Clotilde Balucani1, Riccardo Bianchi2, Edward Feldmann2, Jeremy Weedon2, Dmitri Kolychev2, Steven R Levine2. 1. From the Department of Neurology and Stroke Center (C.B., S.R.L.), Department of Physiology and Pharmacology (R.B.), Department of Scientific Computing (J.W.), and Department of Emergency Medicine (S.R.L.), The State University of New York (SUNY) Downstate Medical Center, Brooklyn; Department of Neurology, TUFTS Medical Center, Boston, MA (E.F., D.K.); and Department of Neurology, The Kings County Hospital Center, Brooklyn, NY (S.R.L.). clotilde.balucani@downstate.edu. 2. From the Department of Neurology and Stroke Center (C.B., S.R.L.), Department of Physiology and Pharmacology (R.B.), Department of Scientific Computing (J.W.), and Department of Emergency Medicine (S.R.L.), The State University of New York (SUNY) Downstate Medical Center, Brooklyn; Department of Neurology, TUFTS Medical Center, Boston, MA (E.F., D.K.); and Department of Neurology, The Kings County Hospital Center, Brooklyn, NY (S.R.L.).
Abstract
BACKGROUND AND PURPOSE: Minor strokes and rapidly improving stroke symptoms are frequent exclusions for intravenous tissue-type plasminogen activator. We explored factors influencing tissue-type plasminogen activator treatment decision for minor strokes/rapidly improving stroke symptoms. METHODS: A pilot survey, including 110 case scenarios, was completed by 17 clinicians from 2 academic medical centers. Respondents were asked whether they would treat each case with tissue-type plasminogen activator at 60 minutes after emergency department admission. Cases varied by (1) National Institutes of Health Stroke Scale score at treatment decision time, (2) symptom pattern over time (improvement or worsening and then improving), (3) type of neurological deficit (3 main domains: motor, visual/sensory/ataxia, and language/neglect), and (4) age/occupation (4 profiles). Logistic regression was used to predict probability of omission (pO). A binomial regression model was used to predict probability of treatment decision. RESULTS: Predicted probability of treatment decision was affected by National Institutes of Health Stroke Scale score (P<0.001) and age/occupation profiles (P<0.001) but not by symptom patterns (P=0.334). There were significant, albeit modest, main effects on probability of treatment decision for neurological domains. Responses were most likely omitted (P=0.027) for cases improvement pattern and language/neglect domain (pO=0.74; 95% confidence interval, 0.52-0.89) and with visual/sensory/ataxia domain (pO=0.74; confidence interval, 0.37-0.93) when compared with improvement pattern and motor domain (pO=0.17; confidence interval, 0.06-0.42) and to any worsening and then improving patterns (0.37<pO<0.56). CONCLUSIONS: This pilot survey provides the first quantitative evidence that National Institutes of Health Stroke Scale score is not the only determinant of treatment decision. A National Institutes of Health Stroke Scale score of 2 is the potential equipoise point, with the least consensus on treatment decision. These preliminary findings require validation in larger population surveys.
BACKGROUND AND PURPOSE:Minor strokes and rapidly improving stroke symptoms are frequent exclusions for intravenous tissue-type plasminogen activator. We explored factors influencing tissue-type plasminogen activator treatment decision for minor strokes/rapidly improving stroke symptoms. METHODS: A pilot survey, including 110 case scenarios, was completed by 17 clinicians from 2 academic medical centers. Respondents were asked whether they would treat each case with tissue-type plasminogen activator at 60 minutes after emergency department admission. Cases varied by (1) National Institutes of Health Stroke Scale score at treatment decision time, (2) symptom pattern over time (improvement or worsening and then improving), (3) type of neurological deficit (3 main domains: motor, visual/sensory/ataxia, and language/neglect), and (4) age/occupation (4 profiles). Logistic regression was used to predict probability of omission (pO). A binomial regression model was used to predict probability of treatment decision. RESULTS: Predicted probability of treatment decision was affected by National Institutes of Health Stroke Scale score (P<0.001) and age/occupation profiles (P<0.001) but not by symptom patterns (P=0.334). There were significant, albeit modest, main effects on probability of treatment decision for neurological domains. Responses were most likely omitted (P=0.027) for cases improvement pattern and language/neglect domain (pO=0.74; 95% confidence interval, 0.52-0.89) and with visual/sensory/ataxia domain (pO=0.74; confidence interval, 0.37-0.93) when compared with improvement pattern and motor domain (pO=0.17; confidence interval, 0.06-0.42) and to any worsening and then improving patterns (0.37<pO<0.56). CONCLUSIONS: This pilot survey provides the first quantitative evidence that National Institutes of Health Stroke Scale score is not the only determinant of treatment decision. A National Institutes of Health Stroke Scale score of 2 is the potential equipoise point, with the least consensus on treatment decision. These preliminary findings require validation in larger population surveys.
Authors: Pooja Khatri; Dawn O Kleindorfer; Sharon D Yeatts; Jeffrey L Saver; Steven R Levine; Patrick D Lyden; Charles J Moomaw; Yuko Y Palesch; Edward C Jauch; Joseph P Broderick Journal: Stroke Date: 2010-09-02 Impact factor: 7.914
Authors: Eric E Smith; Gregg C Fonarow; Mathew J Reeves; Margueritte Cox; Daiwai M Olson; Adrian F Hernandez; Lee H Schwamm Journal: Stroke Date: 2011-09-08 Impact factor: 7.914
Authors: P W Duncan; G P Samsa; M Weinberger; L B Goldstein; A Bonito; D M Witter; C Enarson; D Matchar Journal: Stroke Date: 1997-04 Impact factor: 7.914
Authors: V Rajajee; C Kidwell; S Starkman; B Ovbiagele; J R Alger; P Villablanca; F Vinuela; G Duckwiler; R Jahan; A Fredieu; S Suzuki; J L Saver Journal: Neurology Date: 2006-09-26 Impact factor: 9.910
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: Mandip S Dhamoon; Yeseon Park Moon; Myunghee C Paik; Bernadette Boden-Albala; Tatjana Rundek; Ralph L Sacco; Mitchell S V Elkind Journal: Stroke Date: 2009-06-25 Impact factor: 7.914
Authors: J Pfaff; C Herweh; M Pham; S Schönenberger; S Nagel; P A Ringleb; M Bendszus; M Möhlenbruch Journal: AJNR Am J Neuroradiol Date: 2016-06-30 Impact factor: 3.825
Authors: Ava L Liberman; Daniel Pinto; Sara K Rostanski; Daniel L Labovitz; Andrew M Naidech; Shyam Prabhakaran Journal: Neurohospitalist Date: 2018-09-13