Complex speech-language therapy interventions for stroke-related aphasia: the RELEASE study incorporating a systematic review and individual participant data network meta-analysis
Notice bibliographique
Résumé
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 The results were screened for eligibility, and published and unpublished data sets (randomised controlled trials, non-randomised controlled trials, cohort studies, case series, registries) with at least 10 individual participant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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,060 | 0,142 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,026 | 0,045 |
| Bibliométrie | 0,012 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».