Predicting post-stroke pneumonia: the PANTHERIS score
Version of Record online: 6 MAR 2013
© 2013 John Wiley & Sons A/S
Acta Neurologica Scandinavica
Volume 128, Issue 3, pages 178–184, September 2013
How to Cite
Predicting post-stroke pneumonia: the PANTHERIS score. Acta Neurol Scand 2013: 128: 178–184. © 2013 John Wiley & Sons A/S., , , .
- Issue online: 16 AUG 2013
- Version of Record online: 6 MAR 2013
- Manuscript Accepted: 9 JAN 2013
- European Union's Seventh Framework Programme. Grant Numbers: FP7/2008-2013, 201024, 202213
- European Stroke Network
- Helmholtz Gemeinschaft für Forschungseinrichtungen. Grant Number: SO-022NG
- German Ministry for Health and Education. Grant Number: 01 EO 08 01
- Deutsche Forschungsgemeinschaft. Grant Number: Exc 257
- predictive score;
- stroke-associated pneumonia;
- glasgow coma scale;
- systolic hypertension
Stroke-associated pneumonia (SAP) is a common complication with a known negative impact on neurological outcome. We developed a score to identify patients at highest risk of SAP in order to promote prophylactic measures.
Materials and Methods
We conducted a cohort study on a neurological intensive care unit in patients suffering from acute ischemic MCA infarction. Association of predefined demographics, comorbidities, and clinical characteristics with SAP was investigated using logistic regression analysis.
Between 2003 and 2010, a total of 335 patients were included in this analysis. Frequency of SAP was 31.3%. A 12-point scoring system was developed based on the following factors: Glasgow Coma Scale (GCS) [GCS < 9 = 5, GCS 9–12 = 2, GCS > 12 = 0], age [<60 = 0, 60–80 = 1, >80 = 2], increase in systolic arterial blood pressure >200 mmHg within the first 24 h after admission [no = 0, yes = 2], and white blood cell count >11.000/μl [no = 0, yes = 3]. The score revealed excellent discrimination (AUC = 0.85) and calibration (Nagelkerke's R² = 0.46) properties. Predictive properties were reproduced in an internal validation group.
The PANTHERIS score is a simple scoring system for the prediction of SAP based on easy-to-assess parameters. By identifying patients at high risk, it may guide intense monitoring or prophylactic measures. This score needs to be validated within external datasets.