Ji R, Shen H, Pan Y, Wang P, Liu G, Wang Y, Li H, Wang Y; on behalf of China National Stroke Registry Investigators.
Stroke. 2013 Mar 12
BACKGROUND AND PURPOSE:
To develop and validate a risk score (acute ischemic stroke-associated pneumonia score [AIS-APS]) for predicting in-hospital stroke-associated pneumonia (SAP) after AIS.
The AIS-APS was developed based on the China National Stroke Registry, in which eligible patients were randomly classified into derivation (60%) and internal validation cohort (40%). External validation was performed using the prospective Chinese Intracranial Atherosclerosis Study. Independent predictors of in-hospital SAP after AIS were obtained using multivariable logistic regression, and β-coefficients were used to generate point scoring system of the AIS-APS. The area under the receiver operating characteristic curve and the Hosmer-Lemeshow goodness-of-fit test were used to assess model discrimination and calibration, respectively.
The overall in-hospital SAP after AIS was 11.4%, 11.3%, and 7.3% in the derivation (n=8820), internal (n=5882) and external (n=3037) validation cohort, respectively. A 34-point AIS-APS was developed from the set of independent predictors including age, history of atrial fibrillation, congestive heart failure, chronic obstructive pulmonary disease and current smoking, prestroke dependence, dysphagia, admission National Institutes of Health Stroke Scale score, Glasgow Coma Scale score, stroke subtype (Oxfordshire), and blood glucose. The AIS-APS showed good discrimination (area under the receiver operating characteristic curve) in the internal (0.785; 95% confidence interval, 0.766-0.803) and external (0.792; 95% confidence interval, 0.761-0.823) validation cohort. The AIS-APS was well calibrated (Hosmer-Lemeshow test) in the internal (P=0.22) and external (P=0.30) validation cohort. When compared with 3 prior scores, the AIS-APS showed significantly better discrimination with regard to in-hospital SAP after AIS (all P<0.0001).
The AIS-APS is a valid risk score for predicting in-hospital SAP after AIS.