Complex speech-language therapy interventions for stroke-related aphasia: the RELEASE study incorporating a systematic review and individual participant data network meta-analysis
Bibliographic record
Abstract
Background People with language problems following stroke (aphasia) benefit from speech and language therapy. Optimising speech and language therapy for aphasia recovery is a research priority. Objectives The objectives were to explore patterns and predictors of language and communication recovery, optimum speech and language therapy intervention provision, and whether or not effectiveness varies by participant subgroup or language domain. Design This research comprised a systematic review, a meta-analysis and a network meta-analysis of individual participant data. Setting Participant data were collected in research and clinical settings. Interventions The intervention under investigation was speech and language therapy for aphasia after stroke. Main outcome measures The main outcome measures were absolute changes in language scores from baseline on overall language ability, auditory comprehension, spoken language, reading comprehension, writing and functional communication. Data sources and participants Electronic databases were systematically searched, including MEDLINE, EMBASE, Cumulative Index to Nursing and Allied Health Literature, Linguistic and Language Behavior Abstracts and SpeechBITE (searched from inception to 2015). The results were screened for eligibility, and published and unpublished data sets (randomised controlled trials, non-randomised controlled trials, cohort studies, case series, registries) with at least 10 individual participant data reporting aphasia duration and severity were identified. Existing collaborators and primary researchers named in identified records were invited to contribute electronic data sets. Individual participant data in the public domain were extracted. Review methods Data on demographics, speech and language therapy interventions, outcomes and quality criteria were independently extracted by two reviewers, or available as individual participant data data sets. Meta-analysis and network meta-analysis were used to generate hypotheses. Results We retrieved 5928 individual participant data from 174 data sets across 28 countries, comprising 75 electronic (3940 individual participant data), 47 randomised controlled trial (1778 individual participant data) and 91 speech and language therapy intervention (2746 individual participant data) data sets. The median participant age was 63 years (interquartile range 53–72 years). We identified 53 unavailable, but potentially eligible, randomised controlled trials (46 of these appeared to include speech and language therapy). Relevant individual participant data were filtered into each analysis. Statistically significant predictors of recovery included age (functional communication, individual participant data: 532, n = 14 randomised controlled trials) and sex (overall language ability, individual participant data: 482, n = 11 randomised controlled trials; functional communication, individual participant data: 532, n = 14 randomised controlled trials). Older age and being a longer time since aphasia onset predicted poorer recovery. A negative relationship between baseline severity score and change from baseline ( p < 0.0001) may reflect the reduced improvement possible from high baseline scores. The frequency, duration, intensity and dosage of speech and language therapy were variously associated with auditory comprehension, naming and functional communication recovery. There were insufficient data to examine spontaneous recovery. The greatest overall gains in language ability [14.95 points (95% confidence interval 8.7 to 21.2 points) on the Western Aphasia Battery-Aphasia Quotient] and functional communication [0.78 points (95% confidence interval 0.48 to 1.1 points) on the Aachen Aphasia Test-Spontaneous Communication] were associated with receiving speech and language therapy 4 to 5 days weekly; for auditory comprehension [5.86 points (95% confidence interval 1.6 to 10.0 points) on the Aachen Aphasia Test-Token Test], the greatest gains were associated with receiving speech and language therapy 3 to 4 days weekly. The greatest overall gains in language ability [15.9 points (95% confidence interval 8.0 to 23.6 points) on the Western Aphasia Battery-Aphasia Quotient] and functional communication [0.77 points (95% confidence interval 0.36 to 1.2 points) on the Aachen Aphasia Test-Spontaneous Communication] were associated with speech and language therapy participation from 2 to 4 (and more than 9) hours weekly, whereas the highest auditory comprehension gains [7.3 points (95% confidence interval 4.1 to 10.5 points) on the Aachen Aphasia Test-Token Test] were associated with speech and language therapy participation in excess of 9 hours weekly (with similar gains notes for 4 hours weekly). While clinically similar gains were made alongside different speech and language therapy intensities, the greatest overall gains in language ability [18.37 points (95% confidence interval 10.58 to 26.16 points) on the Western Aphasia Battery-Aphasia Quotient] and auditory comprehension [5.23 points (95% confidence interval 1.51 to 8.95 points) on the Aachen Aphasia Test-Token Test] were associated with 20–50 hours of speech and language therapy. Network meta-analyses on naming and the duration of speech and language therapy interventions across language outcomes were unstable. Relative variance was acceptable (< 30%). Subgroups may benefit from specific interventions. Limitations Data sets were graded as being at a low risk of bias but were predominantly based on highly selected research participants, assessments and interventions, thereby limiting generalisability. Conclusions Frequency, intensity and dosage were associated with language gains from baseline, but varied by domain and subgroup. Future work These exploratory findings require confirmatory study designs to test the hypotheses generated and to develop more tailored speech and language therapy interventions. Study registration This study is registered as PROSPERO CRD42018110947. Funding This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in full in Health and Social Care Delivery Research ; Vol. 10, No. 28. See the NIHR Journals Library website for further project information. Funding was also provided by The Tavistock Trust for Aphasia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.060 | 0.142 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.045 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".