Nomogram to predict 6-month mortality in acute ischemic stroke patients treated with endovascular treatment

Acute Ischemic Stroke (AIS) presents significant challenges in evaluating the effectiveness of Endovascular Treatment (EVT). This study develops a novel prognostic model to predict 6-month mortality post-EVT, aiding in identifying patients likely to benefit less from this intervention, thus enhancing therapeutic decision-making.

We employed a cohort of AIS patients from Shenyang First People's Hospital, serving as the Validation set, to develop our model. LASSO regression was used for feature selection, followed by logistic regression to create a prognostic nomogram for predicting 6-month mortality post-EVT. The model's performance was validated using a dataset from PLA Northern Theater Command General Hospital, assessing discriminative ability (C-index), calibration (calibration plot), and clinical utility (decision curve analysis). Statistical significance was set at p < 0.05.

The development cohort consisted of 219 patients. Six key predictors of 6-month mortality were identified: "Lack of Exercise" (OR, 4.792; 95% CI, 1.731-13.269), "Initial TICI Score 1" (OR, 1.334; 95% CI, 0.628-2.836), "MRS Score 5" (OR, 1.688; 95% CI, 0.754-3.78), "Neutrophil Percentage" (OR, 1.08; 95% CI, 1.042-1.121), "Onset Blood Sugar" (OR, 1.119; 95% CI, 1.007-1.245), and "Onset NIHSS Score" (OR, 1.074; 95% CI, 1.029-1.121). The nomogram demonstrated a high predictive capability with a C-index of 0.872 (95% CI, 0.830-0.911) in the development set and 0.830 (95% CI, 0.726-0.920) in the validation set.

Our nomogram, incorporating factors such as Lack of Exercise, Initial TICI Score 1, MRS Score 5, Neutrophil Percentage, Onset Blood Sugar, and Onset NIHSS Score, provides a valuable tool for predicting 6-month mortality in AIS patients post-EVT. It offers potential to refine early clinical decision-making and optimize patient outcomes, reflecting a shift toward more individualized patient care.