Publications by authors named "Shiteng Lin"
Objective: Low cardiac output syndrome (LCOS) is a severe complication after valve surgery, with no uniform standard for early identification. We developed interpretative machine learning (ML) models for predicting LCOS risk preoperatively and 0.5 h postoperatively for intervention in advance.
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- This study focused on developing nomogram models to predict unfavorable outcomes in patients with basilar artery occlusion (BAO) who underwent mechanical thrombectomy (MT).
- Researchers analyzed data from 127 BAO patients, ultimately including 117 in their findings, and created both preoperative and postoperative models with strong predictive capabilities.
- The models identified key predictors for unfavorable outcomes, such as previous stroke and NIHSS scores, and aim to enhance clinical decision-making for patient selection and post-stroke management.
Background: Even undergoing mechanical thrombectomy (MT), patients with acute vertebrobasilar artery occlusion (AVBAO) still have a high rate of mortality. Tirofiban is a novel antiplatelet agent which is now widely empirically used in acute ischemic stroke (AIS). In this study, we aimed to evaluate the safety and efficacy of tirofiban as adjunctive therapy for MT in AVBAO.
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- The study aimed to create a visual nomogram model to help identify patients with basilar artery occlusion (BAO) who are at high risk of having futile recanalization after endovascular thrombectomy (EVT).
- Researchers analyzed data from 84 BAO patients treated with EVT, finding that 50% experienced futile recanalization.
- The resulting nomogram model demonstrated strong predictive capability, with a high accuracy score (AUC of 0.866) and is available online for clinicians to use in patient assessment.
Treatment for mild stroke remains an open question. We aim to develop a decision support tool based on machine learning (ML) algorithms, called DAMS (Disability After Mild Stroke), to identify mild stroke patients who would be at high risk of post-stroke disability (PSD) if they only received medical therapy and, more importantly, to aid neurologists in making individual clinical decisions in emergency contexts. Ischemic stroke patients were prospectively recorded in the National Advanced Stroke Center of Nanjing First Hospital (China) between July 2016 and September 2020.
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- Intracranial aneurysms (IAs) are a significant health issue, and while endovascular treatment (EVT) is a common management strategy, there's a notable risk of recurrence leading to serious complications.
- The study aimed to create and evaluate machine learning (ML) models to predict the recurrence risk of IAs within 6 months after EVT, utilizing data from patients treated at Hunan Provincial People's Hospital from 2016 to 2019.
- Among five developed ML models, the gradient boosting decision tree (GBDT) model outperformed the others, achieving an area under the curve (AUC) of 0.842, indicating it can effectively predict the risk of recurrence in this patient population.