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As stroke clinicians, we are routinely asked by patients and families regarding prognosis. The cognitive process underlying prognostication is complex, poorly understood, and thus can be subjected to cognitive biases colored by our personal experiences. Previous studies suggest that clinicians, even those with expertise in stroke, perform poorly in predicting clinical outcomes. For example, the JURaSSiC study (Clinician Judgment vs Risk Score to Predict Stroke Outcomes) reported that clinicians’ overall accuracy for predicting death or disability at discharge was a staggeringly low (16.9%), and none of the 111 participating clinicians with expertise in stroke care correctly predicted all 5 case outcomes. Similarly, Ntaios et al found that >50% of all estimates made by physicians with an interest in stroke care were inaccurate, and their predictions became even less precise in those patients who received thrombolytic therapy. Interestingly, meteorologists provide more accurate predictions for rain (r: 95%) and poker players for chances of winning a hand (r: 96%). Contrarily, patients and families rely on our lower rated predictions to make critical decisions. As a result, evidence-based prognostic models are necessary to better inform clinicians. To address this need, several prognostic models have been developed to aid prognostication after ischemic stroke (Table). Comparative studies have shown that these prognostic models outperform clinician judgment in predicting stroke outcomes. These prognostic scores were derived from rigorous mathematical modeling based on data from large cohorts of acute stroke patients. While there have been some external validation studies, they were largely conducted in a Western population and in patients recruited before the widespread utilization of reperfusion therapies. Furthermore, new advances are occurring at an unprecedented pace across the continuum of stroke care, from the development of neuroprotective agents, application of extended window reperfusion therapies, to modern stroke rehabilitation technologies. These prognostic models require regular validation and calibration using updated data to retain their usefulness. In the current issue, Matsumoto et al assessed the performance of 6 stroke prognostic scores in 4237 acute ischemic stroke patients hospitalized at a Japanese stroke center between 2012 and 2017. This study adds value to existing literature given its large sample size and relatively modern enrollment period allowing patients who received reperfusion therapies to be included in the cohort. Authors directly compared the performances of 6 point-based stroke prognostic scores against each other and showed that they all performed reasonably well in this real-world population; areas under the receiver operating characteristic curve ranged from 0.69 (Houston Intra-Arterial Recanalization Therapy [HIAT] score) to 0.92 (Preadmission Comorbidities, Level of Consciousness, Age, and Neurological Deficit [PLAN] score) in predicting poor functional outcomes and 0.87 (PLAN) to 0.88 (Ischemic Stroke Predictive Risk Score [Iscore] and Acute Stroke Registry and Analysis of Lausanne [ASTRAL] score) in predicting in-hospital mortality. This current study also explored the promising field of machine learning in the current era of big data. In all data-driven modeling methods, the National Institutes of Health Stroke Scale score, preadmission modified Rankin Scale (mRS), and age emerged as the top 3 factors predicting functional outcomes, validating what clinicians have long known intuitively to be important clinical prognostic indicators. However, the findings by Matsumoto et al may not generalize readily to other stroke populations. The participants were all treated in a single center in Japan, and only 1.5% underwent endovascular therapy. Nonetheless, this study provides valuable external validation of several prognostic scores in an East Asian cohort. Furthermore, stroke patients who do not undergo reperfusion therapy remain the vast majority globally. Another important caveat to consider is that the functional outcomes in this study were recorded at the time of discharge, while original publications of the ASTRAL, Totaled Health Risks in Vascular Events (THRIVE), and Stroke Prognostication Using Age and National Institutes of Health Stroke Scale (SPAN)-100 scores used 90-day outcomes. This discrepancy may underlie the relatively lower performance of these three scores in this cohort as compared with PLAN and IScore, both of which were originally derived using functional data at discharge. There is a growing body of literature examining the utility of machine learning for both diagnosis and prognosis in a The opinions expressed in this article are not necessarily those of the editors or of the American Heart Association. From the Division of Neurology, Toronto Western Hospital (M.M.G.), Decision Neuroscience Unit, Li Ka Shing Institute (G.S.), and Stroke Program, Division of Neurology, Department of Medicine, St. Michael’s Hospital (M.M.G., J.W., G.S.), University of Toronto, Canada; and Division of Stroke and Cerebrovascular Disease, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA (J.W.). *Drs Gao and Wang contributed equally. Correspondence to Gustavo Saposnik, MD, FRCPC, Division of Neurology, Department of Medicine, St. Michael’s Hospital, University of Toronto, 55 Queen St E, Toronto, Ontario M5C 1R6. Email gustavo. [email protected] The Art and Science of Stroke Outcome Prognostication