Critical Bias in Critical Care Devices.

Critical care data contain information about the most physiologically fragile patients in the hospital, who require a significant level of monitoring. However, medical devices used for patient monitoring suffer from measurement biases that have been largely underreported. This article explores sources of bias in commonly used clinical devices, including pulse oximeters, thermometers, and sphygmomanometers. Further, it provides a framework for mitigating these biases and key principles to achieve more equitable health care delivery.

Sepsis-Induced Coagulopathy and Disseminated Intravascular Coagulation: What We Need to Know and How to Manage for Prolonged Casualty Care.

Coagulopathy can occur in trauma, and it can affect septic patients as a host tries to respond to infection. Sometimes, it can lead to disseminated intravascular coagulopathy (DIC) with a high potential for mortality. New research has delineated risk factors that include neutrophil extracellular traps and endothelial glycocalyx shedding. Managing DIC in septic patients focuses on first treating the underlying cause of sepsis. Further, the International Society on Thrombolysis and Haemostasis (ISTH) has DIC diagnostic criteria. "Sepsis-induced coagulopathy" (SIC) is a new category. Therapy of SIC focuses on treating the underlying infection and the ensuing coagulopathy. Most therapeutic approaches to SIC have focused on anticoagulant therapy. This review will discuss SIC and DIC and how they are relevant to prolonged casualty care (PCC).

A Method to Explore Variations of Ventilator-Associated Event Surveillance Definitions in Large Critical Care Databases in the United States.

The Centers for Disease Control has well-established surveillance programs to monitor preventable conditions in patients supported by mechanical ventilation (MV). The aim of the study was to develop a data-driven methodology to examine variations in the first tier of the ventilator-associated event surveillance definition, described as a ventilator-associated condition (VAC). Further, an interactive tool was designed to illustrate the effect of changes to the VAC surveillance definition, by applying different ventilator settings, time-intervals, demographics, and selected clinical criteria. DESIGN: Retrospective, multicenter, cross-sectional analysis. SETTING: Three hundred forty critical care units across 209 hospitals, comprising 261,910 patients in both the electronic Intensive Care Unit Clinical Research Database and Medical Information Mart for Intensive Care III databases. PATIENTS: A total of 14,517 patients undergoing MV for 4 or more days. MEASUREMENTS AND MAIN RESULTS: We designed a statistical analysis framework, complemented by a custom interactive data visualization tool to depict how changes to the VAC surveillance definition alter its prognostic performance, comparing patients with and without VAC. This methodology and tool enable comparison of three clinical outcomes (hospital mortality, hospital length-of-stay, and ICU length-of-stay) and provide the option to stratify patients by six criteria in two categories: patient population (dataset and ICU type) and clinical features (minimum Fio2, minimum positive end-expiratory pressure, early/late VAC, and worst first-day respiratory Sequential Organ Failure Assessment score). Patient population outcomes were depicted by heatmaps with mortality odds ratios. In parallel, outcomes from ventilation setting variations and clinical features were depicted with Kaplan-Meier survival curves. CONCLUSIONS: We developed a method to examine VAC using information extracted from large electronic health record databases. Building upon this framework, we developed an interactive tool to visualize and quantify the implications of variations in the VAC surveillance definition in different populations, across time and critical care settings. Data for patients with and without VAC was used to illustrate the effect of the application of this method and visualization tool.

Benchmarking emergency department prediction models with machine learning and public electronic health records.

The demand for emergency department (ED) services is increasing across the globe, particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have become increasingly challenging due to the shortage of medical resources and the strain on hospital infrastructure caused by the pandemic. As a result of the widespread use of electronic health records (EHRs), we now have access to a vast amount of clinical data, which allows us to develop prediction models and decision support systems to address these challenges. To date, there is no widely accepted clinical prediction benchmark related to the ED based on large-scale public EHRs. An open-source benchmark data platform would streamline research workflows by eliminating cumbersome data preprocessing, and facilitate comparisons among different studies and methodologies. Based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we created a benchmark dataset and proposed three clinical prediction benchmarks. This study provides future researchers with insights, suggestions, and protocols for managing data and developing predictive tools for emergency care.

