Prediction of Upper Limb Use Three Months After Stroke
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Prediction of upper limb function and daily use after stroke PhD thesis Camilla Biering Lundquist Graduate school of Health Aarhus University Hammel Neurorehabilitation Centre and University Research Clinic 2021 ii Supervisors Principal supervisor: Iris Charlotte Brunner, Associate professor, PT, PhD. Department of Clinical Medi- cine, Hammel Neurorehabilitation Centre and University Research Clinic, Aarhus University, Aarhus, Denmark Co-supervisors: Jørgen Feldbæk Nielsen, Professor, MD, DMSc. Department of Clinical Medicine, Hammel Neurorehabilitation Centre and University Research Clinic, Aarhus University, Aarhus, Denmark Tine Tjørnhøj-Thomsen, Professor, MSc. National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark Evaluation committee Inger Mechlenburg, Professor, PT, MSc, DM (chairperson and moderator of the defence). Department of Orthopaedic Surgery, Aarhus University Hospital. Aarhus, Denmark Geert Verheyden, Professor, PT. Department of Rehabilitation Sciences KU Leuven, Belgium Thomas Platz, Professor, MD. Department for Neurorehabilitation, BDH-Klinik Greifswald, Germany iii Preface This PhD project originates from an interest in upper limb impairment after stroke and a desire to use UL prediction models in clinical practice. When a patient asks: "Will I ever be able to use my arm and hand again?" or "when can I hold a fork while eating?" those questions could be answered with more certainty in the future. Knowledge of upper limb prognosis can be used for the benefit of the pa- tient when setting goals or choosing UL interventions. The clinical use of upper limb prediction models has been a topic of focus at Ham- mel Neurorehabilitation Centre since 2017, when a group of physiotherapists and occupational therapists employed within research or professional development examined and discussed the evidence and potential implementation of UL pre- diction models. Based on these discussions, the most relevant model for clinical use at individual level, appeared to be the Predict Recovery Potential (PREP2) algorithm. The main reason was, that compared to other prediction models, the predictive accuracy of PREP2 for patients with severe upper limb impairment was high. However, several organizational obstacles prevented an implementation of PREP2 at a local level. The first part of PREP2, the Shoulder Abduction Finger Extension (SAFE) test, is designed to be performed within the first 72 hours after stroke, while patients are frequently admitted to RHN at a later point. Due to the limited time window to obtain the prediction, implementation of PREP2 was not feasible. In light of the challenges with implementation of PREP2 in the clinical settting, this PhD project was initialized. Its aim was to investigate the accuracy of PREP2 when obtained at a later point in time than originally proposed. If accuracy would still be high, this would pave the way for an easier incorporation of the algorithm in clinical practice. Whereas the PREP2 predicts upper limb function, real life daily iv use of arm and hand are often more relevant to patients and therapists. To be tru- ly meaningful, improvements in upper limb function must translate into improved use of the arm and hand in daily life. Thus, also the prediction of daily use of the arm and hand was examined. Finally, as the success of a future implementation will to a large extend depend on the health care professionals, a qualitative study was conducted to explore therapists´ perceptions of facilitators and barriers for a future implementation. Though the results were not always as expected, conduct- ing these three studies was an exciting process. Camilla Biering Lundquist, February 20th 2021 v This thesis is based on the following papers: I. C.B. Lundquist, J.F. Nielsen, F.G. Arguissain, I. Brunner Accuracy of the upper limb prediction algorithm PREP2 applied 2 weeks poststroke. A prospective longitudinal study. Published in: Neurorehabilitation and Neural Repair 1-11 2020 II. C.B. Lundquist, J.F. Nielsen, I. Brunner Prediction of upper limb use three months after stroke. A prospective longitudinal study. Submitted to: Disability and Rehabilitation III. C.B. Lundquist, H. Pallesen, T. Tjørnhøj-Thomsen, I. Brunner Exploring physiotherapists’ and occupational therapists’ perceptions of the upper limb prediction algorithm PREP2 after stroke in a rehabilita- tion setting. A qualitative study. Submitted to: BMJ Open. vi vii Contents List of figures x List of tables xi List of abbreviations xii Definitions xiii English summary 1 Danish summary 5 Introduction 9 Background 11 Stroke and stroke epidemiology 11 The ICF in relation to the upper limb 12 Prediction of upper limp function 13 Prediction of upper limp use 17 Implementation of prediction models 18 Gap of knowledge 20 Aims and hypothesis 21 Study I 21 Study II 21 Study III 22 Materials & methods 23 Design 23 Study I & II 23 Study III 23 viii Study setting 23 Study participants 24 Study I & II 24 