Relationship between intensity and recovery in post-stroke rehabilitation: a retrospective analysis

 So they know nothing and suggest future studies because this study wasn't good enough.

Relationship between intensity and recovery in post-stroke rehabilitation: a retrospective analysis

INTRODUCTION Work in animal models suggests high intensity rehabilitation-based training that starts soon after stroke is the most effective approach to promote recovery.1 In humans, the interaction between treatment onset and intensity remains unclear.2 It has been suggested that reducing daily treatment duration below 3hours at the acute and subacute stages leads to a poorer prognosis,3 while there may also be an upper bound beyond which high-intensity motor rehabilitation at the acute stage might lead to unwanted side effects.4 Designing optimal rehabilitation treatment programmes for stroke patients will not be possible until we understand ‘how much’, ‘when’ and ‘what’ treatment should be delivered.2 In this retrospective analysis, we assessed patients’ responsiveness to high-intensity and low-intensity rehabilitation protocols across different stages of chronicity post-stroke to address the ‘how much’ and ‘when’ questions. PATIENTS AND METHODS The Queen Square Upper Limb Neurorehabilitation (QSUL)5 and the Rehabilitation Gaming System (RGS)6 datasets comprise a cohort of 455 individuals with upper-limb hemiparesis treated between 2008 and 2018 at different stages of chronicity post-stroke (subacute <6 months, early chronic 6–18 months, late chronic 18 months to 4 years and beyond 4y >4 years).7 The QSUL programme delivered a 3-week high-intensity rehabilitation programme (high-intensity conventional treatment (H-CT), 6hours daily, 5days per week, 90 hours in total) based on a combination of conventional therapies (n=224). The RGS cohort (n=231) followed a 3–12weeks low-intensity treatment programme (20–30min/session, 3–5days a week, 7.5–30hours in total) consisting of either conventional treatment (lowintensity conventional treatment (L-CT), n=69, 30%) or computer-based embodied goal-oriented rehabilitation in virtual reality that was automatically adjusted to the patient’s performance (low-intensity RGS-based neurorehabilitation (L-RGS), n=162, 70%). Participants underwent assessment with the upper extremity section of the Fugl-Meyer (UE-FM) scale at baseline, end of treatment (weeks 3–6) and 6–24 weeks after discharge (longterm follow-up). The details of the assessment have been reported previously.5 6 To compare the recovery metrics from both datasets, we calculated the improvement rate per week of treatment normalised for the within-subject recovery potential or normalised recovery rate.6 This metric captures an improvement measure normalised to the total amount that each patient could potentially reach given their baseline score, in this case, on the UE-FM and allows the assessment of the responsiveness to treatment. We used the long-term follow-up assessment, where no treatment was given, as our control measurement (no treatment group), for which we calculated the normalised recovery rate from the end of treatment. Data were analysed in MATLAB 2019b (The MathWorks) and Python V.3.6 (Python Software Foundation), using the Data Analysis with Bootstrap-coupled ESTimation libraries. In our analysis, we considered a p value <0.05 as significant. RESULTS Across the two cohorts, a total of 455 patients were included in the analysis. At the start of treatment, the H-CT was more severely impaired as measured with UE-FM (UE-FM score; H-CT: mean 27±13SD; L-CT: mean 37±16SD; L-RGS: mean 36±14SD; p<0.05, one-way Analysis of Variance ANOVA), younger (age; H-CT: mean 49±15SD; L-CT: mean 61±11SD; L-RGS: mean 63±12SD; p<0.001, oneway ANOVA) and more chronic (days since stroke; H-CT: mean 1288±1602SD; L-CT: mean 697±928SD; L-RGS: mean 806±1003SD; p<0.01, one-way ANOVA) than the L-CT and L-RGS groups (detailed post hoc analysis in Ward et al5 and Ballester et al6 ). All groups that received treatment showed significant improvements (all treatment groups UE-FM; mean 5±5SD; no treatment group UE-FM: mean 0±1SD; p value <0.001, two-sided Mann-Whitney test). We observed that this improvement was proportional to training intensity, that is, the high-intensity group showed higher responsiveness to treatment at all measurement points as compared to both low-intensity groups (difference in responsiveness between H-CT and L-CT/ L-RGS; mean 4±1SE for subacute; mean 5±1SE for early chronic; mean 5±1SE for late chronic; mean 6±1SE for beyond 4y; p<0.001, two-sided Mann-Whitney test) representing a clinically meaningful change for the non-subacute patients.8 The analysis of the effect of chronicity showed a consistent decrease in responsiveness to the three types of treatment (figure 1A). Patients who started treatment in the subacute phase (<6 months) showed the largest improvement in comparison to patients at chronic stages for both highintensity (the mean differences in responsiveness between subacute and all other chronicity groups ranged from −1 to −3; all p<0.01, two-sided Mann-Whitney test) and low-intensity treatments (the mean differences in responsiveness between subacute and all other chronicity groups ranged from −3 to −4; all p<0.01, twosided Mann-Whitney test) as shown in figure 1B. The decrease of responsiveness extended beyond the subacute phase and was invariant to training intensity, that is, those who started low-intensity treatment earlier at the chronic stage (6–18 months) also displayed higher gains as compared to those who started later (difference in responsiveness between early and late chronic; mean 1±0SE; p<0.05, twosided Mann-Whitney test). Interestingly, a late start did not attenuate the patient’s responsiveness to the high-intensity programme (difference in responsiveness between early and late chronic; mean 1±1SE; p=0.38, two-sided MannWhitney test). DISCUSSION In this retrospective analysis, we showed that, in both the QSUL and the RGS cohorts, responsiveness to treatment was present at practically all stages post-stroke, demonstrating a gradually declining impact with chronicity and modulation of the responsiveness by the intensity of the intervention. Our findings also support an early rehabilitation start regardless of intensity (ie, at the subacute stage). Beyond this general effect, the high-intensity approach showed a consistent higher impact over low-intensity rehabilitation protocols (L-RGS and L-CT) at all stages post-stroke. Indeed, the advantageous impact of high-intensity rehabilitation on recovery may compensate for a late start. For instance, patients who underwent high-intensity training displayed enhanced responsiveness even when treatment was delivered with a 2-year delay compared with low-intensity interventions. Notice, however, that the difference in intensity between the two rehabilitation approaches was prominent. While the QSUL programme consisted of 6hours daily, the low-intensity group underwent 0.5–1hour daily training for only 3days a week. The lack of proportionality between the groups suggests a non-linear interaction between treatment type, intensity, chronicity and responsiveness.9 This analysis lacks a control group receiving no rehabilitation treatment at early stages post-stroke; thus, we cannot rule out the effect of standard treatment on the patients’ responsiveness to additional treatments. Future studies should investigate and model these relationships to unmask the precise type and intensity of treatment to promote recovery at each stage post-stroke. Altogether, these findings suggest that current stroke guidelines must be revised to incorporate highintensity rehabilitation protocols across the entire continuum of chronicity.10 Currently, patients obtain only 22 min of treatment,11 with less treatment time at later phases post-stroke. Most importantly, we need to find effective solutions to deliver individualised high-intensity rehabilitation protocols. The increase in stroke survivors and the associated burden poses a challenge for the limited resources of our current healthcare system and asks for the translation of effective principles of neurorehabilitation to scalable technologies to deliver sustainable long-term care addressing the socioeconomic burden of stroke.