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

Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Wednesday, November 26, 2025

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

Predictions don't get you recovered! What are the EXACT PROTOCOLS THAT DELIVER RECOVERY? If you can't do proper stroke research; get the hell out and do something simpler like basket weaving!

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

Bing-Hua Lv¹^†

Hao-wei Deng¹^†

Zuo-yv Qin¹

Ning-qin Meng²

Gui-ming Weng¹

Rui-Ting Hu³^*

Chao Qin¹^*

¹Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
²Department of Rheumatology and Immunology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
³Department of Neurology, Minzu Hospital of Guangxi Medical University, Nanning, China

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.