Critical Care Medicine最新文献

Objectives: Telecritical care (TCC) refers to the delivery of critical care using telehealth technologies. Despite increasing utilization, significant practice variation exists and literature regarding efficacy remains sparse. The Telecritical Care Collaborative Network sought to provide expert, consensus-based best practice recommendations for the design and delivery of TCC.

Design: We used a modified Delphi methodology. Following literature review, an oversight panel identified core domains and developed declarative statements for review by an expert voting panel. During three voting rounds, voters agreed or disagreed with statements and provided open-ended feedback, which the oversight panel used to revise statements. Statements met criteria for consensus when accepted by greater than or equal to 85% of voters.

Setting/subjects: The oversight panel included 18 multidisciplinary members of the TCC Collaborative Network, and the voting panel included 32 invited experts in TCC, emphasizing diversity of discipline, care delivery models, and geography.

Interventions: None.

Measurements and main results: We identified ten core domains: definitions/terminology; care delivery models; staffing and coverage models; technological considerations; ergonomics and workplace safety; licensing, credentialing, and certification; trust and relationship building; quality, safety, and efficiency, research agenda; and advocacy, leading to 79 practice statements. Of 79 original statements, 67 were accepted in round 1. After revision, nine were accepted in round 2 and two in round 3 (two statements were merged). In total, 78 practice statements achieved expert consensus.

Conclusions: These expert consensus recommendations cover a broad range of topics relevant to delivery of TCC. Experts agreed that TCC is most effective when delivered by care teams with specific expertise and by programs with explicit protocols focusing on effective communication, technical reliability, and real-time availability. Interventions should be tailored to local conditions. Although further research is needed to guide future best practice statements, these results provide valuable and actionable recommendations for the delivery of high-quality TCC.

Objectives: Sleep promotion bundles being tested in PICUs use elements adapted from adult bundles. As children may react differently than adults in ICU environments, this study investigated what parents report disrupted the sleep of their child in a PICU.

Design: Secondary analysis of a multicenter validation study of the Survey of Sleep quality in the PICU.

Setting: Four Northeastern U.S. PICUs, one hospital-based pediatric sleep laboratory.

Patients: Parents sleeping at the bedside of a child in the PICU or hospital-based sleep laboratory.

Interventions: Anonymous one-time survey eliciting parts of hospital or ICU environments that have been described as disruptive to sleep in validated adult ICU and pediatric inpatient questionnaires.

Measurements and main results: Level of sleep disruption was scored by Likert scale, with higher scores indicating more disruption. Age, demographics, baseline sleep, and PICU exposures were used to describe causes of sleep disruption in a PICU. Of 152 PICU parents, 71% of their children's sleep was disrupted significantly by at least one aspect of being in the PICU. The most prevalent were "being in pain or uncomfortable because they are sick" (38%), "not sleeping at home" (30%), "alarms on machines" (28%), and "not sleeping on their home schedule" (26%). Only 5% were disrupted by excessive nocturnal light exposure. Overall sleep disruption was not different across four PICUs or in those receiving sedation. The validation study control group, healthy children undergoing polysomnography, had less sleep disruption than those in a PICU despite sleeping in a hospital-based sleep laboratory.

Conclusions: There are multiple aspects of critical care environments that affect the sleep of children, which are different from that of adults, such as disruption to home schedules. Future interventional sleep promotion bundles should include sedated children and could be applicable in multicenter settings.

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.

Design: Retrospective analysis of the Premier Healthcare Database.

Setting: Six hundred twelve U.S. hospitals.

Patients: Patients with sepsis and septic shock admitted from the emergency department to the ICU from January 1, 2016, to December 31, 2019.

Interventions: The volume of resuscitative IV fluid administered before the end of hospital day- 1 and mortality.

Measurements and main results: In total, 190,682 patients with sepsis and septic shock were included in the analysis. Based upon patient characteristics and illness severity, we predicted that physicians should prescribe patients with sepsis a narrow mean range of IV fluid (95% range, 3.6-4.5 L). Instead, we observed wide variation in the mean IV fluids administered (95% range, 1.7-7.4 L). After splitting the patients into five groups based upon attending physician practice, we observed patients in the moderate group (4.0 L; interquartile range [IQR], 2.4-5.1 L) experienced a 2.5% reduction in risk-adjusted mortality compared with either the very low (1.6 L; IQR, 1.0-2.5 L) or very high (6.1 L; IQR, 4.0-9.0 L) fluid groups p < 0.01). An analysis of within- and between-hospital IV fluid resuscitation practices showed that physician variation within hospitals instead of practice differences between hospitals accounts for the observed variation.

Conclusions: Individual physician practice drives excess variation in the amount of IV fluid given to patients with sepsis. A moderate approach to IV fluid resuscitation is associated with decreased sepsis mortality and should be tested in future randomized controlled trials.

Objective: To investigate the metabolomic profiles associated with different immune activation states in sepsis patients.

Design: Subgroup analysis of the PROVIDE (a Personalized Randomized trial of Validation and restoration of Immune Dysfunction in severe infections and Sepsis) prospective clinical study.

Setting: Results of the PROVIDE study showed that patients with sepsis may be classified into three states of immune activation: 1) macrophage-activation-like syndrome (MALS) characterized by hyperinflammation, sepsis-induced immunoparalysis, and 3) unclassified or intermediate patients without severe immune dysregulation.

Patients or subjects: Two hundred ten patients from 14 clinical sites in Greece meeting the Sepsis-3 definitions with lung infection, acute cholangitis, or primary bacteremia.

Interventions: During our comparison, we did not perform any intervention.