Study III 25 Procedure 25 Study I & II 25 Baseline assessments 25 Follow-up assessments 29 Specific for Study I 30 Specific for Study II 32 Study III 33 Data analysis 36 Study I & II 36 Specific for Study I 37 Specific for Study II 37 Study III 40 Ethical issues 41 Study I & II 41 Study III 41 Results 43 Study I & II 43 Specific for Study I 46 Specific for Study II 53 Study III 62 Discussion 75 Study I 75 Summary of main results 75 Comparison with other studies 75 Limitations and strengths 78 ix Conclusion 80 Study II 80 Summary of main results 80 Comparison with other studies 82 Limitations and strengths 84 Conclusion 87 Study III 87 Summary of main results 87 Comparison with other studies 88 Limitations and strengths 89 Conclusion 90 Perspectives 91 Acknowledgements 93 References 95 Appendix 109 Paper 1 111 Paper 2 123 Paper 3 151 Declaration of co-author ship for paper 1 176 Declaration of co-author ship for paper 2 178 Declaration of co-author ship for paper 3 180 x List of figures Figure 1. Outcomes Used in the Present PhD project in Relation to ICF 13 Figure 2. The Predict Recovery Potential (PREP2) Algorithm. 16 Figure 3. Overview of Predictor Variables Used in Study I & II 29 Figure 4. The Predict Recovery Potential Algorithm Performed Two Weeks After Stroke 31 Figure 5. Flowchart of Patients Included 44 Figure 6. CART Model for Prediction of UL Function 51 Figure 7. Association between FMA at Baseline and Use Ratio at Three Months After Stroke 57 Figure 8. Association Between MEP Status at Baseline and Use Ratio Three Months After Stroke 59 Figure 9. Association Between Neglect at Baseline and Use Ratio Three Months After Stroke 60 Figure 10. ROC of Sensitivity and Specificity for Prediction of Use Ratio 61 Figure 11. Diagram Showing Examples of Theme Formation 63 Figure 12. Four Main Themes and Their Subthemes 64 xi List of tables Table 1. Interview Guide 35 Table 2. Demographic Characteristics of Included Patients (n=103) 45 Table 3. Baseline Assessments of All Patients Included (n=103) 46 Table 4. Demographic Characteristics and Baseline Assessments for Study I 47 Table 5. Predicted and Actual ARAT Categories and Agreement Between Them 49 Table 6. Accuracy of the Prediction Algorithm for UL Function 50 Table 7. Demographic Characteristics and Baseline Assessments for Study II 52 Table 8. Accelerometry Outcomes at Three Months after Stroke for all Patients and in Accordance with FMA at Baseline 54 Table 9. Regression Models to Examine Prediction of Use Ratio 57 Table 10. Characteristics of Focus Group Participants 62 xii List of abbreviations ARAT Action esearchR Arm Test CART Classification and Regression Tree CCR Correct Classification Rate CFIR Consolidated Framework for advancing Implementation Research CI Confidence Interval FMA Fugl-Meyer Motor Assessment Upper Extremity FIM Functional Independence Measure IQR Inter Quartile Range MEP Motor-evoked Potentials NIHSS National Institute of Health Stroke Scale OT Occupational Therapist PI Prediction Interval PREP2 Predict Recovery Potential algorithm, version 2 PT Physiotherapist RHN Hammel Neurorehabilitation Centre and University Research Clinic SAFE Shoulder Abduction Finger Extension SSS Scandinavian Stroke Scale SD Standard Deviation TMS Transcranial Magnetic Stimulation Twopd Two-point discrimination UL Upper limb xiii Definitions Algorithm: A set of mathematical instructions or rules that, especially if given to a computer, will help to calculate an answer to a problem.1 Biomarker: A stroke recovery biomarker can be defined as "an indicator of disease state that can be used as a measure of underlying molecular/cellular processes that may be difficult to measure directly in humans."2 A Motor-evoked Potential (MEP) is an example of a biomarker, used in the present PhD project. Neglect: Unilateral visuospatial neglect can be defined as "the inability to de- tect, respond to, and orient toward novel and significant stimuli occurring in the hemispace contralateral to a brain lesion."3 Prediction: A statement about what you think will happen in the future4 Use ratio: The use ratio is measured with wrist-worn accelerometers and defined as the total hours of paretic UL use divided by total hours of non-paretic use. A use ratio of 0.5 indicates that the paretic UL is active 50% of the time the non- paretic UL is active. English summary Background: Prediction of UL function and daily use is relevant for targeted reha- bilitation of patients with stroke. In this PhD project a prospective, observational longitudinal study was conducted to examine prediction of UL function (Study I) and prediction of UL use (Study II). A qualitative study was conducted to explore physiotherapists’ and occupational therapists’ perceptions of upper limb predic- tion models (Study III). Study I: The aim was to examine the prognostic accuracy of an existing UL algo- rithm, the Predict Recovery Potential algorithm (PREP2), when the time window to obtain the prediction was expanded to two weeks after stroke. Methods: Patients were assessed in accordance with the PREP2 approach.