*Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom. †Departments of Anesthesia and Clinical Epidemiology and Biostatistics, Michael DeGroote School of Medicine, Faculty of Health Sciences, McMaster University, Population Health Research Institute, Hamilton, Ontario, Canada. .
[Show abstract][Hide abstract] ABSTRACT: Background
To determine whether the predictions of functional outcome after ischemic stroke made at the bedside using a doctor’s clinical experience were more or less accurate than the predictions made by clinical prediction models (CPMs).
Methods and Findings
A prospective cohort study of nine hundred and thirty one ischemic stroke patients recruited consecutively at the outpatient, inpatient and emergency departments of the Western General Hospital, Edinburgh between 2002 and 2005. Doctors made informal predictions of six month functional outcome on the Oxford Handicap Scale (OHS). Patients were followed up at six months with a validated postal questionnaire. For each patient we calculated the absolute predicted risk of death or dependence (OHS≥3) using five previously described CPMs. The specificity of a doctor’s informal predictions of OHS≥3 at six months was good 0.96 (95% CI: 0.94 to 0.97) and similar to CPMs (range 0.94 to 0.96); however the sensitivity of both informal clinical predictions 0.44 (95% CI: 0.39 to 0.49) and clinical prediction models (range 0.38 to 0.45) was poor. The prediction of the level of disability after stroke was similar for informal clinical predictions (ordinal c-statistic 0.74 with 95% CI 0.72 to 0.76) and CPMs (range 0.69 to 0.75). No patient or clinician characteristic affected the accuracy of informal predictions, though predictions were more accurate in outpatients.
CPMs are at least as good as informal clinical predictions in discriminating between good and bad functional outcome after ischemic stroke. The place of these models in clinical practice has yet to be determined.
PLoS ONE 10/2014; 9(10):e110189. DOI:10.1371/journal.pone.0110189 · 3.23 Impact Factor
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