The unsupervised machine learning to analyze the use strategy of statins for ischaemic stroke patients with elevated transaminase.
Ischemic stroke
Statins
Supervised machine learning
Transaminase
Unsupervised machine learning
Journal
Clinical neurology and neurosurgery
ISSN: 1872-6968
Titre abrégé: Clin Neurol Neurosurg
Pays: Netherlands
ID NLM: 7502039
Informations de publication
Date de publication:
09 2023
09 2023
Historique:
received:
27
02
2023
revised:
02
07
2023
accepted:
15
07
2023
medline:
15
9
2023
pubmed:
22
7
2023
entrez:
21
7
2023
Statut:
ppublish
Résumé
Statins could elevate hepatic transaminase in ischemic stroke patients. There needed to be more evidence on which method stopped statins or adjusting the dose of statins was better for patients. And no evidence showed which way more suit for some patients. We collected ischaemic stroke patients with elevated hepatic transaminase when they take statins. The outcome was a recurrent stroke rate, transaminase value after stopping or adjusted, mortality, and favorable functional outcome (FFO). We compare outcome events between the stopped group and the adjustment group. We grouped all patients by unsupervised machine learning and analyzed data characters by the different groups. The patients stopping statins had a higher stroke recurrence and rate of FFO (mRS 0-2), a lower mean value of transaminase, and mortality. By difference unsupervised machine learning group, the km2 group had the lowest stroke recurrence (p = 0.046), lowest mortality (p = 0.049), and highest FFO (p = 0.023). The patients of the km2 group were younger (p < 0.001), more male (p < 0.001), had lesser National Institutes of Health Stroke Scale (NIHSS) scores (p < 0.001), and had slightly higher values of blood pressure (p = 0.002). The group of unsupervised machine learning could improve models' performance. For ischemic patients with elevated hepatic transaminase, stopping statins temporarily was a better choice of treatment strategy. These patients who were younger, male, with a lesser NIHSS score at admission and a slightly higher blood lipid value at admission, could have had a better prognosis.
Sections du résumé
BACKGROUND AND PURPOSE
Statins could elevate hepatic transaminase in ischemic stroke patients. There needed to be more evidence on which method stopped statins or adjusting the dose of statins was better for patients. And no evidence showed which way more suit for some patients.
METHODS
We collected ischaemic stroke patients with elevated hepatic transaminase when they take statins. The outcome was a recurrent stroke rate, transaminase value after stopping or adjusted, mortality, and favorable functional outcome (FFO). We compare outcome events between the stopped group and the adjustment group. We grouped all patients by unsupervised machine learning and analyzed data characters by the different groups.
RESULTS
The patients stopping statins had a higher stroke recurrence and rate of FFO (mRS 0-2), a lower mean value of transaminase, and mortality. By difference unsupervised machine learning group, the km2 group had the lowest stroke recurrence (p = 0.046), lowest mortality (p = 0.049), and highest FFO (p = 0.023). The patients of the km2 group were younger (p < 0.001), more male (p < 0.001), had lesser National Institutes of Health Stroke Scale (NIHSS) scores (p < 0.001), and had slightly higher values of blood pressure (p = 0.002). The group of unsupervised machine learning could improve models' performance.
CONCLUSION
For ischemic patients with elevated hepatic transaminase, stopping statins temporarily was a better choice of treatment strategy. These patients who were younger, male, with a lesser NIHSS score at admission and a slightly higher blood lipid value at admission, could have had a better prognosis.
Identifiants
pubmed: 37478641
pii: S0303-8467(23)00316-5
doi: 10.1016/j.clineuro.2023.107900
pii:
doi:
Substances chimiques
Hydroxymethylglutaryl-CoA Reductase Inhibitors
0
Transaminases
EC 2.6.1.-
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
107900Informations de copyright
Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest Chaohua Cui, Yuchuan Li, Shaohui Liu, Ping Wang, and Zhonghua Huang declared that they have no potential conflicts of interest that might be relevant to the contents of this manuscript.