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