Development of a PMGDNI model to predict the probability of three-month unfavorable outcome acute ischemic stroke after endovascular treatment: a cohort study

Patients with acute large vessel occlusion stroke (ALVOS) may exhibit considerable variability in clinical outcomes following mechanical thrombectomy (MT). This study aimed to develop a novel statistical model predicting functional independence three months post-endovascular treatment for acute stroke and validate its performance within the cohort.

Consecutive patients undergoing endovascular treatment for acute stroke with large vessel occlusion were randomly divided into a modeling group and a validation group in a 7:3 ratio. Independent risk factors were identified through LASSO regression and multivariate logistic regression analyses, leading to the development of a prognostic model whose performance was rigorously validated.

A total of 913 patients were screened, with 893 cases included. The modeling group comprised 625 cases, and the validation group included 268 cases. Identified independent factors for adverse outcomes after endovascular treatment of acute ischemic stroke (AIS) were pneumonia (OR = 4.517, 95% CI = 2.916–7.101, P < 0.001), mechanical ventilation (OR = 2.449, 95% CI = 1.475–5.148, P = 0.001), admission GCS ≥ 8 (OR = 0.365, 95% CI = 0.167–0.745, P = 0.008), dysphagia (OR = 2.074, 95% CI = 1.375–3.126, P < 0.001), and 72-hour highest Na ≥ 145 (OR = 2.794, 95% CI = 1.508–5.439, P = 0.002), along with intracranial hemorrhage (OR = 2.453, 95% CI = 1.408–4.396, P = 0.002). These factors were illustrated in a PMGDNI column chart. The area under the ROC curve for the modeling group was 82.5% (95% CI = 0.793–0.857), and for the validation group, it was 83.7% (95% CI = 0.789–0.885). The Hosmer-Lemeshow test indicates that there is no statistically significant difference (P > 0.05) between the predicted and actual probabilities of adverse outcomes. The clinical decision curve demonstrated optimal net benefits at thresholds of 0.30-1.00 and 0.25-1.00 for both training and validation sets, indicating effective clinical efficacy within these probability ranges.

We have successfully developed a new predictive model enhancing the accuracy of prognostic assessments for acute ischemic stroke following EVT. It provides an individual, visual, and precise prediction of the risk probability of a 90-day unfavorable outcome.