CHEST critical care最新文献

As the health effects of climate change intensify, critical care providers have an urgent responsibility to minimize the environmental impact of health care delivery. Although the response of critical care clinicians in managing climate-exacerbated diseases such as asthma and heat stroke is well recognized, the impact of critical care delivery on climate change itself may be less familiar. This case-based review explores the drivers of the ICU climate footprint, including high energy and electricity use, supply chain contributions, pharmaceutical greenhouse gas emissions, plastic waste, and low-value care. Potential solutions then are presented for each of these elements, with an emphasis on multidisciplinary team engagement to enact lasting change. The role of the ICU clinician as environmental policy advocate also is explored. Despite the grave clinical implications of the climate crisis, critical care providers are well positioned to mitigate their own climate impacts and to help lead health care decarbonization.

Increasing numbers of patients survive critical illness. Survivors of critical illness are at risk of post-intensive care syndrome (PICS). Post-ICU clinics are one way to help patients with PICS and to assist patients in their recovery progress. We report herein how we have structured our ICU recovery clinic and highlight important elements to consider when evaluating patients in a post-ICU clinic, including a new mnemonic, IMPORTANCE: ICU debriefing, medications and immunizations, PICS evaluation, organ failure assessment, referrals, testing, addressing future goals of care, needs assessment, caregiver support, and education about expectations. We present a case study from our ICU recovery clinic. Finally, we discuss future directions of post-ICU clinics.

Background

Postoperative respiratory failure (PRF) is associated with increased hospital charges and worse patient outcomes. Reliable prediction models can help to guide postoperative planning to optimize care, to guide resource allocation, and to foster shared decision-making with patients.

Research Question

Can a predictive model be developed to accurately identify patients at high risk of PRF?

Study Design and Methods

In this single-site proof-of-concept study, we used structured query language to extract, transform, and load electronic health record data from 23,999 consecutive adult patients admitted for elective surgery (2014-2021). Our primary outcome was PRF, defined as mechanical ventilation after surgery of > 48 h. Predictors of interest included demographics, comorbidities, and intraoperative factors. We used logistic regression to build a predictive model and the least absolute shrinkage and selection operator procedure to select variables and to estimate model coefficients. We evaluated model performance using optimism-corrected area under the receiver operating curve and area under the precision-recall curve and calculated sensitivity, specificity, positive and negative predictive values, and Brier scores.

Results

Two hundred twenty-five patients (0.94%) demonstrated PRF. The 18-variable predictive model included: operations on the cardiovascular, nervous, digestive, urinary, or musculoskeletal system; surgical specialty orthopedic (nonspine); Medicare or Medicaid (as the primary payer); race unknown; American Society of Anesthesiologists class ≥ III; BMI of 30 to 34.9 kg/m²; anesthesia duration (per hour); net fluid at end of the operation (per liter); median intraoperative Fio₂, end title CO₂, heart rate, and tidal volume; and intraoperative vasopressor medications. The optimism-corrected area under the receiver operating curve was 0.835 (95% CI, 0.808-0.862) and the area under the precision-recall curve was 0.156 (95% CI, 0.105-0.203).

Interpretation

This single-center proof-of-concept study demonstrated that a structured query language extract, transform, and load process, based on readily available patient and intraoperative variables, can be used to develop a prediction model for PRF. This PRF prediction model is scalable for multicenter research. Clinical applications include decision support to guide postoperative level of care admission and treatment decisions.

Background

The introduction of COVID-19 vaccines exposed volatility and hesitancy around vaccines. Some health care models, including ICU recovery clinics (ICU-RCs), are structured to provide vaccine counseling. However, information regarding provider and patient vaccine conversations is limited in this postacute setting.

Research Question

What factors influence the decision-making process of patients who have survived an ICU stay surrounding influenza and COVID-19 vaccination?

Study Design and Methods

To understand further vaccine perceptions after critical illness, a secondary qualitative thematic analysis was performed using transcripts from a randomized controlled trial designed to develop and refine a telemedicine approach to ICU recovery. Thirty-three ICU-RC visits with 19 adult patients and 13 caregivers were conducted within 12 weeks of hospital discharge. The analysis was guided by the theory of planned behavior (TPB).

Results

Five themes were elicited from the data. The first four themes arose from the TPB: (1) behavioral and attitudinal beliefs (not being susceptible to the flu, concerns about the COVID-19 vaccine causing fertility issues, and not being tested enough), (2) normative beliefs (everyone they know is getting the influenza vaccine so they are, too), (3) control vaccine beliefs (patients are more likely to get the COVID-19 vaccine if it is easy to obtain), and (4) intention to vaccinate. Another theme not related to the TPB arose and could contribute to vaccine intent and behavior: (5) health team engagement with patients and caregivers (allowing for ICU clinicians to correct vaccine misinformation in real time).

Interpretation

Using the information learned in our study, the period after critical illness or other acute illness events may be an especially fruitful target for designing an action plan for improving public trust in vaccines and improving overall completion rates; however, further research is needed.

Trial Registry

ClinicalTrials.gov; No.: NCT03926533; URL: www.clinicaltrials.gov

Background

Sepsis is a syndrome characterized by host immune dysfunction, with the extent of immunoparalysis differing among patients. Lipopolysaccharide (LPS) is used commonly to assess the immune function of critically ill patients with sepsis. However, the reliability of this ex vivo diagnostic test in predicting clinical outcomes remains uncertain.

Research Question

Does LPS-induced tumor necrosis factor (TNF) production from the blood of patients with sepsis predict mortality? Secondary outcomes included ICU and hospital stay durations, nosocomial infection rate, and organ recovery rate.

Study Design and Methods

Human sepsis studies from various databases through April 2023 were evaluated. Inclusion criteria encompassed LPS-stimulated blood assays, English language, and reported clinical outcomes. Bias risk was evaluated using the Newcastle-Ottawa scale (NOS). Relationships between TNF production and mortality were analyzed at sepsis onset and during established sepsis, alongside secondary outcomes.

Results

Of 11,580 studies, 17 studies (14 adult and three pediatric) were selected for analysis. Although 15 studies were evaluated as moderate to high quality using the NOS, it is important to note that some of these studies also had identifiable biases, such as unclear methods of participant recruitment. Nine studies detailed survival outcomes associated with LPS-induced TNF production at sepsis onset, whereas five studies explored TNF production’s relationship with mortality during established sepsis. Trends suggested that lower LPS-induced TNF production correlated with higher mortality. However, heterogeneity in methodologies, especially the LPS assay protocol, hindered definitive conclusions. Publication bias was highlighted using funnel plot analysis. Concerning secondary outcomes, diminished TNF production might signify worsening organ dysfunction, although the link between cytokine production and nosocomial infection varied among studies.

Interpretation

For functional immune profiling in sepsis, streamlined research methodologies are essential. This entails organizing cohorts based on microbial sources of sepsis, establishing standardized definitions of immunoparalysis, using consistent types and dosages of immune stimulants, adhering to uniform blood incubation conditions, and adopting consistent clinical outcomes.