A Muscari1, G M Puddu, N Santoro, M Zoli. 1. Department of Internal Medicine, Aging and Nephrological Diseases, Stroke Unit, University of Bologna, Italy. antonio.muscari@unibo.it
Abstract
OBJECTIVES: According to most existing models, a computer is usually needed for predicting stroke outcome. Our purpose was to construct a simple and reliable prognostic scale not requiring the use of a calculating machine. MATERIALS AND METHODS: The scale [the Bologna Outcome Algorithm for Stroke (BOAS)] was obtained in 221 patients with ischemic stroke not undergoing thrombolysis and was then validated in a test group of 100 different patients. Outcome was assessed at 9 months as the number of dependent or dead patients (modified Rankin scale - mRS > 2). RESULTS: By a preliminary systematic univariate analysis, 25 of 415 baseline variables were found to be associated with a mRS > 2 independently of stroke severity and age. Subsequent multivariable analyses led to a final model based on five dichotomous risk factors (RF): National Institutes of Health Stroke Scale score ≥10, age ≥78, need of urinary catheter, oxygen administration, and persistence of upper limb paralysis at discharge from stroke unit. The patients with two or more RF (53%) had a mRS > 2 in 91% of cases and were dead in 42% of cases. With 0-1 RF, the two percentages were 24% and 2%, respectively (overall accuracy of prediction 83.9%, area under ROC curve [AUC] 0.891). In the test group, the accuracy was 79.0% and the AUC was 0.839. CONCLUSIONS: The need of urinary catheter, oxygen administration, and persistence of upper limb paralysis, together with stroke severity and advanced age, allow a simple and accurate prediction of dependency or death after ischemic stroke.
OBJECTIVES: According to most existing models, a computer is usually needed for predicting stroke outcome. Our purpose was to construct a simple and reliable prognostic scale not requiring the use of a calculating machine. MATERIALS AND METHODS: The scale [the Bologna Outcome Algorithm for Stroke (BOAS)] was obtained in 221 patients with ischemic stroke not undergoing thrombolysis and was then validated in a test group of 100 different patients. Outcome was assessed at 9 months as the number of dependent or dead patients (modified Rankin scale - mRS > 2). RESULTS: By a preliminary systematic univariate analysis, 25 of 415 baseline variables were found to be associated with a mRS > 2 independently of stroke severity and age. Subsequent multivariable analyses led to a final model based on five dichotomous risk factors (RF): National Institutes of Health Stroke Scale score ≥10, age ≥78, need of urinary catheter, oxygen administration, and persistence of upper limb paralysis at discharge from stroke unit. The patients with two or more RF (53%) had a mRS > 2 in 91% of cases and were dead in 42% of cases. With 0-1 RF, the two percentages were 24% and 2%, respectively (overall accuracy of prediction 83.9%, area under ROC curve [AUC] 0.891). In the test group, the accuracy was 79.0% and the AUC was 0.839. CONCLUSIONS: The need of urinary catheter, oxygen administration, and persistence of upper limb paralysis, together with stroke severity and advanced age, allow a simple and accurate prediction of dependency or death after ischemic stroke.
Authors: Jane M Maguire; Steve Bevan; Tara M Stanne; Erik Lorenzen; Israel Fernandez-Cadenas; Graeme J Hankey; Jordi Jimenez-Conde; Katarina Jood; Jin-Moo Lee; Robin Lemmens; Christopher Levi; Bo Norrving; Kristiina Rannikmae; Natalia Rost; Jonathan Rosand; Peter M Rothwell; Rodney Scott; Daniel Strbian; Jonathan Sturm; Cathie Sudlow; Matthew Traylor; Vincent Thijs; Turgut Tatlisumak; Tadeusz Wieloch; Daniel Woo; Bradford B Worrall; Christina Jern; Arne Lindgren Journal: Eur Stroke J Date: 2017-04-19
Authors: Douglas D Thompson; Gordon D Murray; Cathie L M Sudlow; Martin Dennis; William N Whiteley Journal: PLoS One Date: 2014-10-09 Impact factor: 3.240
Authors: Antonio Muscari; Andrea Bonfiglioli; Donatella Magalotti; Giovanni M Puddu; Veronica Zorzi; Marco Zoli Journal: Brain Behav Date: 2016-04-27 Impact factor: 2.708