The Precision Resuscitation With Crystalloids in Sepsis (PRECISE) Trial: A Trial Protocol.

Importance: Intravenous fluids are an essential part of treatment in sepsis, but there remains clinical equipoise on which type of crystalloid fluids to use in sepsis. A previously reported sepsis subphenotype (ie, group D) has demonstrated a substantial mortality benefit from balanced crystalloids compared with normal saline.

Objective: To test the hypothesis that targeting balanced crystalloids to patients with group D sepsis through an electronic health record (EHR) alert will reduce 30-day inpatient mortality.

Should AI models be explainable to clinicians?

In the high-stakes realm of critical care, where daily decisions are crucial and clear communication is paramount, comprehending the rationale behind Artificial Intelligence (AI)-driven decisions appears essential. While AI has the potential to improve decision-making, its complexity can hinder comprehension and adherence to its recommendations. "Explainable AI" (XAI) aims to bridge this gap, enhancing confidence among patients and doctors.

Moderate IV Fluid Resuscitation Is Associated With Decreased Sepsis Mortality.

Objectives: Significant practice variation exists in the amount of resuscitative IV fluid given to patients with sepsis. Current research suggests equipoise between a tightly restrictive or more liberal strategy but data is lacking on a wider range of resuscitation practices. We sought to examine the relationship between a wide range of fluid resuscitation practices and sepsis mortality and then identify the primary driver of this practice variation.

A Delphi Process to Identify Relevant Outcomes That May Be Associated With a Predictive Analytic Tool to Detect Hemodynamic Deterioration in the Intensive Care Unit.

Background The critical care literature has seen an increase in the development and validation of tools using artificial intelligence for early detection of patient events or disease onset in the intensive care unit (ICU). The hemodynamic stability index (HSI) was found to have an AUC of 0.82 in predicting the need for hemodynamic intervention in the ICU.

A MACHINE LEARNING MODEL DERIVED FROM ANALYSIS OF TIME-COURSE GENE-EXPRESSION DATASETS REVEALS TEMPORALLY STABLE GENE MARKERS PREDICTIVE OF SEPSIS MORTALITY.

Sepsis is associated with significant mortality and morbidity among critically ill patients admitted to intensive care units and represents a major health challenge globally. Given the significant clinical and biological heterogeneity among patients and the dynamic nature of the host immune response, identifying those at high risk of poor outcomes remains a critical challenge. Here, we performed secondary analysis of publicly available time-series gene-expression datasets from peripheral blood of patients admitted to the intensive care unit to elucidate temporally stable gene-expression markers between sepsis survivors and nonsurvivors.

HIRA: Heart Rate Interval based Rapid Alert score to characterize autonomic dysfunction among patients with sepsis-related acute respiratory failure (ARF).

. To examine whether heart rate interval based rapid alert (HIRA) score derived from a combination model of heart rate variability (HRV) and modified early warning score (MEWS) is a surrogate for the detection of acute respiratory failure (ARF) in critically ill sepsis patients..

Overt and Occult Hypoxemia in Patients Hospitalized With COVID-19.

Unlabelled: Progressive hypoxemia is the predominant mode of deterioration in COVID-19. Among hypoxemia measures, the ratio of the Pao to the Fio (P/F ratio) has optimal construct validity but poor availability because it requires arterial blood sampling. Pulse oximetry reports oxygenation continuously (ratio of the Spo to the Fio [S/F ratio]), but it is affected by skin color and occult hypoxemia can occur in Black patients.

Serial Measurements of Protein Biomarkers in Sepsis-Induced Acute Respiratory Distress Syndrome.

Unlabelled: The role of early, serial measurements of protein biomarkers in sepsis-induced acute respiratory distress syndrome (ARDS) is not clear.

Objectives: To determine the differences in soluble receptor for advanced glycation end-products (sRAGEs), angiopoietin-2, and surfactant protein-D (SP-D) levels and their changes over time between sepsis patients with and without ARDS.

Design Setting And Participants: Prospective observational cohort study of adult patients admitted to the medical ICU at Grady Memorial Hospital within 72 hours of sepsis diagnosis.

Overt and occult hypoxemia in patients hospitalized with novel coronavirus disease 2019.

Background: Progressive hypoxemia is the predominant mode of deterioration in COVID-19. Among hypoxemia measures, the ratio of the partial pressure of arterial oxygen to the fraction of inspired oxygen (P/F ratio) has optimal construct validity but poor availability because it requires arterial blood sampling. Pulse oximetry reports oxygenation continuously, but occult hypoxemia can occur in Black patients because the technique is affected by skin color.

Leveraging clinical data across healthcare institutions for continual learning of predictive risk models.

The inherent flexibility of machine learning-based clinical predictive models to learn from episodes of patient care at a new institution (site-specific training) comes at the cost of performance degradation when applied to external patient cohorts. To exploit the full potential of cross-institutional clinical big data, machine learning systems must gain the ability to transfer their knowledge across institutional boundaries and learn from new episodes of patient care without forgetting previously learned patterns. In this work, we developed a privacy-preserving learning algorithm named WUPERR (Weight Uncertainty Propagation and Episodic Representation Replay) and validated the algorithm in the context of early prediction of sepsis using data from over 104,000 patients across four distinct healthcare systems.

