Open Access Data Repository and Common Data Model for Pulse Oximeter Performance Data.

The OpenOximetry Repository is a structured database storing clinical and lab pulse oximetry data, serving as a centralized repository and data model for pulse oximetry initiatives. It supports measurements of arterial oxygen saturation (SaO2) by arterial blood gas co-oximetry and pulse oximetry (SpO2), alongside processed and unprocessed photoplethysmography (PPG) data and other metadata. This includes skin color measurements, finger diameter, vital signs (e.

Predicting Long COVID in the National COVID Cohort Collaborative Using Super Learner: Cohort Study.

Background: Postacute sequelae of COVID-19 (PASC), also known as long COVID, is a broad grouping of a range of long-term symptoms following acute COVID-19. These symptoms can occur across a range of biological systems, leading to challenges in determining risk factors for PASC and the causal etiology of this disorder. An understanding of characteristics that are predictive of future PASC is valuable, as this can inform the identification of high-risk individuals and future preventative efforts.

A deep learning approach for generating intracranial pressure waveforms from extracranial signals routinely measured in the intensive care unit.

Intracranial pressure (ICP) is commonly monitored to guide treatment in patients with serious brain disorders such as traumatic brain injury and stroke. Established methods to assess ICP are resource intensive and highly invasive. We hypothesized that ICP waveforms can be computed noninvasively from three extracranial physiological waveforms routinely acquired in the Intensive Care Unit (ICU): arterial blood pressure (ABP), photoplethysmography (PPG), and electrocardiography (ECG).

Patient-Level Meta-Analysis of Low-Dose Hydrocortisone in Adults with Septic Shock.

BACKGROUND: Trials and study-level meta-analyses have failed to resolve the role of corticosteroids in the management of patients with septic shock. Patient-level meta-analyses may provide more precise estimates of treatment effects, particularly subgroup effects. METHODS: We pooled individual patient data from septic shock trials investigating the adjunctive use of intravenous hydrocortisone.

Association between peripheral perfusion index and postoperative acute kidney injury in major noncardiac surgery patients receiving continuous vasopressors: a post hoc exploratory analysis of the VEGA-1 trial.

Background: The peripheral perfusion index is the ratio of pulsatile to nonpulsatile static blood flow obtained by photoplethysmography and reflects peripheral tissue perfusion. We investigated the association between intraoperative perfusion index and postoperative acute kidney injury in patients undergoing major noncardiac surgery and receiving continuous vasopressor infusions.

Methods: In this exploratory post hoc analysis of a pragmatic, cluster-randomised, multicentre trial, we obtained areas and cumulative times under various thresholds of perfusion index and investigated their association with acute kidney injury in multivariable logistic regression analyses.

IntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE): Training and Validation Study.

Objectives: Elevated intracranial pressure (ICP) is a potentially devastating complication of neurologic injury. Developing an ICP prediction algorithm to help the clinician adjust treatments and potentially prevent elevated ICP episodes.

Design: Retrospective study.

Negativity and Positivity in the ICU: Exploratory Development of Automated Sentiment Capture in the Electronic Health Record.

Objectives: To develop proof-of-concept algorithms using alternative approaches to capture provider sentiment in ICU notes.

Design: Retrospective observational cohort study.

Setting: The Multiparameter Intelligent Monitoring of Intensive Care III (MIMIC-III) and the University of California, San Francisco (UCSF) deidentified notes databases.

Development of a core outcome set for ventilation trials in neurocritical care patients with acute brain injury: protocol for a Delphi consensus study of international stakeholders.

Introduction: There is little consensus and high heterogeneity on the optimal set of relevant clinical outcomes in research studies regarding extubation in neurocritical care patients with brain injury undergoing mechanical ventilation. The aims of this study are to: (1) develop a core outcome set (COS) and (2) reach consensus on a hierarchical composite endpoint for such studies.

