American Journal of Critical Care最新文献

Background: Various social determinants of health have been established as significant risk factors for COVID-19 transmission, prevalence, incidence, and mortality. Area deprivation index (ADI, a composite score made up of educational, housing, and poverty markers) is an accepted multidimensional social determinants of health measure. Little is known about how structural social determinants of health before hospitalization, including ADI, may affect mortality related to COVID-19 in critically ill patients.

Objectives: To examine the association of ADI with intensive care unit (ICU) mortality in patients with COVID-19 and compare its predictive power with that of clinical factors.

Methods: This was a retrospective cohort study of critically ill adults with COVID-19 in 3 hospitals within a single health system. Multivariable logistic regression models (adjusted for demographic and clinical variables) were used to examine the association of ADI with ICU mortality.

Results: Data from 1784 patients hospitalized from 2020 to 2022 were analyzed. In multivariable models, no association was found between national ADI and ICU mortality. Notable factors associated with ICU mortality included treatment year, age, van Walraven weighted score, invasive mechanical ventilation, and body mass index.

Conclusion: In this study, clinical factors were more predictive of mortality than ADI and other social determinants of health. The influence of ADI may be most relevant before hospital admission. These findings could serve as a foundation for shaping targeted public health strategies and hospital interventions, enhancing care delivery, and potentially contributing to better outcomes in future pandemics.

Background: Although nurses are the primary clinicians in telemedicine intensive care units (tele-ICUs), their experiences remain underresearched.

Objective: To describe and interpret the lived experiences of tele-ICU nurses.

Methods: A qualitative, hermeneutical, phenomenological approach based on van Manen's methodology was used to collect and interpret interview data. In-depth interviews were conducted with 11 tele-ICU nurses affiliated with a health system in the northeastern United States. The interviews were intended to elicit direct reports of specific incidents to capture the phenomenological experience. Field observations of the practice setting were conducted to enhance data richness.

Results: The participants were primarily female, aged 34 to 67 years, with 11 to 45 years of nursing experience. The following 4 major themes and 12 subthemes were identified: (1) watching intensively and panoptically, with subthemes (a) affirming unremitting diligence, (b) uncovering virtual knowing, and (c) easing perceptions of intrusion; (2) transcending complex boundaries, with subthemes (a) building trustful collaboration, (b) discerning vigilant interventions, and (c) exercising skillful investigation; (3) transforming nursing practice, with subthemes (a) mastering tact, (b) delineating the critical wholeness, and (c) augmenting safety and quality of care; and (4) developing an e-identity, with subthemes (a) tempering feelings of working in shadows, (b) evolving to the enhanced specialist role, and (c) achieving professional gratification.

Conclusions: Tele-ICU nurses play a transformative role in nursing practice. These findings have implications for nursing practice, education, health policy, enhancement of the current American Association of Critical-Care Nurses (AACN) Tele-ICU Nursing Practice Model, and future research on ICU telemedicine.

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: 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.