American Journal of Critical Care最新文献

Background: Communication and collaboration among health care professionals during bedside rounds improve patient outcomes and nurses' and physicians' satisfaction.

Objectives: To determine barriers to nurse-physician communication during bedside rounds and identify opportunities to improve nurse-physician collaboration at an academic medical center.

Methods: A survey with Likert-scale and open-ended questions regarding professional attitudes toward nurse-physician communication was administered to 220 nurses and physicians in medical-surgical units to assess perceptions of participation in bedside rounds. After the survey was given, observational data from 1007 bedside rounds were collected via a standardized data collection tool.

Results: Nurses and physicians perceived different barriers to including nurses in bedside rounds. Nurses most often cited being unaware that bedside rounds were occurring (38 of 46 nurses [83%]); physicians most often cited nurse unavailability (43 of 52 physicians [83%]). Of 1007 observed rounds, 602 (60%) involved in-person contact of nurses and physicians; 418 (69%) of the 602 included a conversation between the nurse and physician about the nurse's concerns. Of 355 rounds with no in-person or telephone contact between nurses and physicians, the medicine team did not contact the nurse in 284 (80%). Conversations about nurses' concerns occurred more often after physician-initiated contacts (73% of 369 contacts) and nurse-initiated contacts (74% of 93 contacts) than after chance encounters (57% of 140 contacts).

Conclusion: Initiating discussions of care between nurses and physicians and discussing nurses' concerns during bedside rounds have multiple benefits.

Background: Sepsis is a complex condition with high morbidity and mortality. Prompt treatment can improve survival, but for survivors the risk of deterioration and readmission remains high. Little is known about the association between discharge setting and readmission among sepsis survivors.

Objective: To examine 30-day hospital readmission rates in adult sepsis survivors by the type of setting to which patients were discharged.

Methods: The Medical Information Mart for Intensive Care database was used to identify adult sepsis survivors and evaluate 30-day readmission by discharge setting. A χ2 contingency analysis was used with each factor and presence/absence of readmission. The Kruskal-Wallis test was used to compare readmissions across discharge settings.

Results: From our sample (N = 7107; mean age 66.5 years; 46.2% women), 23.6% (n = 1674) were readmitted within 30 days and of those readmitted, 30% were readmitted between 1 and 3 times. Discharge setting (P < .001) and age (P = .02) were significantly associated with readmission, but sex, ethnicity, and insurance type were not. High numbers of readmissions were seen in patients discharged to skilled nursing facilities (29.6%), home health care (26.9%), and home (15.0%). Similar high comorbidity burden and acuteness of illness were seen in patients discharged to these settings.

Conclusions: Sepsis survivors discharged to skilled nursing facilities, home health care, and home are at high risk for 30-day readmission. The rates of readmission from home health care and home settings were alarming. Often patients are discharged to inappropriate settings, placing them at risk for residual sepsis and readmission. Future research should focus on appropriate timing of hospital discharge and transition to the most appropriate discharge setting.

Background: Promoting bonding and neurodevelopmental care is an important element in the cardiovascular intensive care unit (CICU); however, holding of infants by family members is inconsistently practiced.

Objectives: This quality improvement study aimed to safely increase the holding of medically complex infants in the CICU by developing a holding guideline and offering simulation-based staff education.

Methods: Using consensus methodology and high-fidelity simulation, an expert work group created a holding guideline and training to increase staff confidence and competence in holding critically ill infants in the CICU. The effectiveness of the intervention was tested via a postintervention survey used to assess participants' confidence in and comfort with holding critically ill infants and elicit suggestions for further support. Participants who engaged with the mannequin were surveyed to assess the validity of the simulation trainer.

Results: After the intervention, participants (N = 130) reported increased confidence with infant holding (76%) and greater confidence in preparing families to hold their infants. Participants (95%) strongly agreed that the simulation enabled realistic, safe practice in holding a medically complex infant with catheters and tubes. More years of experience and engagement with the simulation trainer were associated with increased posttraining confidence. Participants cited increased resources, practice, and adherence monitoring as key supports for infant holding.

Conclusions: Expert group consensus with high-fidelity simulation training is a feasible, safe, and reliable method for teaching higher risk skills and guiding protocol development.

Background: Evaluating energy expenditure is important for establishing optimal goals for nutrition treatment. However, indirect calorimetry, the reference standard for measuring energy expenditure, is difficult to apply widely in clinical practice.

Objective: To test the consistency of bioelectrical impedance analysis (BIA) relative to indirect calorimetry for evaluating energy expenditure in critically ill patients.

Methods: A cross-sectional study of 140 critically ill adult patients was conducted. Within 24 hours of a patient being transferred to the intensive care unit, trained researchers assessed the patient's energy expenditure by use of BIA and indirect calorimetry simultaneously. Consistency of the 2 measurements was detected by intraclass correlation coefficients with a 2-way random-effects model. Factors affecting consistency were analyzed.

Results: Median energy expenditure measured by indirect calorimetry was 1430.0 kcal/d (IQR, 1190.5-1650.8 kcal/d). Median energy expenditure measured by BIA was 1407.0 kcal/d (IQR, 1248.5-1563.5 kcal/d). The correlation coefficient between indirect calorimetry and BIA was 0.813 (95% CI, 0.748-0.862; P < .001). The consistency of the 2 measurements was lower in patients with comorbidities than in those without (P = .004).

