Prognostic prediction of thrombolytic therapy with rt-PA in acute ischemic stroke patients using an artificial intelligence model.

Miao Chen, Liya Zhou, Limin Fan, Yanhong Li, Hairong Wang

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Article in American journal of translational research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors.

Miao ChenDepartment of Emergency, The First Affiliated Hospital of Hainan Medical University Haikou 570102, Hainan, China.

Liya ZhouInformation Management and Engineering, Shanghai University of Finance and Economics Shanghai 200433, China.

Limin FanThe Institute for Biomedical Engineering and Nano Science, Tongji University School of Medicine Shanghai 200092, China.

Yanhong LiInformation Management and Engineering, Shanghai University of Finance and Economics Shanghai 200433, China.

Hairong WangDepartment of Emergency, Xinhua Hospital Affiliated to Shanghai Jiaotong University, School of Medicine Shanghai 200092, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThrough the comparison of different prediction models, we hope to find a promising statistical method to evaluate the prognosis of patients with acute ischemic stroke (AIS) after thrombolytic therapy.

methodsData of 518 patients who received thrombolytic therapy were retrospectively collected in this study. Among them, 362 patients met the eligibility criteria, so their data such as age, sex, smoking history, previous medical history, clinical and laboratory indicators were analyzed. According to the 3 month follow-up results, 266 patients were included in a good prognosis group (modified Rankin Scale (mRS) score ≤2) and 96 in a poor prognosis group (3≤mRS≤6). All variables with P<0.05 in univariant and multivariant analyses were assigned in logistic regression model and artificial neural network (ANN) model to predict neurological prognosis, and the performance of the two models were compared.

resultsAge, NIHSS scores, the serum concentration of immediate glucose, APTT and MBP at admission were found to be the predictive factors through ANN and logistic regression analysis. The binary logistic regression model revealed that the percentage correction, the precision, recall and F1 score of the regression model were 79%, 69.23%, 37.50% and 48.65%, respectively. While those of ANN were 79.98%, 69.70%, 37.25%, and 49.66%, correspondingly.

conclusionsANN model is as effective as a logistic regression model in predicting the prognosis of AIS after thrombolytic therapy with rt-PA. Moreover, ANN is slightly superior to logistic regression in accuracy, precision and F1 score.

Indexed as

Acute ischemic strokeartificial neural networkintra-venous thrombolytic therapyprediction modelprognosis

Identifiers

PMID37560207

PMCPMC10408517

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