A machine learning-based predictive nomogram for early neurological improvement after thrombolysis in acute ischemic stroke

TL;DR

This study creates a machine learning model to predict early neurological improvement in stroke patients treated with clot-busting drugs.

Contribution

A novel machine learning-based nomogram is developed to predict early neurological improvement after stroke treatment.

Findings

The MLP model achieved the highest AUC of 0.77 for predicting early neurological improvement.

Six key clinical and biochemical parameters were identified as core predictors of improvement.

The nomogram demonstrated strong predictive ability with a C-index of 0.817.

Abstract

Early neurological improvement (ENI) is a critical prognostic indicator for acute ischemic stroke (AIS) patients undergoing intravenous thrombolysis with recombinant tissue plasminogen activator (rt-PA). This study aimed to develop and validate a machine learning (ML)-based model for predicting ENI using clinical and biochemical data.

Clinical data from 217 AIS patients (97 ENI, 120 non-ENI) were retrospectively analyzed. Significant baseline differences were identified between groups, including hemorrhage, onset-to-needle time (ONT), neutrophil-to-lymphocyte ratio (NLR), weight, and activated partial thromboplastin time (APTT). Four ML algorithms, including Multilayer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), and XGBoost, were implemented. Model performance was evaluated via area under the receiver operating characteristic curve (AUC). Key predictors were identified by intersecting top-ranked features from all algorithms, followed by logistic regression modeling and nomogram visualization.

The MLP model achieved the highest AUC (0.77) in the testing set, outperforming RF (0.72), SVM (0.63), and XGBoost (0.68). Six overlapping parameters, including APTT, ALT/AST ratio, ONT, mean corpuscular hemoglobin concentration (MCHC), weight, and NLR, were selected as core predictors. The logistic regression model incorporating these parameters yielded an AUC of 0.74, while the nomogram demonstrated that the predictive model exhibited strong discriminative ability (C-index: 0.817) for predicting ENI in rt-PA-treated AIS patients.

This ML-based model effectively predicts ENI in rt-PA-treated AIS patients by integrating critical clinical and biochemical markers. Its application may optimize personalized treatment strategies, enhance clinical decision-making, and improve patient outcomes.

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