisation guideline.

Authors:  Catharina Jm Klijn; Maurizio Paciaroni; Eivind Berge; Eleni Korompoki; Janika Kõrv; Avtar Lal; Jukka Putaala; David J Werring
Journal:  Eur Stroke J       Date:  2019-04-09

6.  Apixaban versus warfarin in patients with atrial fibrillation.

Authors:  Christopher B Granger; John H Alexander; John J V McMurray; Renato D Lopes; Elaine M Hylek; Michael Hanna; Hussein R Al-Khalidi; Jack Ansell; Dan Atar; Alvaro Avezum; M Cecilia Bahit; Rafael Diaz; J Donald Easton; Justin A Ezekowitz; Greg Flaker; David Garcia; Margarida Geraldes; Bernard J Gersh; Sergey Golitsyn; Shinya Goto; Antonio G Hermosillo; Stefan H Hohnloser; John Horowitz; Puneet Mohan; Petr Jansky; Basil S Lewis; Jose Luis Lopez-Sendon; Prem Pais; Alexander Parkhomenko; Freek W A Verheugt; Jun Zhu; Lars Wallentin
Journal:  N Engl J Med       Date:  2011-08-27       Impact factor: 91.245

7.  Stroke Prognostication using Age and NIH Stroke Scale: SPAN-100.

Authors:  Gustavo Saposnik; Amy K Guzik; Mathew Reeves; Bruce Ovbiagele; S Claiborne Johnston
Journal:  Neurology       Date:  2012-11-21       Impact factor: 9.910

8.  Edoxaban versus warfarin in patients with atrial fibrillation.

Authors:  Robert P Giugliano; Christian T Ruff; Eugene Braunwald; Sabina A Murphy; Stephen D Wiviott; Jonathan L Halperin; Albert L Waldo; Michael D Ezekowitz; Jeffrey I Weitz; Jindřich Špinar; Witold Ruzyllo; Mikhail Ruda; Yukihiro Koretsune; Joshua Betcher; Minggao Shi; Laura T Grip; Shirali P Patel; Indravadan Patel; James J Hanyok; Michele Mercuri; Elliott M Antman
Journal:  N Engl J Med       Date:  2013-11-19       Impact factor: 91.245

9.  Admission hyperglycemia predicts a worse outcome in stroke patients treated with intravenous thrombolysis.

Authors:  Alexandre Y Poppe; Sumit R Majumdar; Thomas Jeerakathil; William Ghali; Alastair M Buchan; Michael D Hill
Journal:  Diabetes Care       Date:  2009-01-08       Impact factor: 17.152

Review 10.  Effect of treatment delay, age, and stroke severity on the effects of intravenous thrombolysis with alteplase for acute ischaemic stroke: a meta-analysis of individual patient data from randomised trials.

Authors:  Jonathan Emberson; Kennedy R Lees; Patrick Lyden; Lisa Blackwell; Gregory Albers; Erich Bluhmki; Thomas Brott; Geoff Cohen; Stephen Davis; Geoffrey Donnan; James Grotta; George Howard; Markku Kaste; Masatoshi Koga; Ruediger von Kummer; Maarten Lansberg; Richard I Lindley; Gordon Murray; Jean Marc Olivot; Mark Parsons; Barbara Tilley; Danilo Toni; Kazunori Toyoda; Nils Wahlgren; Joanna Wardlaw; William Whiteley; Gregory J del Zoppo; Colin Baigent; Peter Sandercock; Werner Hacke
Journal:  Lancet       Date:  2014-08-05       Impact factor: 79.321

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  2 in total

1.  Nomogram to predict hemorrhagic transformation for acute ischemic stroke in Western China: a retrospective analysis.

Authors:  Keming Zhang; Jianfang Luan; Changqing Li; Mingli Chen
Journal:  BMC Neurol       Date:  2022-04-26       Impact factor: 2.903

2.  Practical "1-2-3-4-Day" Rule for Starting Direct Oral Anticoagulants After Ischemic Stroke With Atrial Fibrillation: Combined Hospital-Based Cohort Study.

Authors:  Shunsuke Kimura; Kazunori Toyoda; Sohei Yoshimura; Kazuo Minematsu; Masahiro Yasaka; Maurizio Paciaroni; David J Werring; Hiroshi Yamagami; Takehiko Nagao; Shinichi Yoshimura; Alexandros Polymeris; Annaelle Zietz; Stefan T Engelter; Bernd Kallmünzer; Manuel Cappellari; Tetsuya Chiba; Takeshi Yoshimoto; Masayuki Shiozawa; Takanari Kitazono; Masatoshi Koga
Journal:  Stroke       Date:  2022-02-02       Impact factor: 10.170

  2 in total

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