It Is Difficult to Make Predictions, Especially About the Future

Abstract

Physicists, politicians, poets, and punters understand the pitfalls of predicting the future. Similarly, predicting outcomes after stroke rehabilitation can be difficult when based on clinical impression, and several approaches to combining key variables in predictive models have been developed. [1][2][3] In this issue, Scrutinio et al 4 introduce a predictive model of functional outcome after stroke based on retrospective data from several hundred patients who were treated at the Maugeri rehabilitation centers between 2002 and 2015. Their primary binary logistic model predicts the probability of a patient having mild disability at discharge from inpatient rehabilitation, defined as a score of ≥61 on the motor component of the functional independence scale (M-FIM; maximum score 91). A second model predicts the probability of a patient requiring no more than supervision in activities of daily living at discharge. Both models use expected predictors such as age, sex, and FIM scores on admission to rehabilitation and perform well with areas under the curve around 0.80. The authors have taken a further step by creating an online tool that allows them to calculate the probability that each of their patients will achieve each of these outcomes. In their study sample, the probability of achieving an M-FIM score of at least 61 points is <20% for 60% of patients, >80% for 20% of patients, and around chance for the remaining 20% of patients. The probability of requiring no more than supervision is <20% for 80% of patients.The strengths of the study include the large sample of patients most of whom had moderate-to-severe disability on admission to rehabilitation, rigorous statistical analyses, and the use of independent data sets to derive and validate their models. The authors acknowledge some limitations, including the retrospective nature of the study and differences between the rehabilitation service at their centers compared with other settings. The latter is particularly important. The median time poststroke of admission to rehabilitation was around 20 days, and the median length of stay was between 7 and 8 weeks. This means that the models are unlikely to generalize to other settings that typically have shorter rehabilitation time frames.The dichotomous nature of the predicted outcomes and the prediction of these outcomes at discharge from rehabilitation are potentially problematic. The latter may mean that predictions become self-fulfilling prophecies, as patients will be discharged when they achieve the expected outcome. The dichotomous nature of the outcomes is problematic if the predictions are used to select patients for rehabilitation, as the authors suggest. Doing so might mean that patients who could make meaningful improvements in function are denied rehabilitation because they have a low probability of achieving an M-FIM score of at least 61. The authors are silent on how high the probability of achieving this outcome needs to be before they consider rehabilitation worthwhile, and the question of what the ...

IL-6 Plasma Levels Correlate With Cerebral Perfusion Deficits and Infarct Sizes in Stroke Patients Without Associated Infections

“…Prognosis and precise prediction of outcome remains challenging, especially during the hyperacute phase of the disease. While demographic and clinical characteristics, such as i.e., age and severity of acute clinical syndrome, allow for an educated guess (4), the accuracy of prediction is limited (5). For the post-acute phase of the disease, recent progress has been made in prediction of motor and cognitive recovery (6, 7).…”

Accuracy of the Upper Limb Prediction Algorithm PREP2 Applied 2 Weeks Poststroke: A Prospective Longitudinal Study

“…8,9,11,[21][22][23] However, the majority of these algorithms are most accurate for predicting recovery in patients with mild to moderate UL impairment. 9,12,24 The Predict Recovery Potential (PREP2) algorithm stands out as its overall predictive value is reported to be 75%. 25 Especially in patients with severe paresis its accuracy exceeds that of previous prediction algorithms.…”

Inflammatory factors are associated with early neurological deterioration in acute ischemic stroke patients receiving endovascular therapy-The AISRNA study

“…Precise prediction of clinical outcome remains challenging during the acute phase of ischemic stroke (IS) after EVT. While our previous studies have found that demographic and several clinical characteristics are associated with prognosis after acute ischemic stroke (2)(3)(4), the accuracy of prediction remains limited, especially during the acute phase of IS (5). Therefore, development of precise scores to predict prognosis in the acute stage may bene t from the individual biomarkers.…”