Racial differences in laboratory testing as a potential mechanism for bias in AI: A matched cohort analysis in emergency department visits.

AI models are often trained using available laboratory test results. Racial differences in laboratory testing may bias AI models for clinical decision support, amplifying existing inequities. This study aims to measure the extent of racial differences in laboratory testing in adult emergency department (ED) visits.

Mortality of Patients With Sepsis Administered Piperacillin-Tazobactam vs Cefepime.

Importance: Experimental and observational studies have suggested that empirical treatment for bacterial sepsis with antianaerobic antibiotics (eg, piperacillin-tazobactam) is associated with adverse outcomes compared with anaerobe-sparing antibiotics (eg, cefepime). However, a recent pragmatic clinical trial of piperacillin-tazobactam and cefepime showed no difference in short-term outcomes at 14 days. Further studies are needed to help clarify the empirical use of these agents.

Acute Respiratory Distress Syndrome: Definition, Diagnosis, and Routine Management.

Acute respiratory distress syndrome (ARDS) is an acute inflammatory lung injury characterized by severe hypoxemic respiratory failure, bilateral opacities on chest imaging, and low lung compliance. ARDS is a heterogeneous syndrome that is the common end point of a wide variety of predisposing conditions, with complex pathophysiology and underlying mechanisms. Routine management of ARDS is centered on lung-protective ventilation strategies such as low tidal volume ventilation and targeting low airway pressures to avoid exacerbation of lung injury, as well as a conservative fluid management strategy.

Shift-Level Team Familiarity Is Associated with Improved Outcomes in Mechanically Ventilated Adults.

Organizing ICU interprofessional teams is a high priority because of workforce needs, but the role of interprofessional familiarity remains unexplored. Determine if mechanically ventilated patients cared for by teams with greater familiarity have improved outcomes, such as lower mortality, shorter duration of mechanical ventilation (MV), and greater spontaneous breathing trial (SBT) implementation. We used electronic health records data of five ICUs in an academic medical center to map interprofessional teams and their ICU networks, measuring team familiarity as network coreness and mean team value.

Rural-Urban Differences in Mortality among Mechanically Ventilated Patients in Intensive and Intermediate Care.

Intermediate care (also termed "step-down" or "moderate care") has been proposed as a lower cost alternative to care for patients who may not clearly benefit from intensive care unit admission. Intermediate care units may be appealing to hospitals in financial crisis, including those in rural areas. Outcomes of patients receiving intermediate care are not widely described.

The Lung Microbiome Predicts Mortality and Response to Azithromycin in Lung Transplant Recipients with Chronic Rejection.

Chronic lung allograft dysfunction (CLAD) is the leading cause of death after lung transplant, and azithromycin has variable efficacy in CLAD. The lung microbiome is a risk factor for developing CLAD, but the relationship between lung dysbiosis, pulmonary inflammation, and allograft dysfunction remains poorly understood. Whether lung microbiota predict outcomes or modify treatment response CLAD is unknown.

Early Deep Sedation Practices Worsened During the Pandemic Among Adult Patients Without COVID-19: A Retrospective Cohort Study.

Background: There is substantial evidence that patients with COVID-19 were treated with sustained deep sedation during the pandemic. However, it is unknown whether such guideline-discordant care had spillover effects to patients without COVID-19.

Research Question: Did patterns of early deep sedation change during the pandemic for patients on mechanical ventilation without COVID-19?

Study Design And Methods: We used electronic health record data from 4,237 patients who were intubated without COVID-19.

Learning Concept Credible Models for Mitigating Shortcuts.

During training, models can exploit spurious correlations as shortcuts, resulting in poor generalization performance when shortcuts do not persist. In this work, assuming access to a representation based on domain knowledge () that is invariant to shortcuts, we aim to learn robust and accurate models from biased training data. In contrast to previous work, we do not rely solely on known concepts, but allow the model to also learn unknown concepts.

Higher versus lower oxygenation targets in adult ICU patients: A rapid practice guideline.

