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

Lundquist, Camilla Biering; Nielsen, Jørgen Feldbæk; Arguissain, Federico; Brunner, Iris

doi:10.1177/1545968320971763

Cited by 26 publications

(38 citation statements)

References 53 publications

(100 reference statements)

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“…This hypothesis remains to be tested empirically, and it is notable to point out that the approach may only be applicable to patients who exhibit MEPs when stimulated. Approximately 13.4% (Stinear et al, 2017a) are deemed "MEP negative, " and those tend to be the most severely affected (Stinear et al, 2017b;Smith et al, 2019;Lundquist et al, 2021). Incorporating multimodality into BCI paradigms may also extend beyond the aforementioned suggestions concerning acquisition modalities.…”

Section: Practical and Technical Challenges With Clinical Implementation Of Bcimentioning

confidence: 99%

Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation

et al. 2021

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Brain-computer interfaces (BCIs) provide a unique technological solution to circumvent the damaged motor system. For neurorehabilitation, the BCI can be used to translate neural signals associated with movement intentions into tangible feedback for the patient, when they are unable to generate functional movement themselves. Clinical interest in BCI is growing rapidly, as it would facilitate rehabilitation to commence earlier following brain damage and provides options for patients who are unable to partake in traditional physical therapy. However, substantial challenges with existing BCI implementations have prevented its widespread adoption. Recent advances in knowledge and technology provide opportunities to facilitate a change, provided that researchers and clinicians using BCI agree on standardisation of guidelines for protocols and shared efforts to uncover mechanisms. We propose that addressing the speed and effectiveness of learning BCI control are priorities for the field, which may be improved by multimodal or multi-stage approaches harnessing more sensitive neuroimaging technologies in the early learning stages, before transitioning to more practical, mobile implementations. Clarification of the neural mechanisms that give rise to improvement in motor function is an essential next step towards justifying clinical use of BCI. In particular, quantifying the unknown contribution of non-motor mechanisms to motor recovery calls for more stringent control conditions in experimental work. Here we provide a contemporary viewpoint on the factors impeding the scalability of BCI. Further, we provide a future outlook for optimal design of the technology to best exploit its unique potential, and best practices for research and reporting of findings.

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Section: Practical and Technical Challenges With Clinical Implementation Of Bcimentioning

confidence: 99%

Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation

et al. 2021

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“…18,28,31,32 To date, two studies investigated PREP2 metrics in a healthcare system outside of NZ, but initial data collection occurred on a timeline that coincides more closely to AR admission in the US (initial strength measurements made for patients approximately 1-to 2-weeks post-stroke). 33,34 Prediction accuracy in both studies was similar but lower than in the NZ cohort (~60% overall vs. NZ accuracy=75%). 24,33 The lower accuracy may be explained by only including participants admitted to inpatient rehabilitation, by omission of biomarkers of functional corticospinal tract integrity, and by delayed initial strength measurement.…”

Section: Introductionmentioning

confidence: 67%

“…24,33 The lower accuracy may be explained by only including participants admitted to inpatient rehabilitation, by omission of biomarkers of functional corticospinal tract integrity, and by delayed initial strength measurement. 33,34 However, the lower observed accuracy also emphasizes the need to identify measures that can be collected at admission to AR that more accurately predict stroke outcome.…”

Section: Introductionmentioning

confidence: 99%

Paretic upper extremity strength at acute rehabilitation evaluation predicts motor function outcome after stroke