Efficacy of Transcendental Meditation to Reduce Stress Among Health Care Workers: A Randomized Clinical Trial.

Importance: Health care workers (HCWs) have been experiencing substantial stress and burnout, and evidence-based mitigation strategies are needed. Transcendental Meditation (TM) is a mantra meditation practice with potential efficacy in reducing stress. Objective: To assess the efficacy of TM practice in reducing stress among HCWs over a 3-month period. Design, Setting, and Participants: This single-center open-label randomized clinical trial was conducted among HCWs at an academic medical center from November 19, 2020, to August 31, 2021. Inclusion criteria comprised a score of 6 points or greater on the Subjective Units of Distress Scale and an increase of 5% or greater in baseline heart rate or an increase of 33% or greater in galvanic skin response after exposure to a stressful script. Exclusion criteria included the use of antipsychotic or ß blocker medications, current suicidal ideation, or previous TM training. Of 213 HCWs who participated in prescreening, 95 attended in-person visits, resulting in 80 eligible participants who were randomized to receive a TM intervention (TM group) or usual treatment (control group). Interventions: The TM group practiced TM for 20 minutes twice daily over a 3-month period. The control group received usual treatment, which consisted of access to wellness resources. Main Outcomes and Measures: The primary outcome was change in acute psychological distress measured by the Global Severity Index. Secondary outcomes included changes in burnout (measured by the Maslach Burnout Inventory), insomnia (measured by the Insomnia Severity Index), and anxiety (measured by the Generalized Anxiety Disorder-7 scale). Results: Among 80 participants, 66 (82.5%) were women, with a mean (SD) age of 40 (11) years. One participant (1.3%) was American Indian or Alaska Native, 5 (6.3%) were Asian, 12 (15.0%) were Black, 59 (73.8%) were White, and 3 (3.8%) were of unknown or unreported race; 4 participants (5.0%) were Hispanic, and 76 (95.0%) were non-Hispanic. A total of 41 participants were randomized to the TM group, and 39 were randomized to the control group. Participants in the TM group did not show a statistically significant decrease in psychological distress on the Global Severity Index compared with those in the control group (-5.6 points vs -3.8 points; between-group difference, -1.8 points; 95% CI, -4.2 to 0.6 points; P = .13). Compared with the control group, the TM group had significantly greater reductions in the secondary end points of emotional exhaustion (Maslach Burnout Inventory subscore: -8.0 points vs -2.6 points; between-group difference, -5.4 points; 95% CI, -9.2 to -1.6 points; P = .006), insomnia (Insomnia Severity Scale score: -4.1 points vs -1.9 points; between-group difference, -2.2 points; 95% CI, -4.4 to 0 points; P = .05), and anxiety (Generalized Anxiety Disorder-7 score: -3.1 points vs -0.9 points; between-group difference, -2.2 points; 95% CI, -3.8 to -0.5; P = .01) at 3 months. A total of 38 participants (92.7%) in the TM group adhered to home practice. Conclusions and Relevance: In this randomized clinical trial, TM practice among HCWs over a 3-month period did not result in a statistically significant reduction in the primary outcome of acute psychological distress compared with usual treatment but significantly improved the secondary outcomes of burnout, anxiety, and insomnia. These findings suggest that TM may be a safe and effective strategy to alleviate chronic stress among HCWs. Trial Registration: ClinicalTrials.gov identifier: NCT04632368.

The Future of Critical Care: Optimizing Technologies and a Learning Healthcare System to Potentiate a More Humanistic Approach to Critical Care.

