Non-invasive biomarkers for detecting progression toward hypovolemic cardiovascular instability in a lower body negative pressure model.

Occult hemorrhages after trauma can be present insidiously, and if not detected early enough can result in patient death. This study evaluated a hemorrhage model on 18 human subjects, comparing the performance of traditional vital signs to multiple off-the-shelf non-invasive biomarkers. A validated lower body negative pressure (LBNP) model was used to induce progression towards hypovolemic cardiovascular instability.

Prediction of Occult Hemorrhage in the Lower Body Negative Pressure Model: Initial Validation of Machine Learning Approaches.

Introduction: Detection of occult hemorrhage (OH) before progression to clinically apparent changes in vital signs remains an important clinical problem in managing trauma patients. The resource-intensiveness associated with continuous clinical patient monitoring and rescue from frank shock makes accurate early detection and prediction with noninvasive measurement technology a desirable innovation. Despite significant efforts directed toward the development of innovative noninvasive diagnostics, the implementation and performance of the newest bedside technologies remain inadequate.

Changing Trend of Risk Factors of Mucormycosis Including Diabetes, Acidosis, and Serum Iron in the Second Wave of COVID-19.

To analyze the clinic-pathological profile of patients presenting with mucormycosis infection to a tertiary care center during the second wave of the COVID-19 pandemic in India. This descriptive cross-sectional study was conducted in a hospital setting from April 2021 to July 2021 and analysis was carried out to find associations between the stratified data and the extent of the disease involvement based on radiological findings. Statistical tests like percentage, average, chi-square test, etc.

Spatial and temporal patterns in dynamic-contrast enhanced intraoperative fluorescence imaging enable classification of bone perfusion in patients undergoing leg amputation.

Dynamic contrast-enhanced fluorescence imaging (DCE-FI) classification of tissue viability in twelve adult patients undergoing below knee leg amputation is presented. During amputation and with the distal bone exposed, indocyanine green contrast-enhanced images were acquired sequentially during baseline, following transverse osteotomy and following periosteal stripping, offering a uniquely well-controlled fluorescence dataset. An unsupervised classification machine leveraging 21 different spatiotemporal features was trained and evaluated by cross-validation in 3.

The first case of proliferative fasciitis of tongue coexistent with squamous cell carcinoma: Case report of a rare lesion.

Proliferative fasciitis (PF) is a rare pseudosarcomatous myofibroblastic benign tumor, a subcutaneous counterpart of proliferative myositis. Usually seen in upper extremities, no case has yet been documented in tongue or any other subsites in oral cavity. The present case becomes the first to be reported at this site as well as the first case of synchronous coexistent PF with squamous cell carcinoma (SCC) of tongue.

Detection of subclinical hemorrhage using electrical impedance: a porcine study.

Analyze the performance of electrical impedance tomography (EIT) in an innovative porcine model of subclinical hemorrhage and investigate associations between EIT and hemodynamic trends.. Twenty-five swine were bled at slow rates to create an extended period of subclinical hemorrhage during which the animal's heart rate (HR) and blood pressure (BP) remained stable from before hemodynamic deterioration, where stable was defined as <15% decrease in BP and <20% increase in HRi.

Predicting Future Occurrence of Acute Hypotensive Episodes Using Noninvasive and Invasive Features.

Introduction: Early prediction of the acute hypotensive episode (AHE) in critically ill patients has the potential to improve outcomes. In this study, we apply different machine learning algorithms to the MIMIC III Physionet dataset, containing more than 60,000 real-world intensive care unit records, to test commonly used machine learning technologies and compare their performances.

Materials And Methods: Five classification methods including K-nearest neighbor, logistic regression, support vector machine, random forest, and a deep learning method called long short-term memory are applied to predict an AHE 30 minutes in advance.

Early Detection of Hypotension Using a Multivariate Machine Learning Approach.

Introduction: The ability to accurately detect hypotension in trauma patients at the earliest possible time is important in improving trauma outcomes. The earlier an accurate detection can be made, the more time is available to take corrective action. Currently, there is limited research on combining multiple physiological signals for an early detection of hemorrhagic shock.

Stochastic programming for outpatient scheduling with flexible inpatient exam accommodation.

This study is concerned with the determination of an optimal appointment schedule in an outpatient-inpatient hospital system where the inpatient exams can be cancelled based on certain rules while the outpatient exams cannot be cancelled. Stochastic programming models were formulated and solved to tackle the stochasticity in the procedure durations and patient arrival patterns. The first model, a two-stage stochastic programming model, is formulated to optimize the slot size.

Simulation Analysis and Comparison of Point of Care Testing and Central Laboratory Testing.

In response to demand for fast and efficient clinical testing, the use of point-of-care testing (POCT) has become increasingly common in the United States. However, studies of POCT implementation have found that adopting POCT may not always be advantageous relative to centralized laboratory testing. We construct a simulation model of patient flow in an outpatient care setting to evaluate tradeoffs involved in POCT implementation across multiple dimensions, comparing measures of patient outcomes in varying clinical scenarios, testing regimes, and patient conditions.