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Risk factor identification and prediction models for prolonged length of stay in hospital after acute ischemic stroke using artificial neural networks
ConclusionThe artificial neural network model achieved adequate discriminative power for predicting prolonged length of stay after acute ischemic stroke and identified crucial factors associated with a prolonged hospital stay. The proposed model can assist in clinically assessing the risk of prolonged hospitalization, informing decision-making, and developing individualized medical care plans for patients with acute ischemic stroke.
Source: Frontiers in Neurology - February 9, 2023 Category: Neurology Source Type: research

The potential role of insulin resistance in predicting outcome from intravenous thrombolytic therapy
ConclusionInsulin resistance and metabolic syndrome were associated with worse functional outcomes in non-diabetic stroke patients after receiving rt-PA.
Source: Acta Neurologica Belgica - August 20, 2022 Category: Neurology Source Type: research

Study on the Baseline Factors and Platelet Indices That Predict Outcome of Acute Ischemic Stroke Patients after Thrombolytic Therapy
Conclusions: Overall, lower NIHSS and MPV levels on admission were predictors of good functional outcomes in patients with AIS after undergoing thrombolytic therapy. The correlations between MPV, PDW, and PLR may be helpful to evaluate prognosis in stroke patients and deserve further exploration.Cerebrovasc Dis
Source: Cerebrovascular Diseases - November 17, 2021 Category: Neurology Source Type: research

Correlation between Immune-Inflammatory Markers and Clinical Features in Patients with Acute Ischemic Stroke
CONCLUSIONS: Immune-inflammatory markers dynamically increased from symptom onset of acute ischemic stroke in patients eligible for thrombolytic therapy. Higher levels of immune-inflammatory markers suggest more in-hospital complications and unfavorable short-term outcomes.PMID:34018169
Source: Acta Neurologica Taiwanica - May 21, 2021 Category: Neurology Authors: Yu-Wei Chu Pei-Ya Chen Shinn-Kuang Lin Source Type: research

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