While technological innovations are the invariable crux of speculation about the future of critical care, they cannot replace the clinician at the bedside. This article summarizes the work of the Society of Critical Care Medicine-appointed multiprofessional task for the Future of Critical Care. The Task Force notes that critical care practice will be transformed by novel technologies, integration of artificial intelligence decision support algorithms, and advances in seamless data operationalization across diverse healthcare systems and geographic regions and within federated datasets. Yet, new technologies will be relevant and meaningful only if they improve the very human endeavor of caring for someone who is critically ill.

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. Design, Setting, and Participants: This multicenter, retrospective, cross-sectional study included 3 publicly available electronic health record (EHR) databases (ie, the Electronic Intensive Care Unit-Clinical Research Database and Medical Information Mart for Intensive Care III and IV) as well as Emory Healthcare (2014-2021) and Grady Memorial (2014-2020) databases, spanning 215 hospitals and 382 ICUs. From 141 600 hospital encounters with recorded ABG measurements, 87 971 participants with first ABG measurements and an Spo2 of at least 88% within 5 minutes before the ABG test were included. Exposures: Patients with hidden hypoxemia (ie, Spo2 ≥88% but Sao2 <88%). Main Outcomes and Measures: Outcomes, stratified by race and ethnicity, were Sao2 for each Spo2, hidden hypoxemia prevalence, initial demographic characteristics (age, sex), clinical outcomes (in-hospital mortality, length of stay), organ dysfunction by scores (Sequential Organ Failure Assessment [SOFA]), and laboratory values (lactate and creatinine levels) before and 24 hours after the ABG measurement. Results: The first Spo2-Sao2 pairs from 87 971 patient encounters (27 713 [42.9%] women; mean [SE] age, 62.2 [17.0] years; 1919 [2.3%] Asian patients; 26 032 [29.6%] Black patients; 2397 [2.7%] Hispanic patients, and 57 632 [65.5%] White patients) were analyzed, with 4859 (5.5%) having hidden hypoxemia. Hidden hypoxemia was observed in all subgroups with varying incidence (Black: 1785 [6.8%]; Hispanic: 160 [6.0%]; Asian: 92 [4.8%]; White: 2822 [4.9%]) and was associated with greater organ dysfunction 24 hours after the ABG measurement, as evidenced by higher mean (SE) SOFA scores (7.2 [0.1] vs 6.29 [0.02]) and higher in-hospital mortality (eg, among Black patients: 369 [21.1%] vs 3557 [15.0%]; P < .001). Furthermore, patients with hidden hypoxemia had higher mean (SE) lactate levels before (3.15 [0.09] mg/dL vs 2.66 [0.02] mg/dL) and 24 hours after (2.83 [0.14] mg/dL vs 2.27 [0.02] mg/dL) the ABG test, with less lactate clearance (-0.54 [0.12] mg/dL vs -0.79 [0.03] mg/dL). Conclusions and Relevance: In this study, there was greater variability in oxygen saturation levels for a given Spo2 level in patients who self-identified as Black, followed by Hispanic, Asian, and White. Patients with and without hidden hypoxemia were demographically and clinically similar at baseline ABG measurement by SOFA scores, but those with hidden hypoxemia subsequently experienced higher organ dysfunction scores and higher in-hospital mortality.

One-Stop Serum Assay Identifies COVID-19 Disease Severity and Vaccination Responses.