Methods And Analysis: The study will include a broadly representative, international panel of stakeholders with research and clinical expertise in this field and will involve four stages: (1) a scoping review to generate an initial list of outcomes represented in the literature, (2) an investigator meeting to review the outcomes for inclusion in the Delphi surveys, (3) four rounds of online Delphi consensus-building surveys and (4) online consensus meetings to finalise the COS and hierarchical composite endpoint.

Development of a Machine Learning Model of Postoperative Acute Kidney Injury Using Non-Invasive Time-Sensitive Intraoperative Predictors.

Acute kidney injury (AKI) is a major postoperative complication that lacks established intraoperative predictors. Our objective was to develop a prediction model using preoperative and high-frequency intraoperative data for postoperative AKI. In this retrospective cohort study, we evaluated 77,428 operative cases at a single academic center between 2016 and 2022.

Availability of information needed to evaluate algorithmic fairness - A systematic review of publicly accessible critical care databases.

Background: Machine learning (ML) may improve clinical decision-making in critical care settings, but intrinsic biases in datasets can introduce bias into predictive models. This study aims to determine if publicly available critical care datasets provide relevant information to identify historically marginalized populations.

Method: We conducted a review to identify the manuscripts that report the training/validation of ML algorithms using publicly accessible critical care electronic medical record (EMR) datasets.

Leveraging observational data to identify targeted patient populations for future randomized trials.

Randomized controlled trials reported in the literature are often affected by poor generalizability, and pragmatic trials have become an increasingly utilized workaround approach to overcome logistical limitations and explore routine interventions demonstrating equipoise in clinical practice. Intravenous albumin, for example, is commonly administered in the perioperative setting despite lacking supportive evidence. Given concerns for cost, safety, and efficacy, randomized trials are needed to explore the clinical equipoise of albumin therapy in this setting, and we therefore present an approach to identifying populations exposed to perioperative albumin to encourage clinical equipoise in patient selection and optimize study design for clinical trials.

Pulse contour techniques for perioperative hemodynamic monitoring: A nationwide carbon footprint and cost estimation.

Background: The question of environmentally sustainable perioperative medicine represents a new challenge in an era of cost constraints and climate crisis. The French Society of Anaesthesia and Intensive Care (SFAR) recommends stroke volume optimization in high-risk surgical patients. Pulse contour techniques have become increasingly popular for stroke volume monitoring during surgery.

Intensive Care Unit activity in France from the national database between 2013 and 2019: More critically ill patients, shorter stay and lower mortality rate.

Background: Knowledge of the occurrence and outcome of admissions to Intensive Care Units (ICU) over time is important to inform healthcare services planning. This observational study aims at describing the activity of French ICUs between 2013 and 2019.

Methods: Patient admission characteristics, organ dysfunction scores, therapies, ICU and hospital lengths of stay and case fatality were collected from the French National Hospital Database (population-based cohort).

Risk factors of long term symptoms and outcomes among patients discharged after covid-19: prospective, multicentre observational study.

Objective: To investigate risk factors and subphenotypes associated with long term symptoms and outcomes after hospital admission for covid-19.

Design: Prospective, multicentre observational study.

Setting: 93 hospitals in France.

Norepinephrine versus phenylephrine for treating hypotension during general anaesthesia in adult patients undergoing major noncardiac surgery: a multicentre, open-label, cluster-randomised, crossover, feasibility, and pilot trial.

Background: Intraoperative hypotension is associated with postoperative complications. The use of vasopressors is often required to correct hypotension but the best vasopressor is unknown.

Methods: A multicentre, cluster-randomised, crossover, feasibility and pilot trial was conducted across five hospitals in California.

Personalized online ensemble machine learning with applications for dynamic data streams.

In this work we introduce the personalized online super learner (POSL), an online personalizable ensemble machine learning algorithm for streaming data. POSL optimizes predictions with respect to baseline covariates, so personalization can vary from completely individualized, that is, optimization with respect to subject ID, to many individuals, that is, optimization with respect to common baseline covariates. As an online algorithm, POSL learns in real time.