Active clinical trials for "Critical Illness"

Introduction: Early and rapid diagnosis of etiology is often an important part of saving the lives of patients in emergency department. Chest CT is an important examination method for emergency diagnosis because of its fast examination speed and accurate localization. Traditional medical imaging diagnosis relies on radiologists to report in a qualitative and subjective manner. Through the interdisciplinary combination of clinical, imaging and artificial intelligence, the integration of multi-omics data, the construction of large-scale language models, and the construction of the auxiliary diagnosis support system of "one check for multiple diseases" provide new ideas and means for the rapid and accurate screening of emergency critical diseases. Method： Study design Investigators retrospectively collected cardiovascular, respiratory, digestive, and neurological CT images, demographic data, medical history and laboratory date of emergency department patients during the period from 1 January 2018 and 30 December 2024. Regularly carry out standardized follow-up work, and complete the collection and database establishment of clinical-imaging multi-omics data of patients attending emergency department.The inclusion criteria are:1. adult emergency patients with cardiovascular, respiratory, digestive, and nervous system diseases; 2. These patients had CT images. Patients with incomplete clinical or radiographic data were excluded from the analysis. Regularly carry out standardized follow-up work, and complete the collection and database establishment of clinical-imaging multi-omics data of patients attending emergency department. Based on the collected medical text data, an artificial intelligence large-scale language model algorithm framework is built. After the structure annotation of chest CT images is performed by doctors above the intermediate level of imaging, the Transformer deep neural network is trained for CT image segmentation, and a series of tasks such as structural structure segmentation, damage detection, disease classification and automatic report generation are developed based on Vision Transformer self-attention architecture mechanism. A multi-disease diagnosis and treatment decision-making system based on chest CT images, clinical text and examination multimodal data was constructed and validated. Disscusion Emergency medicine deals mainly with unpredictable critical and sudden illnesses. Patients who come to the emergency department for medical treatment often have acute onset, hidden condition, rapid progress, many complications, high mortality and disability rate. Assisted diagnosis systems developed by combining clinical text, images and artificial intelligence can greatly improve the ability of emergency department doctors to accurately diagnose diseases. This study fills the blank of CT artificial intelligence aided diagnosis system for emergency patients, and provides a rapid diagnosis scheme for multi-system and multi-disease. Finally, the results will be transformed into clinical application software and used and promoted in clinical work to improve the diagnosis and treatment level.

Acute kidney injury (AKI) is a common disorder and associated with high morbidity and mortality. However, distinguishing transient AKI from persistent AKI may help in individualizing treatment and limit short and long term consequences of AKI. Previous studies suggested usual urinary indices to perform poorly for separating transient from persistent AKI in an unselected population of critically ill patients. The recent KDIGO (Kidney Disease Improving Global Outcomes) guidelines underlined the need for additional strategies in estimating renal short term prognosis. Recently, a Furosemide stress test (FST) was validated in a cohort of unselected critically ill patients. This stress test performance was found to be good in predicting capacity to identify those patients that will progress to advanced stage AKI. Additionally, FST performance was higher than those of usual renal biomarker. The limited sample size of this preliminary study however precluded adjustment for usual confounders including oliguria. The primary objective of this study is to assess diagnostic performance of FST in differentiating transient and persistent AKI. Secondary objectives are to assess diagnostic performance of FST in predicting need for renal replacement therapy, and to confirm FST results after adjustment for confounders.

It is well accepted that during critical illness there is an increase in protein breakdown and loss of lean body mass. Previous studies have shown that during critical illness muscle breakdown increases dramatically. The aim of our study is to test the hypothesis that critically ill patients have improved outcomes with higher protein supplementation.

The goal of this multi-site observational study is to compare delirium rates, days on mechanical ventilation, and Post Intensive Care Syndrome (PICS) rates in adult Intensive Care Unit (ICU) patients. The study will examine patients whose sedation and analgesia infusion titration is managed with both Richmond Agitation and Sedation Scale (RASS) and Processed Electroencephalography (pEEG) monitoring vs patients who receive RASS monitoring alone. The main questions are: Compared to RASS monitoring method alone, does the use of 4 channel pEEG monitor in conjunction with RASS to guide the management of sedation and analgesic in patients who are ventilated reduce the average number of delirium days, measured by Intensive Care Delirium Screening Checklist (ICDSC)? To determine when compared to RASS monitoring alone if the use of 4 channel pEEG monitor in conjunction with RASS to guide the management of Intravenous (IV) sedation and analgesia in ventilated patients reduces the days a patient spends on a mechanical ventilator when compared to RASS only monitoring from retrospective data. To determine when compared to RASS monitoring method alone, does the use of 4 channel pEEG monitor in conjunction with RASS experience lower doses of sedation and analgesia infusions? To determine when compared to RASS monitoring method alone, does the use of 4 channel pEEG monitor in conjunction with RASS experience less incidence and duration of PICS?

The overall objective of this proposal is to quantify the bias in pulse oximeter reported oxygen saturation (SpO2) by evaluating its measures compared to the gold standard blood gas measured arterial oxygen saturation (SaO2) across race and skin pigmentation. The main question that the investigators intend to answer is whether There is greater pulse oximeter bias and subclinical hypoxemia in (1a) Black compared to White infants, and (1b) dark versus light-pigmented infants This bias increases with gestational and postnatal maturation This bias is associated with adverse patient outcomes

A Comparison between CRP, ferritin, and serum zinc as early diagnostic biomarkers of sepsis in critically ill patients

The purpose of this study is to assess fluid accumulation (FA) in the body using BIA (Bioelectrical Impedance Analysis) in critically ill patients treated in the ICU. This study is an observational cohort with an initial phase that analyzes prospective individual patient data

The purpose of this study is to compare the efficacy of ultrasound- guided with the traditional palpation radial artery cannulation in critically ill children.

The investigator will investigate the effects of higher protein/amino acid dosing (≥2.2 g/kg/d) vs usual protein/amino acid dosing (≤1.2 g/kg/d) on clinical outcomes in nutritionally high risk ill patients.

Each year, millions of Americans are admitted to an intensive care unit (ICU). For more than half of them, ICU admission initiates a cascade of decisions about treatment and end-of-life care.This is particularly the case for patients with chronic critical illness, a life-limiting syndrome. Most (74%-82%) ICU patients who require mechanical ventilation have transient or persistent cognitive impairment that precludes them from making their own healthcare decisions. Among ICU patients, the chronically critically ill (CCI) are at highest risk for cognitive impairment and thus require a surrogate decision maker (SDM), usually a family member. SDMs for the critically ill often describe high states of psychological stress associated with the uncertainty of the patient's condition and their decision making role. The purpose of this study is to test the effectiveness of two decision support interventions for end-of-life care delivered to SDMs of CCI patients. This will be the first study to test interventions tailored to the unique needs of the SDMs of CCI patients delivered using an interactive avatar based format.