SARS-CoV-2 has caused over 100,000,000 cases and almost 2,500,000 deaths globally. Comprehensive assessment of the multifaceted antiviral Ab response is critical for diagnosis, differentiation of severity, and characterization of long-term immunity, especially as COVID-19 vaccines become available. Severe disease is associated with early, massive plasmablast responses. We developed a multiplex immunoassay from serum/plasma of acutely infected and convalescent COVID-19 patients and prepandemic and postpandemic healthy adults. We measured IgA, IgG, and/or IgM against SARS-CoV-2 nucleocapsid (N), spike domain 1 (S1), S1-receptor binding domain (RBD) and S1-N-terminal domain. For diagnosis, the combined [IgA + IgG + IgM] or IgG levels measured for N, S1, and S1-RBD yielded area under the curve values ≥0.90. Virus-specific Ig levels were higher in patients with severe/critical compared with mild/moderate infections. A strong prozone effect was observed in sera from severe/critical patients-a possible source of underestimated Ab concentrations in previous studies. Mild/moderate patients displayed a slower rise and lower peak in anti-N and anti-S1 IgG levels compared with severe/critical patients, but anti-RBD IgG and neutralization responses reached similar levels at 2-4 mo after symptom onset. Measurement of the Ab responses in sera from 18 COVID-19-vaccinated patients revealed specific responses for the S1-RBD Ag and none against the N protein. This highly sensitive, SARS-CoV-2-specific, multiplex immunoassay measures the magnitude, complexity, and kinetics of the Ab response and can distinguish serum Ab responses from natural SARS-CoV-2 infections (mild or severe) and mRNA COVID-19 vaccines.

Randomized Controlled Trial of a Novel Communication Device Assessed During Noninvasive Ventilation Therapy.

BACKGROUND: Noninvasive ventilation (NIV), a form of positive airway pressure (PAP) therapy, is the standard of care for various forms of acute respiratory failure (ARF). Communication impairment is a side effect of NIV, impedes patient care, contributes to distress and intolerance, and potentially increases intubation rates. This study aimed to evaluate communication impairment during CPAP therapy and demonstrate communication device improvement with a standardized protocol. RESEARCH QUESTION: How does an oronasal mask affect communication intelligibility? How does use of an NIV communication device change this communication intelligibility? STUDY DESIGN AND METHODS: A single-center randomized controlled trial (36 outpatients with OSA on CPAP therapy) assessed exposure to CPAP 10 cm H2O and PAP communication devices (SPEAX, Ataia Medical). Communication impairment was evaluated by reading selected words and sentences for partners to record and were tabulated as %words correct. Each outpatient-partner pair performed three assessments: (1) baseline (conversing normally), (2) mask baseline (conversing with PAP), and (3) randomized to functioning device (conversing with PAP and device) or sham device. After each stage, both outpatients and partners completed Likert surveys regarding perceived intelligibility and comfort. RESULTS: While conversing with PAP, word and sentence intelligibility decreased relatively by 52% (87% vs 41%) and relatively by 57% (94% vs 40%), respectively, compared with normal conversation. Word and sentence intelligibility in the intervention arm increased relatively by 75% (35% vs 61%; P < .001) and by 126% (33% vs 76%; P < .001) higher than the control arm, respectively. The device improved outpatient-perceived PAP comfort relatively by 233% (15% vs 50%, P = .042) and partner-perceived comfort by relatively 245% (20% vs 69%, P = .0074). INTERPRETATION: Use of this PAP communication device significantly improves both intelligibility and comfort. This is one of the first studies quantifying communication impairment during PAP delivery. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT03795753; URL: www.clinicaltrials.gov.

Consequences and Solutions for the Impact of Communication Impairment on Noninvasive Ventilation Therapy for Acute Respiratory Failure: A Focused Review.

