A study of gene expression in the living human brain.

A goal of psychiatric research is to determine the molecular basis of human brain health and illness. One way to achieve this goal is through studies of gene expression in human brain tissue. Due to the unavailability of brain tissue from living people, most such studies are performed using tissue from postmortem brain donors.

1-4-2: Evaluation of applied mechanical power to individual lungs in a simulator-based setting of one ventilator for two patients.

Background: The concept of ventilating multiple patients concurrently using a single ventilator has been proposed as a solution when the demand for ventilators surpasses the available supply. While the practicality of this approach has been established, a thorough evaluation of the risks involved has yet to be comprehensively addressed.

Methods: Two circuits, a simple one (circuit-1) and another with an adjustable resistance valve (circuit-2), were evaluated within an experimental framework utilizing two computer-controlled lung simulators (TestChest and ASL 5000).

A distributional reinforcement learning model for optimal glucose control after cardiac surgery.

This study introduces Glucose Level Understanding and Control Optimized for Safety and Efficacy (GLUCOSE), a distributional offline reinforcement learning algorithm for optimizing insulin dosing after cardiac surgery. Trained on 5228 patients, tested on 920, and externally validated on 649, GLUCOSE achieved a mean estimated reward of 0.0 [-0.

Electronic-Medical-Record-Driven Machine Learning Predictive Model for Hospital-Acquired Pressure Injuries: Development and External Validation.

Hospital-acquired pressure injuries (HAPIs) affect approximately 2.5 million patients annually in the United States, leading to increased morbidity and healthcare costs. Current rule-based screening tools, such as the Braden Scale, lack sensitivity, highlighting the need for improved risk prediction methods.

Perioperative Near Infrared Spectroscopy Measurements of Cerebral Regional Oxygen Desaturations Are Not Associated With Delirium After Cardiac Surgery.

Objective: Postoperative delirium remains a common complication after cardiac surgery in high-risk patients and has been associated with prolonged intensive care unit length of stay, overall morbidity, and mortality. It has been proposed that cerebral hypoperfusion is an important etiological component. In the present study, we retrospectively queried intraoperative near-infrared spectroscopy measurements of regional cerebral oxygen saturations (rSO) during adult cardiac surgical procedures to examine the association between rSO desaturations and postoperative delirium.

Comparison of Perioperative Outcomes for Transfusion of Hemoconcentrated Bypass Versus Cell Saver Blood in Cardiac Surgical Patients.

Objective: Hemoconcentration and cell saver use are blood conservation techniques that are often used in cardiac surgery to salvage the patient's own blood to reduce autologous transfusion. The purpose of this study was to examine the perioperative outcomes including transfusion rates in cardiac surgical patients receiving hemoconcentrated blood versus cell saver blood via retrospective chart review. We hypothesized that hemoconcentration would have better patient outcomes, including reduced transfusion rates, compared to only cell salvage technique.

Association of Intraoperative Occult Hypoxemia With 30-Day and 1-Year Mortality.

Background: Despite the widespread use of pulse oximetry for intraoperative estimation of arterial oxygen saturation, there is growing evidence that certain patient populations may be vulnerable to inaccurate pulse oximetry measurements and that unrecognized hypoxemia is associated with end-organ damage and adverse outcomes. In this single-center retrospective cohort study, we sought to better elucidate the relationship between intraoperative occult hypoxemia and postoperative mortality among patients undergoing anesthesia and surgery.

Methods: Data were collected from our departmental data warehouse for adult patients (≥18 years) undergoing anesthesia between 2008 and 2019 with at least 1 intraoperative arterial blood gas recorded.

Traditional Machine Learning, Deep Learning, and BERT (Large Language Model) Approaches for Predicting Hospitalizations From Nurse Triage Notes: Comparative Evaluation of Resource Management.

Background: Predicting hospitalization from nurse triage notes has the potential to augment care. However, there needs to be careful considerations for which models to choose for this goal. Specifically, health systems will have varying degrees of computational infrastructure available and budget constraints.

Development and Validation of a Deep Learning Classifier Using Chest Radiographs to Predict Extubation Success in Patients Undergoing Invasive Mechanical Ventilation.

The decision to extubate patients on invasive mechanical ventilation is critical; however, clinician performance in identifying patients to liberate from the ventilator is poor. Machine Learning-based predictors using tabular data have been developed; however, these fail to capture the wide spectrum of data available. Here, we develop and validate a deep learning-based model using routinely collected chest X-rays to predict the outcome of attempted extubation.

Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial.

Objectives: Machine learning algorithms can outperform older methods in predicting clinical deterioration, but rigorous prospective data on their real-world efficacy are limited. We hypothesized that real-time machine learning generated alerts sent directly to front-line providers would reduce escalations.

Design: Single-center prospective pragmatic nonrandomized clustered clinical trial.

A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation.

Background: Early prediction of the need for invasive mechanical ventilation (IMV) in patients hospitalized with COVID-19 symptoms can help in the allocation of resources appropriately and improve patient outcomes by appropriately monitoring and treating patients at the greatest risk of respiratory failure. To help with the complexity of deciding whether a patient needs IMV, machine learning algorithms may help bring more prognostic value in a timely and systematic manner. Chest radiographs (CXRs) and electronic medical records (EMRs), typically obtained early in patients admitted with COVID-19, are the keys to deciding whether they need IMV.