Measurements and main results: Untargeted metabolomics analysis was performed on plasma samples from 210 patients (a total of 1394 products). Differential abundance analysis identified 221 significantly different metabolites across the immune states. Metabolites were enriched in pathways related to ubiquinone biosynthesis, tyrosine metabolism, and tryptophan metabolism when comparing MALS to immunoparalysis and unclassified patients. When comparing MALS to unclassified, 312 significantly different metabolites were found, and pathway analysis indicated enrichment in multiple pathways. Comparing immunoparalysis to unclassified patients revealed only two differentially regulated metabolites.

Conclusions: Findings suggest distinct metabolic dysregulation patterns associated with different immune dysfunctions in sepsis: the strongest metabolic dysregulation is associated with MALS.

Objectives: To evaluate the transferability of deep learning (DL) models for the early detection of adverse events to previously unseen hospitals.

Design: Retrospective observational cohort study utilizing harmonized intensive care data from four public datasets.

Setting: ICUs across Europe and the United States.

Patients: Adult patients admitted to the ICU for at least 6 hours who had good data quality.

Interventions: None.

Measurements and main results: Using carefully harmonized data from a total of 334,812 ICU stays, we systematically assessed the transferability of DL models for three common adverse events: death, acute kidney injury (AKI), and sepsis. We tested whether using more than one data source and/or algorithmically optimizing for generalizability during training improves model performance at new hospitals. We found that models achieved high area under the receiver operating characteristic (AUROC) for mortality (0.838-0.869), AKI (0.823-0.866), and sepsis (0.749-0.824) at the training hospital. As expected, AUROC dropped when models were applied at other hospitals, sometimes by as much as -0.200. Using more than one dataset for training mitigated the performance drop, with multicenter models performing roughly on par with the best single-center model. Dedicated methods promoting generalizability did not noticeably improve performance in our experiments.

Conclusions: Our results emphasize the importance of diverse training data for DL-based risk prediction. They suggest that as data from more hospitals become available for training, models may become increasingly generalizable. Even so, good performance at a new hospital still depended on the inclusion of compatible hospitals during training.

Objectives: Given the uncertainty regarding the optimal approach to laryngoscopy for the intubation of critically ill adult patients, we conducted a systematic review and meta-analysis to compare video laryngoscopy (VL) vs. direct laryngoscopy (DL) for intubation in emergency department and ICU patients.

Data sources: We searched MEDLINE, PubMed, Embase, Cochrane Library, and unpublished sources, from inception to February 27, 2024.

Study selection: We included randomized controlled trials (RCTs) of critically ill adult patients randomized to VL compared with DL for endotracheal intubation.

Data extraction: Reviewers screened abstracts, full texts, and extracted data independently and in duplicate. We pooled data using a random-effects model, assessed risk of bias using the modified Cochrane tool and certainty of evidence using the Grading Recommendations Assessment, Development, and Evaluation approach. We pre-registered the protocol on PROSPERO (CRD42023469945).

Data synthesis: We included 20 RCTs ( n = 4569 patients). Compared with DL, VL probably increases first pass success (FPS) (relative risk [RR], 1.13; 95% CI, 1.06-1.21; moderate certainty) and probably decreases esophageal intubations (RR, 0.47; 95% CI, 0.27-0.82; moderate certainty). VL may result in fewer aspiration events (RR, 0.74; 95% CI, 0.51-1.09; low certainty) and dental injuries (RR, 0.46; 95% CI, 0.19-1.11; low certainty) and may have no effect on mortality (RR, 0.97; 95% CI, 0.88-1.07; low certainty) compared with DL.

Conclusions: In critically ill adult patients undergoing intubation, the use of VL, compared with DL, probably leads to higher rates of FPS and probably decreases esophageal intubations. VL may result in fewer dental injuries as well as aspiration events compared with DL with no effect on mortality.

Objective: To determine the impact of point-of-care ultrasound (POCUS)-guided resuscitation on clinical outcomes in adult patients with shock.

Data source: We searched MEDLINE, Embase, and unpublished sources from inception to December 2023.

Study selection: We included randomized controlled trials (RCTs) that examined the use of POCUS to guide resuscitation in patients with shock.

Data extraction: We collected data regarding study and patient characteristics, POCUS protocol, control group interventions, and outcomes.

Data synthesis: We identified 18 eligible RCTs. POCUS slightly influences physicians' plans for IV fluid (IVF) and vasoactive medication prescription (moderate certainty), but results in little to no changes in the administration of IVF (low to high certainty) or inotropes (high certainty). POCUS may result in no change in the number of CT scans performed (low certainty) but probably reduces the number of diagnostic echocardiograms performed (moderate certainty). POCUS-guided resuscitation probably reduces 28-day mortality (relative risk [RR] 0.88; 95% CI, 0.78-0.99), the duration of vasoactive medication (mean difference -0.73 d; 95% CI, -1.16 to -0.30), and the need for renal replacement therapy (RRT) (RR 0.80; 95% CI, 0.63-1.02) (low to moderate certainty evidence), and lactate clearance (high certainty evidence). POCUS-guided resuscitation may results in little to no difference in ICU or hospital admissions, ICU and hospital length of stay, and the need for mechanical ventilation (MV) (low to moderate certainty evidence). There is an uncertain effect on the risk of acute kidney injury and the duration of MV or RRT (very low certainty evidence).

Conclusions: POCUS-guided resuscitation in shock may yield important patient and health system benefits. Due to lack of sufficient evidence, we were unable to explore how the thresholds of operator competency, frequency, and timing of POCUS scans impact patient outcomes.