Coronavirus Disease 2019 Temperature Trajectories Correlate With Hyperinflammatory and Hypercoagulable Subphenotypes.

Objectives: Body temperature trajectories of infected patients are associated with specific immune profiles and survival. We determined the association between temperature trajectories and distinct manifestations of coronavirus disease 2019.

Design: Retrospective observational study.

Analysis of Discrepancies Between Pulse Oximetry and Arterial Oxygen Saturation Measurements by Race and Ethnicity and Association With Organ Dysfunction and Mortality.

Importance: Discrepancies in oxygen saturation measured by pulse oximetry (Spo2), when compared with arterial oxygen saturation (Sao2) measured by arterial blood gas (ABG), may differentially affect patients according to race and ethnicity. However, the association of these disparities with health outcomes is unknown.

Objective: To examine racial and ethnic discrepancies between Sao2 and Spo2 measures and their associations with clinical outcomes.

A Locally Optimized Data-Driven Tool to Predict Sepsis-Associated Vasopressor Use in the ICU.

Objectives: To train a model to predict vasopressor use in ICU patients with sepsis and optimize external performance across hospital systems using domain adaptation, a transfer learning approach.

Design: Observational cohort study.

Setting: Two academic medical centers from January 2014 to June 2017.

Prediction of Acute Respiratory Failure Requiring Advanced Respiratory Support in Advance of Interventions and Treatment: A Multivariable Prediction Model From Electronic Medical Record Data.

Background: Acute respiratory failure occurs frequently in hospitalized patients and often begins outside the ICU, associated with increased length of stay, cost, and mortality. Delays in decompensation recognition are associated with worse outcomes.

Objectives: The objective of this study is to predict acute respiratory failure requiring any advanced respiratory support (including noninvasive ventilation).

Machine Learning Methods to Predict Acute Respiratory Failure and Acute Respiratory Distress Syndrome.

Acute respiratory failure (ARF) is a common problem in medicine that utilizes significant healthcare resources and is associated with high morbidity and mortality. Classification of acute respiratory failure is complicated, and it is often determined by the level of mechanical support that is required, or the discrepancy between oxygen supply and uptake. These phenotypes make acute respiratory failure a continuum of syndromes, rather than one homogenous disease process.

Predicting Progression to Septic Shock in the Emergency Department Using an Externally Generalizable Machine-Learning Algorithm.

Study Objective: Machine-learning algorithms allow improved prediction of sepsis syndromes in the emergency department (ED), using data from electronic medical records. Transfer learning, a new subfield of machine learning, allows generalizability of an algorithm across clinical sites. We aim to validate the Artificial Intelligence Sepsis Expert for the prediction of delayed septic shock in a cohort of patients treated in the ED and demonstrate the feasibility of transfer learning to improve external validity at a second site.

Major publications in the critical care pharmacotherapy literature: 2019.

Purpose: To summarize selected meta-analyses and trials related to critical care pharmacotherapy published in 2019.

Materials And Methods: The Critical Care Pharmacotherapy Literature Update (CCPLU) Group screened 36 journals monthly for impactful articles and reviewed 113 articles during 2019 according to Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) criteria.

Results: Articles with a 1A grade, including three clinical practice guidelines, six meta-analyses, and five original research trials are reviewed here from those included in the monthly CCPLU.

Predicting progression to septic shock in the emergency department using an externally generalizable machine learning algorithm.

Objective: Machine-learning (ML) algorithms allow for improved prediction of sepsis syndromes in the ED using data from electronic medical records. Transfer learning, a new subfield of ML, allows for generalizability of an algorithm across clinical sites. We aimed to validate the Artificial Intelligence Sepsis Expert (AISE) for the prediction of delayed septic shock in a cohort of patients treated in the ED and demonstrate the feasibility of transfer learning to improve external validity at a second site.

Fluid Response Evaluation in Sepsis Hypotension and Shock: A Randomized Clinical Trial.

Background: Fluid and vasopressor management in septic shock remains controversial. In this randomized controlled trial, we evaluated the efficacy of dynamic measures (stroke volume change during passive leg raise) to guide resuscitation and improve patient outcome.

Research Question: Will resuscitation that is guided by dynamic assessments of fluid responsiveness in patients with septic shock improve patient outcomes?

Study Design And Methods: We conducted a prospective, multicenter, randomized clinical trial at 13 hospitals in the United States and United Kingdom.

Increasing Cardiovascular Data Sampling Frequency and Referencing It to Baseline Improve Hemorrhage Detection.

Unlabelled: We hypothesize that knowledge of a stable personalized baseline state and increased data sampling frequency would markedly improve the ability to detect progressive hypovolemia during hemorrhage earlier and with a lower false positive rate than when using less granular data.

Design: Prospective temporal challenge.

Setting: Large animal research laboratory, University Medical Center.

A FHIR-Enabled Streaming Sepsis Prediction System for ICUs.

Sepsis is a common disease with very costly, potentially deadly implications. Early prediction of Sepsis and initiation of antibiotic is widely considered as an important determinant of patient survival. Cross-institutional validation and implementation of algorithms for early prediction of Sepsis at a minimum require common data formats, streaming analytic platforms for timely risk assessment, and interoperable and standardized interfaces.