Background and Purpose: Several clinical scoring systems as well as biomarkers have been proposed to predict Stroke-associated pneumonia (SAP). We aimed to externally and competitively validate SAP scores and hypothesized that 5 selected biomarkers would improve performance of these scores.
Methods: We pooled the clinical data of two acute stroke studies with identical data assessment: STRAWINSKI and PREDICT. Biomarkers (ultrasensitive Procalcitonin; mid-regional pro-Adrenomedullin; mid-regional pro-atrionatriuretic peptide; ultrasensitive Copeptin; C-terminal pro Endothelin) were measured from hospital admission serum samples. A literature search was
performed to identify SAP prediction scores. We then calculated multivariate regression models with the individual scores and the biomarkers. Areas under receiver-operating characteristic curves (AUROC) were used to compare discrimination of these scores and models.
Results: The combined cohort consisted of 683 cases, of which 573 had available backup samples to perform the biomarker analysis. Literature search identified 9 SAP prediction scores. Our dataset enabled us to calculate 5 of these scores. The scores had AUROC of 0.543-0.651 for physician
determined SAP, 0.574-0.685 for probable and 0.689-0.811 for definite SAP according to Pneumonia In Stroke ConsEnSus (PISCES) group criteria. Multivariate models of the scores with biomarkers improved virtually all predictions, but mostly in the range of an AUROC delta of 0.05.
Conclusion: All SAP prediction scores identified patients who would develop SAP with fair to strong capabilities, with better discrimination when stricter criteria for SAP diagnosis were applied. The selected biomarkers provided only limited added predictive value, currently not warranting addition of these markers to prediction models.