Jinju Guk1,2, Dongwoo Chae1,2, Hankil Son1, Joonsang Yoo3, Ji Hoe Heo4, Kyungsoo Park1. 1. Department of Pharmacology, Yonsei University College of Medicine, Seoul, South Korea. 2. Brain Korea 21 plus Project for Medical Science, Yonsei University, Seoul, South Korea. 3. Department of Neurology, Keimyung University College of Medicine, Daegu, South Korea. 4. Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea.
Abstract
AIMS: Recombinant tissue plasminogen activator (rt-PA) is the only first-line agent approved by the US Food and Drug Administration to treat acute ischaemic stroke. However, it often causes the serious adverse event (AE) of haemorrhagic transformation. The present study developed a pharmacometric model for the rt-PA treatment effect and AE and, using the developed model, proposed a benefit-to-risk ratio assessment scheme as a supportive tool to optimize treatment outcome. METHODS: The data from 336 acute ischaemic stroke patients were used. The treatment effect was assessed based on an improvement in National Institutes of Health Stroke Scale (NIHSS) scores, which were described using an item response theory (IRT)-based disease progression model. Treatment failure and AE probabilities, and their occurrence times, were described by incidence and time-to-event models. Using the developed model, benefit-to-risk ratios were simulated under various scenarios using the global benefit-to-risk trade-off ratio (GBR). RESULTS: High initial NIHSS score and middle cerebral artery (MCA) stroke were risk factors for treatment failure, where the failure rate with MCA stroke was 2.87-fold higher than with non-MCA stroke. The haemorrhagic transformation time was associated with longitudinal changes in NIHSS scores. The benefit-to-risk ratio simulated was highest in minor stroke severity, with GBR >1 in all scenarios, and the ratio with non-MCA stroke was 2-3 fold higher than with MCA stroke. CONCLUSIONS: The study demonstrated the feasibility of applying an IRT model to describing the time course of the rt-PA treatment effect and AE. Benefit-to-risk ratio analyses showed that the treatment was optimal in non-MCA stroke with minor stroke severity.
AIMS: Recombinant tissue plasminogen activator (rt-PA) is the only first-line agent approved by the US Food and Drug Administration to treat acute ischaemic stroke. However, it often causes the serious adverse event (AE) of haemorrhagic transformation. The present study developed a pharmacometric model for the rt-PA treatment effect and AE and, using the developed model, proposed a benefit-to-risk ratio assessment scheme as a supportive tool to optimize treatment outcome. METHODS: The data from 336 acute ischaemic strokepatients were used. The treatment effect was assessed based on an improvement in National Institutes of Health Stroke Scale (NIHSS) scores, which were described using an item response theory (IRT)-based disease progression model. Treatment failure and AE probabilities, and their occurrence times, were described by incidence and time-to-event models. Using the developed model, benefit-to-risk ratios were simulated under various scenarios using the global benefit-to-risk trade-off ratio (GBR). RESULTS: High initial NIHSS score and middle cerebral artery (MCA) stroke were risk factors for treatment failure, where the failure rate with MCA stroke was 2.87-fold higher than with non-MCA stroke. The haemorrhagic transformation time was associated with longitudinal changes in NIHSS scores. The benefit-to-risk ratio simulated was highest in minor stroke severity, with GBR >1 in all scenarios, and the ratio with non-MCA stroke was 2-3 fold higher than with MCA stroke. CONCLUSIONS: The study demonstrated the feasibility of applying an IRT model to describing the time course of the rt-PA treatment effect and AE. Benefit-to-risk ratio analyses showed that the treatment was optimal in non-MCA stroke with minor stroke severity.
Authors: Bijoy K Menon; Jeffrey L Saver; Shyam Prabhakaran; Mathew Reeves; Li Liang; Daiwai M Olson; Eric D Peterson; Adrian F Hernandez; Gregg C Fonarow; Lee H Schwamm; Eric E Smith Journal: Stroke Date: 2012-07-17 Impact factor: 7.914
Authors: Daniel Strbian; Stefan Engelter; Patrik Michel; Atte Meretoja; Lucka Sekoranja; Frank J Ahlhelm; Satu Mustanoja; Igor Kuzmanovic; Tiina Sairanen; Nina Forss; Maria Cordier; Philippe Lyrer; Markku Kaste; Turgut Tatlisumak Journal: Ann Neurol Date: 2012-05 Impact factor: 10.422
Authors: Edward C Jauch; Jeffrey L Saver; Harold P Adams; Askiel Bruno; J J Buddy Connors; Bart M Demaerschalk; Pooja Khatri; Paul W McMullan; Adnan I Qureshi; Kenneth Rosenfield; Phillip A Scott; Debbie R Summers; David Z Wang; Max Wintermark; Howard Yonas Journal: Stroke Date: 2013-01-31 Impact factor: 7.914
Authors: Simon D Harding; Joanna L Sharman; Elena Faccenda; Chris Southan; Adam J Pawson; Sam Ireland; Alasdair J G Gray; Liam Bruce; Stephen P H Alexander; Stephen Anderton; Clare Bryant; Anthony P Davenport; Christian Doerig; Doriano Fabbro; Francesca Levi-Schaffer; Michael Spedding; Jamie A Davies Journal: Nucleic Acids Res Date: 2018-01-04 Impact factor: 16.971