Optimizing early neurological deterioration prediction in acute ischemic stroke patients following intravenous thrombolysis: a LASSO regression model approach
BackgroundAcute ischemic stroke (AIS) remains a leading cause of disability and mortality globally among adults. Despite Intravenous Thrombolysis (IVT) with recombinant tissue plasminogen activator (rt-PA) emerging as the standard treatment for AIS, approximately 6–40% of patients undergoing IVT exp...
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| Format: | Article |
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Frontiers Media S.A.
2024-04-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2024.1390117/full |
| _version_ | 1797224744862351360 |
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| author | Ning Li Ying-Lei Li Jia-Min Shao Chu-Han Wang Si-Bo Li Ye Jiang |
| author_facet | Ning Li Ying-Lei Li Jia-Min Shao Chu-Han Wang Si-Bo Li Ye Jiang |
| author_sort | Ning Li |
| collection | DOAJ |
| description | BackgroundAcute ischemic stroke (AIS) remains a leading cause of disability and mortality globally among adults. Despite Intravenous Thrombolysis (IVT) with recombinant tissue plasminogen activator (rt-PA) emerging as the standard treatment for AIS, approximately 6–40% of patients undergoing IVT experience Early Neurological Deterioration (END), significantly impacting treatment efficacy and patient prognosis.ObjectiveThis study aimed to develop and validate a predictive model for END in AIS patients post rt-PA administration using the Least Absolute Shrinkage and Selection Operator (LASSO) regression approach.MethodsIn this retrospective cohort study, data from 531 AIS patients treated with intravenous alteplase across two hospitals were analyzed. LASSO regression was employed to identify significant predictors of END, leading to the construction of a multivariate predictive model.ResultsSix key predictors significantly associated with END were identified through LASSO regression analysis: previous stroke history, Body Mass Index (BMI), age, Onset to Treatment Time (OTT), lymphocyte count, and glucose levels. A predictive nomogram incorporating these factors was developed, effectively estimating the probability of END post-IVT. The model demonstrated robust predictive performance, with an Area Under the Curve (AUC) of 0.867 in the training set and 0.880 in the validation set.ConclusionThe LASSO regression-based predictive model accurately identifies critical risk factors leading to END in AIS patients following IVT. This model facilitates timely identification of high-risk patients by clinicians, enabling more personalized treatment strategies and optimizing patient management and outcomes. |
| first_indexed | 2024-04-24T13:57:59Z |
| format | Article |
| id | doaj.art-9260dc5532bd4d31901405d04fa2f8f9 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-24T13:57:59Z |
| publishDate | 2024-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-9260dc5532bd4d31901405d04fa2f8f92024-04-03T15:48:15ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2024-04-011810.3389/fnins.2024.13901171390117Optimizing early neurological deterioration prediction in acute ischemic stroke patients following intravenous thrombolysis: a LASSO regression model approachNing Li0Ying-Lei Li1Jia-Min Shao2Chu-Han Wang3Si-Bo Li4Ye Jiang5Department of Neurology, Affiliated Hospital of Hebei University, Baoding, ChinaDepartment of Emergency Medicine, Baoding No.1 Central Hospital, Baoding, ChinaDepartment of Neurology, Affiliated Hospital of Hebei University, Baoding, ChinaDepartment of Neurology, Affiliated Hospital of Hebei University, Baoding, ChinaDepartment of Neurology, Affiliated Hospital of Hebei University, Baoding, ChinaDepartment of Neurology, Affiliated Hospital of Hebei University, Baoding, ChinaBackgroundAcute ischemic stroke (AIS) remains a leading cause of disability and mortality globally among adults. Despite Intravenous Thrombolysis (IVT) with recombinant tissue plasminogen activator (rt-PA) emerging as the standard treatment for AIS, approximately 6–40% of patients undergoing IVT experience Early Neurological Deterioration (END), significantly impacting treatment efficacy and patient prognosis.ObjectiveThis study aimed to develop and validate a predictive model for END in AIS patients post rt-PA administration using the Least Absolute Shrinkage and Selection Operator (LASSO) regression approach.MethodsIn this retrospective cohort study, data from 531 AIS patients treated with intravenous alteplase across two hospitals were analyzed. LASSO regression was employed to identify significant predictors of END, leading to the construction of a multivariate predictive model.ResultsSix key predictors significantly associated with END were identified through LASSO regression analysis: previous stroke history, Body Mass Index (BMI), age, Onset to Treatment Time (OTT), lymphocyte count, and glucose levels. A predictive nomogram incorporating these factors was developed, effectively estimating the probability of END post-IVT. The model demonstrated robust predictive performance, with an Area Under the Curve (AUC) of 0.867 in the training set and 0.880 in the validation set.ConclusionThe LASSO regression-based predictive model accurately identifies critical risk factors leading to END in AIS patients following IVT. This model facilitates timely identification of high-risk patients by clinicians, enabling more personalized treatment strategies and optimizing patient management and outcomes.https://www.frontiersin.org/articles/10.3389/fnins.2024.1390117/fullacute ischemic stroke (AIS)intravenous thrombolysis (IVT)early neurological deterioration (END)LASSO regressionpredictive modeling |
| spellingShingle | Ning Li Ying-Lei Li Jia-Min Shao Chu-Han Wang Si-Bo Li Ye Jiang Optimizing early neurological deterioration prediction in acute ischemic stroke patients following intravenous thrombolysis: a LASSO regression model approach Frontiers in Neuroscience acute ischemic stroke (AIS) intravenous thrombolysis (IVT) early neurological deterioration (END) LASSO regression predictive modeling |
| title | Optimizing early neurological deterioration prediction in acute ischemic stroke patients following intravenous thrombolysis: a LASSO regression model approach |
| title_full | Optimizing early neurological deterioration prediction in acute ischemic stroke patients following intravenous thrombolysis: a LASSO regression model approach |
| title_fullStr | Optimizing early neurological deterioration prediction in acute ischemic stroke patients following intravenous thrombolysis: a LASSO regression model approach |
| title_full_unstemmed | Optimizing early neurological deterioration prediction in acute ischemic stroke patients following intravenous thrombolysis: a LASSO regression model approach |
| title_short | Optimizing early neurological deterioration prediction in acute ischemic stroke patients following intravenous thrombolysis: a LASSO regression model approach |
| title_sort | optimizing early neurological deterioration prediction in acute ischemic stroke patients following intravenous thrombolysis a lasso regression model approach |
| topic | acute ischemic stroke (AIS) intravenous thrombolysis (IVT) early neurological deterioration (END) LASSO regression predictive modeling |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2024.1390117/full |
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