Development and Validation of a Predictive Model for Functional Outcome After Stroke Rehabilitation

Scrutinio, Domenico; Lanzillo, B.; Guida, Pietro; Mastropasqua, Filippo; Monitillo, Vincenzo; M, Pusineri; Formica, Roberto; Russo, G.; Guarnaschelli, C.; Ferretti, Chiara; Calabrese, Gianluigi

doi:10.1161/strokeaha.117.018058

Cited by 61 publications

(50 citation statements)

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“…7 The tool could also be useful for testing the effectiveness of interventions within their service that might increase the probability of achieving good outcomes. 4 confirms previous work showing that functional outcomes after stroke rehabilitation are affected by patients' age, sex, and initial disability. It is also an interesting first step toward using these factors to predict the probability of being minimally disabled on discharge from this rehabilitation setting.…”

supporting

confidence: 89%

“…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).…”

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It Is Difficult to Make Predictions, Especially About the Future

Byblow

Stinear

2017

Stroke

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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 ...

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It Is Difficult to Make Predictions, Especially About the Future

Byblow

Stinear

2017

Stroke

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“…Several studies have investigated predictors of clinical outcomes after acute inpatient stroke rehabilitation [10][11][12][13][14][15]. Their main focus was to predict individual's ability to perform activities of daily living, as measured by the FIM and the Barthel Index [16], or to predict walking speed as measured by the TMWT [14].…”

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confidence: 99%

Inpatient stroke rehabilitation: prediction of clinical outcomes using a machine-learning approach

Harari

O’Brien

Lieber

et al. 2020

J NeuroEngineering Rehabil

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Background: In clinical practice, therapists often rely on clinical outcome measures to quantify a patient's impairment and function. Predicting a patient's discharge outcome using baseline clinical information may help clinicians design more targeted treatment strategies and better anticipate the patient's assistive needs and discharge care plan. The objective of this study was to develop predictive models for four standardized clinical outcome measures (Functional Independence Measure, Ten-Meter Walk Test, Six-Minute Walk Test, Berg Balance Scale) during inpatient rehabilitation. Methods: Fifty stroke survivors admitted to a United States inpatient rehabilitation hospital participated in this study. Predictors chosen for the clinical discharge scores included demographics, stroke characteristics, and scores of clinical tests at admission. We used the Pearson product-moment and Spearman's rank correlation coefficients to calculate correlations among clinical outcome measures and predictors, a cross-validated Lasso regression to develop predictive equations for discharge scores of each clinical outcome measure, and a Random Forest based permutation analysis to compare the relative importance of the predictors. Results: The predictive equations explained 70-77% of the variance in discharge scores and resulted in a normalized error of 13-15% for predicting the outcomes of new patients. The most important predictors were clinical test scores at admission. Additional variables that affected the discharge score of at least one clinical outcome were time from stroke onset to rehabilitation admission, age, sex, body mass index, race, and diagnosis of dysphasia or speech impairment. Conclusions: The models presented in this study could help clinicians and researchers to predict the discharge scores of clinical outcomes for individuals enrolled in an inpatient stroke rehabilitation program that adheres to U.S. Medicare standards.

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“…With regard to the possible effect of stroke rehabilitation on survival, two recent studies showed that the magnitude of improvement in motor impairment after rehabilitation was positively associated with long-term survival, regardless of age, gender, cardiovascular comorbidities, the etiology and severity of stroke (as assessed by the National Institutes of Health Stroke Scale), and the initial severity of disability [66,67]. This effect was confirmed in the most severely disabled post-stroke patients [65].…”

Section: Stroke Rehabilitationmentioning

confidence: 93%

The new frontiers of rehabilitation medicine in people with chronic disabling illnesses

Scrutinio

Giardini

Chiovato

et al. 2019

European Journal of Internal Medicine

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Because of the demographic shift and the increased proportion of patients surviving acute critical illnesses, the number of people living with severely disabling chronic diseases and, consequently, the demand for rehabilitation are expected to increase sharply over time. As underscored by the World Health Organization, there is substantial evidence that the provision of inpatient rehabilitation in specialized rehabilitation units to people with complex needs is effective in fostering functional recovery, improving health-related quality of life, increasing independence, reducing institutionalization rate, and improving prognosis. Recent studies in the real world setting reinforce the evidence that patients with ischemic heart disease or stroke benefit from rehabilitation in terms of improved prognosis. In addition, there is evidence of the effectiveness of rehabilitation for the prevention of functional deterioration in patients with complex and/or severe chronic diseases. Given this evidence of effectiveness, rehabilitation should be regarded as an essential part of the continuum of care. Nonetheless, rehabilitation still is underdeveloped and underused. Efforts should be devoted to foster healthcare professional awareness of the benefits of rehabilitation and to increase referral and participation.

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Development and Validation of a Predictive Model for Functional Outcome After Stroke Rehabilitation

Abstract: This study provides researchers and clinicians with an easy-to-use, accurate, and validated predictive tool for potential application in rehabilitation research and stroke management.

Cited by 61 publications

References 32 publications

It Is Difficult to Make Predictions, Especially About the Future

It Is Difficult to Make Predictions, Especially About the Future

Inpatient stroke rehabilitation: prediction of clinical outcomes using a machine-learning approach

The new frontiers of rehabilitation medicine in people with chronic disabling illnesses

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