OBJECTIVES: With over 2 million cases of acute respiratory failure in the United States per year, noninvasive ventilation has become a leading treatment modality, often supplanting invasive mechanical ventilation as the initial treatment of choice. Most acute respiratory failure patients use a full face (oronasal) mask with noninvasive ventilation, which is known to impair communication, but its popularity and benefit has led many providers to accept the communication impairment. Medical staff periodically remove masks to communicate with patients, but patients are often limited to short utterances and risk lung derecruitment upon removal of positive pressure. These problems can lead to noninvasive ventilation failure, which is often linked to worse outcomes than first initiating invasive mechanical ventilation and can lead to increased hospitalization costs. DATA SOURCES: We searched MEDLINE and Google Scholar for "speech," "communication," "impairment," "failure," "complications," "NIPPV," "NIV," and "noninvasive ventilation." STUDY SELECTION: We included articles with patients in acute respiratory failure. We excluded articles for patients using noninvasive ventilation therapy for obstructive sleep apnea. DATA SYNTHESIS: Communication impairment has been associated with increasing noninvasive ventilation anxiety (odds ratio, 1.25). Of patients using noninvasive ventilation, 48% require early discontinuation, 22% refuse noninvasive ventilation, and 9% are ultimately intubated. Improvements to communication have been shown to reduce fear and anxiety in invasive mechanical ventilation patients. Analogous communication problems exist with effective solutions in other fields, such as fighter pilot masks, that can be easily implemented to enhance noninvasive ventilation patient care, increase adherence to noninvasive ventilation treatment, and improve patient outcomes. CONCLUSIONS: Communication impairment is an underappreciated cause of noninvasive ventilation complications and failure and requires further characterization. Analogous solutions-such as throat microphones and mask-based microphones-that can be easily implemented show potential as cost-effective methods to reduce noninvasive ventilation failure.

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. Early recognition of the risk factors for new or worsening acute respiratory failure may prevent that process from occurring. Predictive analytical methods using machine learning leverage clinical data to provide an early warning for impending acute respiratory failure or its sequelae. The aims of this review are to summarize the current literature on ARF prediction, to describe accepted procedures and common machine learning tools for predictive tasks through the lens of ARF prediction, and to demonstrate the challenges and potential solutions for ARF prediction that can improve patient outcomes.

Computerized detection of adverse drug reactions in the medical intensive care unit.

OBJECTIVE: Clinical event monitors are a type of active medication monitoring system that can use signals to alert clinicians to possible adverse drug reactions. The primary goal was to evaluate the positive predictive values of select signals used to automate the detection of ADRs in the medical intensive care unit. METHOD: This is a prospective, case series of adult patients in the medical intensive care unit during a six-week period who had one of five signals presents: an elevated blood urea nitrogen, vancomycin, or quinidine concentration, or a low sodium or glucose concentration. Alerts were assessed using 3 objective published adverse drug reaction determination instruments. An event was considered an adverse drug reaction when 2 out of 3 instruments had agreement of possible, probable or definite. Positive predictive values were calculated as the proportion of alerts that occurred, divided by the number of times that alerts occurred and adverse drug reactions were confirmed. RESULTS: 145 patients were eligible for evaluation. For the 48 patients (50% male) having an alert, the mean±SD age was 62±19 years. A total of 253 alerts were generated. Positive predictive values were 1.0, 0.55, 0.38 and 0.33 for vancomycin, glucose, sodium, and blood urea nitrogen, respectively. A quinidine alert was not generated during the evaluation. CONCLUSIONS: Computerized clinical event monitoring systems should be considered when developing methods to detect adverse drug reactions as part of intensive care unit patient safety surveillance systems, since they can automate the detection of these events using signals that have good performance characteristics by processing commonly available laboratory and medication information.

Bayesian combinatorial partitioning for detecting interactions among genetic variants.

Detecting epistatic (nolinear) interactions among single nucleotide polymorphisms (SNPs) at multiple loci is important in the analysis of genomic data in association studies. We developed a Bayesian combinatorial partitioning (BCP) for detecting such interactions among SNPs that are predictive of disease. When compared with multifactor dimensionality reduction (MDR), a widely used combinatorial partitioning method for detecting interactions, BCP has significantly greater power and is computationally more efficient.

A Bayesian method for identifying genetic interactions.

An important challenge in the analysis of single nucleotide polymorphism (SNP) data is the identification of SNPs that interact in a nonlinear fashion in their association with disease. Such epistatic interactions among genetic variants at multiple loci likely underlie the inheritance of common diseases. We have developed a novel method called the Bayesian combinatorial method (BCM) for detecting combination of genetic variants that are predictive of disease. When compared with the multifactor dimensionality reduction (MDR), a widely used combinatorial method, BCM has significantly greater power to detect interactions and is computationally more efficient.