A quantitative CT perfusion-derived online dynamic nomogram for predicting hemorrhagic transformation after intravenous thrombolysis in acute ischemic stroke

Purpose

To evaluate the diagnostic value of cerebral perfusion and its predictive ability of hemorrhagic transformation (HT) in acute ischemic stroke (AIS) after intravenous thrombolysis (IVT) using CT perfusion (CTP).

Methods

Retrospective cohort of 55 AIS patients who underwent CTP before IVT was included. Clinical information, such as the National Institutes of Health Stroke Scale (NIHSS) score and history of atrial fibrillation (AF), were collected. CTP parameters, including cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), permeability surface area product (PS), time-to-maximum (Tmax), time to peak (TTP), were measured in pathological hemispheres. Relative values (rCBV, rCBF, rMTT, rPS, rTmax, rTTP) were calculated as pathological-to-asymptomatic hemisphere ROI ratios. Comparisons between HT and non-HT groups were conducted using Student’s t-Test and Mann-Whitney U test. ROC curve and Logistic regression analysis were used to evaluate model predictive values. Delong's test compared AUC differences among parameters. Dynamic nomogram model was constructed with R-shiny and evaluated.

Results

NIHSS score at admission, NIHSS score before IVT, NIHSS score after IVT, NIHSS score at discharge, AF, PS and rPS were significantly higher than those in the non-HT group (p < 0.005). ROC curve and logistic regression analyses revealed that the combined model including NIHSS score before IVT, AF, and rPS displayed the highest AUC of 0.899 (95 % CI:0.814,0.984; p < 0.001).

Conclusion

Dynamic nomogram model combined NIHSS score before IVT, AF and rPS may act as a real-time visualization tool in the prediction of HT risk after IVT in patients with AIS.