Machine learning using institution-specific multi-modal electronic health records improves mortality risk prediction for cardiac surgery patients.

Background: The Society of Thoracic Surgeons risk scores are widely used to assess risk of morbidity and mortality in specific cardiac surgeries but may not perform optimally in all patients. In a cohort of patients undergoing cardiac surgery, we developed a data-driven, institution-specific machine learning-based model inferred from multi-modal electronic health records and compared the performance with the Society of Thoracic Surgeons models.

Methods: All adult patients undergoing cardiac surgery between 2011 and 2016 were included.

A study of gene expression in the living human brain.

A goal of medical research is to determine the molecular basis of human brain health and illness. One way to achieve this goal is through observational studies of gene expression in human brain tissue. Due to the unavailability of brain tissue from living people, most such studies are performed using tissue from postmortem brain donors.

A Web-Based Reporting System for Reviewing Local Practice Patterns of Anesthesiologists Derived from the Electronic Medical Record.

After completion of training, anesthesiologists may have fewer opportunities to see how colleagues practice, and their breadth of case experiences may also diminish due to specialization. We created a web-based reporting system based on data extracted from electronic anesthesia records that allows practitioners to see how other clinicians practice in similar cases. One year after implementation, the system continues to be utilized by clinicians.

Informative Censoring-A Cause of Bias in Estimating COVID-19 Mortality Using Hospital Data.

(1) Background: Several retrospective observational analyzed treatment outcomes for COVID-19; (2) Methods: Inverse probability of censoring weighting (IPCW) was applied to correct for bias due to informative censoring in database of hospitalized patients who did and did not receive convalescent plasma; (3) Results: When compared with an IPCW analysis, overall mortality was overestimated using an unadjusted Kaplan-Meier curve, and hazard ratios for the older age group compared to the youngest were underestimated using the Cox proportional hazard models and 30-day mortality; (4) Conclusions: An IPCW analysis provided stabilizing weights by hospital admission.

Plasma Renin Activity Increases With Cardiopulmonary Bypass and is Associated With Vasoplegia After Cardiac Surgery.

Objectives: To describe the trend in plasma renin activity over time in patients undergoing cardiac surgery on cardiopulmonary bypass, and to investigate if increased plasma renin activity is associated with postcardiopulmonary bypass vasoplegia.

Design: A prospective cohort study.

Setting: Patients were enrolled from June 2020 to May 2021 at a tertiary cardiac surgical institution.

Predicting Adult Hospital Admission from Emergency Department Using Machine Learning: An Inclusive Gradient Boosting Model.

Background And Aim: We analyzed an inclusive gradient boosting model to predict hospital admission from the emergency department (ED) at different time points. We compared its results to multiple models built exclusively at each time point.

Methods: This retrospective multisite study utilized ED data from the Mount Sinai Health System, NY, during 2015-2019.

Automated electrocardiogram signal quality assessment based on Fourier analysis and template matching.

We developed and tested a novel template matching approach for signal quality assessment on electrocardiogram (ECG) data. A computational method was developed that uses a sinusoidal approximation to the QRS complex to generate a correlation value at every point of an ECG. The strength of this correlation can be numerically adapted into a 'score' for each segment of an ECG, which can be used to stratify signal quality.

How businesses are working together to deliver NASA/JPL-designed ventilators to the world in the fight against COVID-19.

In response to the COVID-19 pandemic, NASA Jet Propulsion Laboratory (JPL) engineers had embarked on an ambitious project to design a reliable, easy-to-use, and low-cost ventilator that was made of readily available parts to address the unexpected global shortage of these lifesaving devices. After successfully designing and building the VITAL (Ventilator Intervention Technology Accessible Locally) ventilator in record time, FDA Emergency Use Authorization (EUA) was obtained and then the license to manufacture and sell these ventilators was made available to select companies through a competitive process. STARK Industries, LLC (STARK), located in Columbus, OH, USA, was one of only eight U.

The fourth wave: vaccination status and intensive care unit mortality at a large hospital system in New York City.

Background: We aim to describe the demographics and outcomes of patients with severe disease with the Omicron variant. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus continues to mutate, and the availability of vaccines and boosters continue to rise, it is important to understand the health care burden of new variants. We analyze patients admitted to intensive care units (ICUs) in a large Academic Health System during New York City's fourth surge beginning on November 27, 2021.

Proteomic Characterization of Acute Kidney Injury in Patients Hospitalized with SARS-CoV2 Infection.

Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. Using measurements of ∼4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction.

Machine learning to predict in-hospital mortality among patients with severe obesity: Proof of concept study.

Objectives: Hospitalized patients with severe obesity require adapted hospital management. The aim of this study was to evaluate a machine learning model to predict in-hospital mortality among this population.

Methods: Data of unselected consecutive emergency department admissions of hospitalized patients with severe obesity (BMI ≥ 40 kg/m) was analyzed.