Pre-Stroke Modified Rankin Scale: Evaluation of Validity, Prognostic Accuracy, and Association with Treatment

Quinn, Terence J; Taylor‐Rowan, Martin; Coyte, Aishah; Clark, Allan; Musgrave, Stanley D.; Metcalf, Anthony K.; Day, Diana J.; Bachmann, Max; Warburton, Elizabeth A.; Potter, John F.; Myint, Phyo Kyaw

doi:10.3389/fneur.2017.00275

Cited by 90 publications

(64 citation statements)

References 26 publications

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“…No significant difference was found between the dichotomized groups except for age, where patients were older in the high pre-mRS and dis-mRS groups. The prevalence of identified background injury was 1,520/1,704, in the context of a distribution pre-mRS scores consistent with previous studies (31).…”

Section: Resultssupporting

confidence: 89%

“…Third, this retrospective study uses information routinely gathered during clinical practice over 2 years and collected as part of the SSNAP initiative in the UK. The modified Rankin Scale score is a general summary measure of patients' function, which is subject to moderate variability, particularly in the clinical setting (31,42). Although converting the score to a binary outcome may obviate some of this variability, both aspects will impact on our model's potential to accurately classify patients.…”

Section: Figure 4 |mentioning

confidence: 99%

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Quantifying the Impact of Chronic Ischemic Injury on Clinical Outcomes in Acute Stroke With Machine Learning

2020

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Acute stroke is often superimposed on chronic damage from previous cerebrovascular events. This background will inevitably modulate the impact of acute injury on clinical outcomes to an extent that will depend on the precise anatomical pattern of damage. Previous attempts to quantify such modulation have employed only reductive models that ignore anatomical detail. The combination of automated image processing, large-scale data, and machine learning now enables us to quantify the impact of this with high-dimensional multivariate models sensitive to individual variations in the detailed anatomical pattern. We introduce and validate a new automated chronic lesion segmentation routine for use with non-contrast CT brain scans, combining non-parametric outlier-detection score, Zeta, with an unsupervised 3-dimensional maximum-flow, minimum-cut algorithm. The routine was then applied to a dataset of 1,704 stroke patient scans, obtained at their presentation to a hyper-acute stroke unit (St George's Hospital, London, UK), and used to train a support vector machine (SVM) model to predict between low (0-2) and high (3-6) pre-admission and discharge modified Rankin Scale (mRS) scores, quantifying performance by the area under the receiver operating curve (AUROC). In this single center retrospective observational study, our SVM models were able to differentiate between low (0-2) and high (3-6) pre-admission and discharge mRS scores with an AUROC of 0.77 (95% confidence interval of 0.74-0.79), and 0.76 (0.74-0.78), respectively. The chronic lesion segmentation routine achieved a mean (standard deviation) sensitivity, specificity and Dice similarity coefficient of 0.746 (0.069), 0.999 (0.001), and 0.717 (0.091), respectively. We have demonstrated that machine learning models capable of capturing the high-dimensional features of chronic injuries are able to stratify patients-at the time of presentation-by pre-admission and discharge mRS scores. Our fully automated chronic stroke lesion segmentation routine simplifies this process, and utilizes routinely collected CT head scans, thereby facilitating future large-scale studies to develop supportive clinical decision tools.

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Section: Resultssupporting

confidence: 89%

Section: Figure 4 |mentioning

confidence: 99%

Quantifying the Impact of Chronic Ischemic Injury on Clinical Outcomes in Acute Stroke With Machine Learning

2020

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“…The validity of the scale can however be affected when a proxy is used to generate a score, or when applied in the acute setting during when the patient has not yet had the chance to resume normal activities. When used in a retrospective fashion to determine the pre-stroke functional state, the mRS validity can be diminished, demonstrating moderate-concurrent validity (ρ > 0.4) when compared with other variables associated with function ( 33 ).…”

Section: Participation: Modified Rankin Scalementioning

confidence: 99%

Functional Assessment for Acute Stroke Trials: Properties, Analysis, and Application

et al. 2018

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A measure of treatment effect is needed to assess the utility of any novel intervention in acute stroke. For a potentially disabling condition such as stroke, outcomes of interest should include some measure of functional recovery. There are many functional outcome assessments that can be used after stroke. In this narrative review, we discuss exemplars of assessments that describe impairment, activity, participation, and quality of life. We will consider the psychometric properties of assessment scales in the context of stroke trials, focusing on validity, reliability, responsiveness, and feasibility. We will consider approaches to the analysis of functional outcome measures, including novel statistical approaches. Finally, we will discuss how advances in audiovisual and information technology could further improve outcome assessment in trials.

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“…mRS scores were obtained at each time point, and a pre-event mRS score was derived retrospectively using information from patients and carers. Pre-and post-event mRS scores show good concurrent validity and are a robust predictor of prognosis (27,28).…”

Section: Independent Variables: Abcd2 Score Vascular Risk Factors Pmentioning

confidence: 97%

Cognition in the First Year After a Minor Stroke, Transient Ischemic Attack, or Mimic Event and the Role of Vascular Risk Factors

et al. 2020

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Background: Cognitive impairment following a minor stroke or transient ischemic attack (TIA) is common; however, due to diagnostic difficulties, the prevalence and underlying cause of impairment remain poorly defined. We compared cognition in patients after a minor stroke, TIA, or mimic event at three time points in the first year following the event. We examine whether cognitive impairment occurs following these events and whether this impairment differs based on the event type. Further, we measure whether these findings persist after controlling for age, education, and the presence of vascular risk factors and whether the presence of vascular risk factors, independent of event etiology, is associated with cognitive impairment. Lastly, we investigate whether increased stroke risk, as assessed by the ABCD2, is associated with reduced cognition.Methods: Medical information, a cognitive screening test, and a measure of executive functioning were collected from 613 patients (123 minor stroke, 175 TIA, and 315 mimics) using phone interviews at three time points in the first year following the event. Linear mixed models were used to determine the effect of event type, vascular risk factors, and predicted stroke risk on cognitive performance while controlling for confounders.Results: There was no relationship between event type and performance on either cognitive measure. When all confounders are controlled for, performance on the cognitive screening test was uniquely accounted for by the presence of heart failure, myocardial infarction, angina, and hypertension (all p < 0.047), whereas the measure of executive functioning was uniquely accounted for by the presence of hypertension and angina (all p < 0.032). Increased stroke risk also predicted performance on the cognitive screening test and the measure of executive functioning (all p < 0.002). Conclusions:Our findings indicate that cognitive impairment following a minor stroke or TIA may be attributed to the high prevalence of chronic vascular risk factors in these patients. This highlights the importance of long-term management of vascular risk factors Nicolas et al.Cognition After a Minor Stroke, TIA, or Mimic-TIA beyond event recovery to reduce the risk of cognitive impairment. Increased stroke risk (i.e., ABCD2 score) was also associated with reduced cognition, suggesting that it may be helpful in signaling the need for further cognitive evaluation and intervention post-event.

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Pre-Stroke Modified Rankin Scale: Evaluation of Validity, Prognostic Accuracy, and Association with Treatment

Cited by 90 publications

References 26 publications

Quantifying the Impact of Chronic Ischemic Injury on Clinical Outcomes in Acute Stroke With Machine Learning

Quantifying the Impact of Chronic Ischemic Injury on Clinical Outcomes in Acute Stroke With Machine Learning

Functional Assessment for Acute Stroke Trials: Properties, Analysis, and Application

Cognition in the First Year After a Minor Stroke, Transient Ischemic Attack, or Mimic Event and the Role of Vascular Risk Factors

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