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Systematic review assesses performance and applicability of aspiration risk prediction models in stroke

Systematic review assesses performance and applicability of aspiration risk prediction models in str…
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Key Takeaway
Consider the high risk of bias and limited external validation when interpreting aspiration risk prediction models for stroke patients.

This systematic review examines the overall performance and applicability of prediction models designed to assess aspiration risk in adult stroke patients. The authors synthesized data from 18 model development studies to evaluate how well these tools identify patients at risk. The primary outcome focused on performance and applicability, while secondary outcomes included risk of bias assessment and applicability assessment.

Regarding discrimination performance, most models demonstrated good discrimination capabilities. The reported AUC/C-index values ranged from 0.756–0.955 across the included literature. In terms of applicability, 16 studies showed good applicability, suggesting potential integration into clinical workflows. However, the review identified key predictors without specifying exact variables in the summary data.

Significant limitations were noted regarding the quality of the evidence. All 18 studies had a high risk of bias. The authors highlighted methodological bias, heterogeneity, and retrospective designs as major concerns. Additional issues included small sample sizes, inadequate missing data handling, univariate variable selection, and limited external validation which impacts reliability.

The certainty of the evidence is low due to the high risk of bias in the included studies. The review concludes that clinical utility is limited by methodological heterogeneity and generalizability is constrained by limited external validation. These findings inform future model optimization and clinical use rather than immediate implementation. No causal inference on interventions was made by the authors.

Study Details

Study typeMeta analysis
EvidenceLevel 1
PublishedApr 2026
View Original Abstract ↓
BackgroundStroke is a leading cause of disability and death, with post-stroke dysphagia significantly increasing aspiration risk, leading to complications, such as aspiration pneumonia and higher mortality. Various prediction models for aspiration exist, but their clinical utility is limited by methodological heterogeneity.AimThis review aimed to evaluate the performance and applicability of these models for stroke patients, informing future model optimization and clinical use.Study designA comprehensive search was conducted across nine electronic databases until 20 January 2025. Studies on the development or validation of aspiration risk prediction models in adult stroke patients were included. Data extraction followed the CHARMS checklist, and bias risk and applicability were assessed using the PROBAST tool. The review was prospectively registered with PROSPERO (CRD420251007112).ResultsEighteen model development studies were included. Most demonstrated good discrimination (AUC/C-index: 0.756–0.955), with 16 showing good applicability. However, all studies had a high risk of bias, mainly due to retrospective designs, small sample sizes (events per variable < 20), inadequate missing data handling, univariate variable selection, and limited external validation. Key predictors included age, NIHSS score, Kubota Water Swallowing Test, history of aspiration, and Glasgow Coma Scale score.ConclusionAspiration risk prediction models for stroke patients show promising predictive performance but are limited by methodological bias and heterogeneity. Future research should prioritize rigorous reporting and multi-center, large-sample external validation to improve model robustness and clinical applicability.Systematic review registrationPROSPERO CRD420251007112, URL: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD420251007112.
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