Conclusions: Results of BIA were highly consistent with indirect calorimetry assessments of energy expenditure in critically ill patients. Few factors except comorbidity affect the accuracy of BIA when assessing energy expenditure. Therefore, as a low-cost, easy-to-use, and noninvasive method, BIA is a valuable clinical tool for assessing energy expenditure in critically ill patients.

Background: Hospital-acquired pressure injuries (HAPIs) have a major impact on patient outcomes in intensive care units (ICUs). Effective prevention relies on early and accurate risk assessment. Traditional risk-assessment tools, such as the Braden Scale, often fail to capture ICU-specific factors, limiting their predictive accuracy. Although artificial intelligence models offer improved accuracy, their "black box" nature poses a barrier to clinical adoption.

Objective: To develop an artificial intelligence-based HAPI risk-assessment model enhanced with an explainable artificial intelligence dashboard to improve interpretability at both the global and individual patient levels.

Methods: An explainable artificial intelligence approach was used to analyze ICU patient data from the Medical Information Mart for Intensive Care. Predictor variables were restricted to the first 48 hours after ICU admission. Various machine-learning algorithms were evaluated, culminating in an ensemble "super learner" model. The model's performance was quantified using the area under the receiver operating characteristic curve through 5-fold cross-validation. An explainer dashboard was developed (using synthetic data for patient privacy), featuring interactive visualizations for in-depth model interpretation at the global and local levels.

Results: The final sample comprised 28 395 patients with a 4.9% incidence of HAPIs. The ensemble super learner model performed well (area under curve = 0.80). The explainer dashboard provided global and patient-level interactive visualizations of model predictions, showing each variable's influence on the risk-assessment outcome.

Conclusion: The model and its dashboard provide clinicians with a transparent, interpretable artificial intelligence-based risk-assessment system for HAPIs that may enable more effective and timely preventive interventions.

Background: Adoption of early mobility interventions into intensive care unit (ICU) practice has been slow and varied.

Objectives: To examine factors associated with early mobility performance in critically ill adults and evaluate factors' effects on predicting next-day early mobility performance.

Methods: A secondary analysis of 66 ICUs' data from patients admitted for at least 24 hours. Mixed-effects logistic regression modeling was done, with area under the receiver operating characteristic curve (AUC) calculated.

Results: In 12 489 patients, factors independently associated with higher odds of next-day mobility included significant pain (adjusted odds ratio [AOR], 1.16; 95% CI, 1.09-1.23), documented sedation target (AOR, 1.09; 95% CI, 1.01-1.18), performance of spontaneous awakening trials (AOR, 1.77; 95% CI, 1.59-1.96), spontaneous breathing trials (AOR, 2.35; 95% CI, 2.14-2.58), mobility safety screening (AOR, 2.26; 95% CI, 2.04-2.49), and prior-day physical/occupational therapy (AOR, 1.44; 95% CI, 1.30-1.59). Factors independently associated with lower odds of next-day mobility included deep sedation (AOR, 0.44; 95% CI, 0.39-0.49), delirium (AOR, 0.63; 95% CI, 0.59-0.69), benzodiazepine administration (AOR, 0.85; 95% CI, 0.79-0.92), physical restraints (AOR, 0.74; 95% CI, 0.68-0.80), and mechanical ventilation (AOR, 0.73; 95% CI, 0.68-0.78). Black and Hispanic patients had lower odds of next-day mobility than other patients. Models incorporating patient, practice, and between-unit variations displayed high discriminant accuracy (AUC, 0.853) in predicting next-day early mobility performance.

Conclusions: Collectively, several modifiable and nonmodifiable factors provide excellent prediction of next-day early mobility performance.

Background: Little is known about differences in patient characteristics before and after implementation of the new definition of sepsis (Sepsis-3) and whether the new definition is affecting clinical practice in intensive care units.

Objective: To examine and compare the clinicoepidemiologic characteristics of patients with sepsis or septic shock before and after implementation of Sepsis-3.

Methods: In this population-based cohort study, a nationwide registration database in South Korea was used to identify patients with sepsis or septic shock. Patients admitted to hospitals from 2012 to 2015 constituted the Sepsis-2 group, and patients admitted from 2017 to 2020 constituted the Sepsis-3 group.

Results: The study involved 443 217 patients, of whom 170 660 (38.5%) were in the Sepsis-2 group and 272 557 (61.5%) were in the Sepsis-3 group. The mean (SD) age was 73.3 (14.5) years in the Sepsis-2 group and 75.5 (14.5) years in the Sepsis-3 group. The intensive care unit admission rate was higher in the Sepsis-2 group (34.6%, 59 081 of 170 660) than in the Sepsis-3 group (21.3%, 57 997 of 272 557). Multivariable Cox regression analysis showed that 1-year all-cause mortality was 21% lower in the Sepsis-3 group than in the Sepsis-2 group (hazard ratio, 0.79; 95% CI, 0.78-0.79; P < .001).

Conclusions: Implementation of the Sepsis-3 definition was associated with an increased number of patients with sepsis. Other findings suggested that patients in the Sepsis-2 group had more severe illness, with increased 1-year all-cause mortality, compared with those in the Sepsis-3 group.