Serial ASPECTS to predict stroke-associated pneumonia after thrombolysis in patients with acute ischemic stroke.

Abstract

Stroke-associated pneumonia (SAP) is a serious complication in stroke patients, significantly increasing mortality. The Alberta Stroke Program Early CT Score (ASPECTS) is a recognized predictor of acute ischemic stroke outcomes. We aimed to investigate the performance of serial ASPECTS assessments (baseline ASPECTS, 24-h ASPECTS, and change in ASPECTS) for predicting SAP in patients with thrombolyzed acute anterior circulation ischemic stroke (AACIS). A retrospective observational cohort study of adult patients with thrombolyzed AACIS was conducted. Baseline and 24-h ASPECTS using non-contrast computed tomography (NCCT), complications of stroke, including SAP and swallowing dysfunction using the Modified Water Swallowing test, were collected. Baseline and 24-h ASPECTS were evaluated by a certified neurologist and neuroradiologist. The predictive performance was determined based on the receiver operating characteristic curve (ROC). Multivariable logistic regression analyses were employed to assess the impact of serial ASPECTS assessment on predicting SAP. Of the 345 patients with thrombolyzed AACIS in our study, 18.4% (64/345) experienced SAP. The patients' median age was 62 years [interquartile range (IQR): 52-73], with 53.4% being male. The median NIHSS score was 11 points (IQR: 8-17). The ROC analysis revealed areas under the curve for predicting SAP with baseline ASPECTS, 24-h ASPECTS, and change in ASPECTS were 0.75 (95% CI, 0.69-0.82), 0.84 (95% CI, 0.79-0.89), and 0.82 (95% CI, 0.76-0.87), respectively. Of the three measures, 24-h ASPECTS was a better predictor of SAP (odds ratio: 5.33, 95%CI: 2.08-13.67, p < 0.001) and had a higher sensitivity (0.84 [95%CI, 0.74-0.92]) and specificity (0.79 [95%CI, 0.74-0.84]) than both baseline ASPECTS and change in ASPECTS. 24-h NCCT-ASPECTS outperformed both baseline ASPECTS and change in ASPECTS for predicting SAP. Notably, 24-h ASPECTS, with a cut-off value of ≤6, exhibited good predictive performance and emerged as the better predictor for SAP.