AIMC Topic: Hypotension

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Intraoperative Hypotension Prediction: Current Methods, Controversies, and Research Outlook.

Anesthesia and analgesia
Intraoperative hypotension prediction has been increasingly emphasized due to its potential clinical value in reducing organ injury and the broad availability of large-scale patient datasets and powerful machine learning tools. Hypotension prediction...

Machine learning-augmented interventions in perioperative care: a systematic review and meta-analysis.

British journal of anaesthesia
BACKGROUND: We lack evidence on the cumulative effectiveness of machine learning (ML)-driven interventions in perioperative settings. Therefore, we conducted a systematic review to appraise the evidence on the impact of ML-driven interventions on per...

A review of machine learning methods for non-invasive blood pressure estimation.

Journal of clinical monitoring and computing
Blood pressure is a very important clinical measurement, offering valuable insights into the hemodynamic status of patients. Regular monitoring is crucial for early detection, prevention, and treatment of conditions like hypotension and hypertension,...

Prediction of intraoperative hypotension using deep learning models based on non-invasive monitoring devices.

Journal of clinical monitoring and computing
PURPOSE: Intraoperative hypotension is associated with adverse outcomes. Predicting and proactively managing hypotension can reduce its incidence. Previously, hypotension prediction algorithms using artificial intelligence were developed for invasive...

Predictive ability of hypotension prediction index and machine learning methods in intraoperative hypotension: a systematic review and meta-analysis.

Journal of translational medicine
INTRODUCTION: Intraoperative Hypotension (IOH) poses a substantial risk during surgical procedures. The integration of Artificial Intelligence (AI) in predicting IOH holds promise for enhancing detection capabilities, providing an opportunity to impr...

LDSG-Net: an efficient lightweight convolutional neural network for acute hypotensive episode prediction during ICU hospitalization.

Physiological measurement
. Acute hypotension episode (AHE) is one of the most critical complications in intensive care unit (ICU). A timely and precise AHE prediction system can provide clinicians with sufficient time to respond with proper therapeutic measures, playing a cr...

Machine learning model based on RCA-PDCA nursing methods and differentiating factors to predict hypotension during cesarean section surgery.

Computers in biology and medicine
BACKGROUND: Intraoperative hypotension during cesarean section has become a serious complication for maternal and fetal healthy. It is commonly encountered by subarachnoid anesthesia. However, currently used control methods have varying degrees of si...

Effect of different targets of goal-directed fluid therapy on intraoperative hypotension and fluid infusion in robot-assisted laparoscopic gynecological surgery: a randomized non-inferiority trial.

Journal of robotic surgery
Carotid corrected flow time (FTc) and tidal volume challenge pulse pressure variation (VtPPV) are useful clinical parameters for assessing volume status and fluid responsiveness in robot-assisted surgery, but their usefulness as goal-directed fluid t...

Incidence, Determinants, and Outcome of Contrast-induced Acute Kidney Injury following Percutaneous Coronary Intervention at a Tertiary Care Hospital.

Saudi journal of kidney diseases and transplantation : an official publication of the Saudi Center for Organ Transplantation, Saudi Arabia
Contrast-induced acute kidney injury (CI-AKI) after percutaneous coronary intervention (PCI) is the common cause of in-hospital acquired AKI and is associated with in-hospital mortality and prolonged hospital stay. We studied the incidence of CI-AKI ...

Real-time dual prediction of intradialytic hypotension and hypertension using an explainable deep learning model.

Scientific reports
Both intradialytic hypotension (IDH) and hypertension (IDHTN) are associated with poor outcomes in hemodialysis patients, but a model predicting dual outcomes in real-time has never been developed. Herein, we developed an explainable deep learning mo...