The aim of this Intensive Care Medicine Rapid Practice Guideline (ICM-RPG) was to provide evidence-based clinical guidance about the use of higher versus lower oxygenation targets for adult patients in the intensive care unit (ICU). The guideline panel comprised 27 international panelists, including content experts, ICU clinicians, methodologists, and patient representatives. We adhered to the methodology for trustworthy clinical practice guidelines, including the use of the Grading of Recommendations Assessment, Development, and Evaluation approach to assess the certainty of evidence, and used the Evidence-to-Decision framework to generate recommendations.

Measuring the Impact of AI in the Diagnosis of Hospitalized Patients: A Randomized Clinical Vignette Survey Study.

Importance: Artificial intelligence (AI) could support clinicians when diagnosing hospitalized patients; however, systematic bias in AI models could worsen clinician diagnostic accuracy. Recent regulatory guidance has called for AI models to include explanations to mitigate errors made by models, but the effectiveness of this strategy has not been established.

Objectives: To evaluate the impact of systematically biased AI on clinician diagnostic accuracy and to determine if image-based AI model explanations can mitigate model errors.

Prediction of episode of hemodynamic instability using an electrocardiogram based analytic: a retrospective cohort study.

Background: Predicting the onset of hemodynamic instability before it occurs remains a sought-after goal in acute and critical care medicine. Technologies that allow for this may assist clinicians in preventing episodes of hemodynamic instability (EHI). We tested a novel noninvasive technology, the Analytic for Hemodynamic Instability-Predictive Indicator (AHI-PI), which analyzes a single lead of electrocardiogram (ECG) and extracts heart rate variability and morphologic waveform features to predict an EHI prior to its occurrence.

Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning.

As machine learning (ML) models gain traction in clinical applications, understanding the impact of clinician and societal biases on ML models is increasingly important. While biases can arise in the labels used for model training, the many sources from which these biases arise are not yet well-studied. In this paper, we highlight (, differences in testing rates across patient groups) as a source of label bias that clinical ML models may amplify, potentially causing harm.

Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality.

As portable chest X-rays are an efficient means of triaging emergent cases, their use has raised the question as to whether imaging carries additional prognostic utility for survival among patients with COVID-19. This study assessed the importance of known risk factors on in-hospital mortality and investigated the predictive utility of radiomic texture features using various machine learning approaches. We detected incremental improvements in survival prognostication utilizing texture features derived from emergent chest X-rays, particularly among older patients or those with a higher comorbidity burden.

External Validation and Comparison of a General Ward Deterioration Index Between Diversely Different Health Systems.

Objectives: Implementing a predictive analytic model in a new clinical environment is fraught with challenges. Dataset shifts such as differences in clinical practice, new data acquisition devices, or changes in the electronic health record (EHR) implementation mean that the input data seen by a model can differ significantly from the data it was trained on. Validating models at multiple institutions is therefore critical.

Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19 During the Circulation of the SARS-CoV-2 Delta Variant and the Transition to the SARS-CoV-2 Omicron Variant.

Importance: Breath analysis has been explored as a noninvasive means to detect COVID-19. However, the impact of emerging variants of SARS-CoV-2, such as Omicron, on the exhaled breath profile and diagnostic accuracy of breath analysis is unknown.

Objective: To evaluate the diagnostic accuracies of breath analysis on detecting patients with COVID-19 when the SARS-CoV-2 Delta and Omicron variants were most prevalent.

Prioritizing evidence-based practices for acute respiratory distress syndrome using digital data: an iterative multi-stakeholder process.

Background: Evidence-based practices (EBPs) for patients receiving invasive mechanical ventilation vary in the quality of their underlying evidence and ease of implementation.

Research Question: How do researchers and clinicians prioritize EBPs to help guide clinical decision-making and focus implementation efforts to improve patient care using existing, validated measures?

Study Design And Methods: We developed a 4-step rapid method using existing criteria to prioritize EBPs associated with lower mortality and/or shorter duration of invasive mechanical ventilation for patients suffering from acute respiratory failure or acute respiratory distress syndrome. Using different types of data including surveys, we (1) identified relevant EBPs, (2) rated EBPs using the Guideline Implementability Appraisal (GLIA) tool, (3) surveyed practicing ICU clinicians from different hospital systems using a subset of GLIA criteria, and (4) developed metrics to assess EBP performance.