Integrating the A2DS2 Score with 24-Hour ASPECTS and red cell distribution width for enhanced prediction of stroke-associated pneumonia following intravenous thrombolysis: model development and internal validation
Bibliographic record
Abstract
Stroke-associated pneumonia (SAP) is a major cause of mortality during the acute phase of stroke. The A 2 DS 2 score is widely used to predict SAP risk but does not include 24-h non-contrast computed tomography-Alberta Stroke Program Early CT Score (NCCT-ASPECTS) or red cell distribution width (RDW). We aim to evaluate the added prognostic value of incorporating 24-h NCCT-ASPECTS and RDW into the A 2 DS 2 score and to develop a novel prediction model for SAP following thrombolysis. This retrospective cohort study included thrombolyzed AIS patients at Saraburi Hospital, Thailand. The combined A 2 DS 2 -MFP model incorporated 24-h NCCT-ASPECTS and RDW, along with non-linear continuous predictors, using multivariable fractional polynomial (MFP) regression. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AuROC), calibration plots, and decision curve analysis (DCA), comparing it with the traditional A 2 DS 2 model and a model with continuous predictors. The goodness of fit for logistic regression models in relation to the observed data was determined through the Hosmer–Lemeshow method, and the accuracy of the probability predictions was examined using a calibration curve. Internal validation was performed using a bootstrapping approach. The predicted probability equation obtained from the final model after optimism correction was developed into a web-based application for predicting the risk of SAP, using PHP and JavaScript. Of 345 AIS patients, 20.3% developed SAP. The combined A 2 DS 2 -MFP model demonstrated excellent discriminative performance (AuROC: 0.917) compared to the traditional A 2 DS 2 model (AuROC: 0.868) and the model with continuous predictors (AuROC: 0.888). Both the calibration curve and the Hosmer–Lemeshow test indicated that the predicted probabilities and observed frequencies were in acceptable agreement. Incorporating 24-h NCCT-ASPECTS and RDW significantly improved risk prediction and clinical utility, as shown by improved reclassification indices and DCA. The model was internally validated with a C-statistic of 0.912, confirming its robustness. The combined A 2 DS 2 -MFP calculation showed superior performance, enabling early SAP detection and improving survival outcomes. This novel model offers a practical tool for resource-limited settings, supporting better SAP risk stratification and clinical management.
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".