Silva

Cook

Stinear

et al. 2021

Preprint

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Objective: The primary objective of this study was to retrospectively assess current care practices to determine the routinely collected measures that are most predictive of paretic upper extremity (PUE) functional outcome post-stroke in patients undergoing acute inpatient rehabilitation (AR). Methods: We conducted a longitudinal chart review of patients post-stroke who received care in the Emory University Hospital system for acute hospitalization, AR, and outpatient therapy in fiscal years 2016-2018. We identified eligible patients using previously established inclusion and exclusion criteria. We extracted demographics, stroke characteristics, and longitudinal documentation of post-stroke motor function from institutional electronic medical records. Serial assessments of PUE strength were estimated using available shoulder abduction and finger extension manual muscle test documentation (E-SAFE). Estimated Action Research Arm Test (E-ARAT) was used to quantify 3-month PUE functional outcome. Metric associations were explored through correlation and cluster analyses, Kruskal-Wallis tests, classification and regression tree (CART) analysis. Results: Thirty-four patients met study eligibility criteria. E-SAFE assessments performed closest to acute hospitalization day-3 (Acute E-SAFE) and upon AR admission (AR E-SAFE) were correlated with E-ARAT. Cluster analysis produced three distinct outcome groups and aligned closely to previous outcome categories. Outcome groups significantly differed in Acute E-SAFE and AR E-SAFE. Exploratory CART analysis selected AR E-SAFE to classify patient outcome with 70.6% accuracy. Conclusions: Current study findings reveal that: PUE E-SAFE, measured both acutely and at AR admission, is associated with PUE motor recovery outcome; categorizations of outcome are consistent with previous studies; and predictive models can identify recovery outcome category in patients undergoing AR. Impact Statement: Our findings highlight the clinical utility of SAFE as an easy-to-acquire, readily implementable screening metric. Early, intentional use of SAFE in AR settings may improve clinical decision-making, enabling therapists to deliver precision-based interventions that serve to speed or enhance recovery outcome for patients post-stroke.

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“…gov number: NCT03632499). Results of this study have been published recently by Lundquist et al 22 Second, the current study was conducted to explore facilitators and barriers for future implementation.…”

Section: Study Settingmentioning

confidence: 90%

“…In this context, it is important to consider that prediction algorithms should not be implemented in clinical practice until both development and validation studies have been conducted. 6 Before implementation of a UL prediction model in our local setting, the results from a recent study on the accuracy of PREP2 when obtained 2 weeks post-stroke 22 should be combined with the knowledge obtained from the current study.…”

Section: Future Directionsmentioning

confidence: 99%

Exploring physiotherapists’ and occupational therapists’ perceptions of the upper limb prediction algorithm PREP2 after stroke in a rehabilitation setting: a qualitative study

Lundquist¹,

Pallesen

Tjørnhøj‐Thomsen

et al. 2021

BMJ Open

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ObjectiveTo explore how physiotherapists (PTs) and occupational therapists (OTs) perceive upper limb (UL) prediction algorithms in a stroke rehabilitation setting and identify potential barriers to and facilitators of their implementation.DesignThis was a qualitative study.SettingThe study took place at a neurorehabilitation centre.ParticipantsThree to six PTs and OTs.MethodsWe conducted four focus group interviews in order to explore therapists’ perceptions of UL prediction algorithms, in particular the Predict Recovery Potential algorithm (PREP2). The Consolidated Framework for advancing Implementation Research was used to develop the interview guide. Data were analysed using a thematic content analysis. Meaning units were identified and subthemes formed. Information gained from all interviews was synthesised, and four main themes emerged.ResultsThe four main themes were current practice, perceived benefits, barriers and preconditions for implementation. The participants knew of UL prediction algorithms. However, only a few had a profound knowledge and few were using the Shoulder Abduction Finger Extension test, a core component of the PREP2 algorithm, in their current practice. PREP2 was considered a potentially helpful tool when planning treatment and setting goals. A main barrier was concern about the accuracy of the algorithm. Furthermore, participants dreaded potential dilemmas arising from having to confront the patients with their prognosis. Preconditions for implementation included tailoring the implementation to a specific unit, sufficient time for acquiring new skills and an organisation supporting implementation.ConclusionIn the present study, experienced neurological therapists were sceptical towards prediction algorithms due to the lack of precision of the algorithms and concerns about ethical dilemmas. However, the PREP2 algorithm was regarded as potentially useful.

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

Cited by 26 publications

References 53 publications

Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation

Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation

Paretic upper extremity strength at acute rehabilitation evaluation predicts motor function outcome after stroke

Exploring physiotherapists’ and occupational therapists’ perceptions of the upper limb prediction algorithm PREP2 after stroke in a rehabilitation setting: a qualitative study

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