Point-of-care ultrasound (POCUS) is nowadays an essential tool in critical care. Its role seems more important in neonates and children where other monitoring techniques may be unavailable. POCUS ...Working Group of the European Society of Paediatric and Neonatal Intensive Care (ESPNIC) aimed to provide evidence-based clinical guidelines for the use of POCUS in critically ill neonates and children.
Creation of an international Euro-American panel of paediatric and neonatal intensivists expert in POCUS and systematic review of relevant literature. A literature search was performed, and the level of evidence was assessed according to a GRADE method. Recommendations were developed through discussions managed following a Quaker-based consensus technique and evaluating appropriateness using a modified blind RAND/UCLA voting method. AGREE statement was followed to prepare this document.
Panellists agreed on 39 out of 41 recommendations for the use of cardiac, lung, vascular, cerebral and abdominal POCUS in critically ill neonates and children. Recommendations were mostly (28 out of 39) based on moderate quality of evidence (B and C).
Evidence-based guidelines for the use of POCUS in critically ill neonates and children are now available. They will be useful to optimise the use of POCUS, training programs and further research, which are urgently needed given the weak quality of evidence available.
Leaders of critical care services require knowledge and skills not typically acquired during their medical education and training. Leaders possess personality characteristics and evolve and adopt ...behaviors and knowledge in addition to those useful in the care of patients and rounding with an ICU team. Successful leaders have impeccable integrity, possess a service mentality, are decisive, and speak the truth consistently and accurately. Effective leaders are thoughtful listeners, introspective, develop a range of relationships, and nurture others. They understand group psychology, observe, analyze assumptions, decide, and improve the system of care and the performance of their team members. A leader learns to facilely adapt to circumstance, generate new ideas, and be a catalyst of change. Those most successful further their education as a leader and learn when and where to seek mentorship. Leaders understand their organization and its operational complexities. Leaders learn to participate and knowledgeably contribute to the fiscal aspects of income, expense, budget, and contracts from an institutional and department perspective. Clinician compensation must be commensurate with expectations and be written to motivate and make clear duties that are clinical and nonclinical. A leader understands and plans to address the evolving challenges facing healthcare, especially resource constraints, the emotions and requirements of managing the end of life, the complexities of competing demands and motivations, the bureaucracy of healthcare practice, and reimbursement. Responsibilities to manage and evolve must be met with intelligence, sensitivity, and equanimity.
Leaders of critical care programs have significant responsibility to develop and maintain a system of intensive care. At inception, those clinician resources necessary to provide and be available for ...the expected range of patient illness and injury and throughput are determined. Simultaneously, non-ICU clinical responsibilities and other expectations, such as education of trainees and participation in hospital operations, must be understood. To meet these responsibilities, physicians must be recruited, mentored, and retained. The physician leader may have similar responsibilities for nonphysician practitioners. In concert with other critical care leaders, the service adopts a model of care and assembles an ICU team of physicians, nurses, nonphysician providers, respiratory therapists, and others to provide clinical services. Besides clinician resources, leaders must assure that services such as radiology, pharmacy, the laboratory, and information services are positioned to support the complexities of ICU care. Metrics are developed to report success in meeting process and outcomes goals. Leaders evolve the system of care by reassessing and modifying practice patterns to continually improve safety, efficacy, and efficiency. Major emphasis is placed on the importance of continuity, consistency, and communication by expecting practitioners to adopt similar practices and patterns. Services anticipate and adapt to evolving expectations and resource availability. Effective services will result when skilled practitioners support one another and ascribe to a service philosophy of care.
Intensive care units (ICUs) face financial, bed management, and staffing constraints. Detailed data covering all aspects of patients' journeys into and through intensive care are now collected and ...stored in electronic health records: machine learning has been used to analyse such data in order to provide decision support to clinicians.
Systematic review of the applications of machine learning to routinely collected ICU data. Web of Science and MEDLINE databases were searched to identify candidate articles: those on image processing were excluded. The study aim, the type of machine learning used, the size of dataset analysed, whether and how the model was validated, and measures of predictive accuracy were extracted.
Of 2450 papers identified, 258 fulfilled eligibility criteria. The most common study aims were predicting complications (77 papers 29.8% of studies), predicting mortality (70 27.1%), improving prognostic models (43 16.7%), and classifying sub-populations (29 11.2%). Median sample size was 488 (IQR 108-4099): 41 studies analysed data on > 10,000 patients. Analyses focused on 169 (65.5%) papers that used machine learning to predict complications, mortality, length of stay, or improvement of health. Predictions were validated in 161 (95.2%) of these studies: the area under the ROC curve (AUC) was reported by 97 (60.2%) but only 10 (6.2%) validated predictions using independent data. The median AUC was 0.83 in studies of 1000-10,000 patients, rising to 0.94 in studies of > 100,000 patients. The most common machine learning methods were neural networks (72 studies 42.6%), support vector machines (40 23.7%), and classification/decision trees (34 20.1%). Since 2015 (125 studies 48.4%), the most common methods were support vector machines (37 studies 29.6%) and random forests (29 23.2%).
The rate of publication of studies using machine learning to analyse routinely collected ICU data is increasing rapidly. The sample sizes used in many published studies are too small to exploit the potential of these methods. Methodological and reporting guidelines are needed, particularly with regard to the choice of method and validation of predictions, to increase confidence in reported findings and aid in translating findings towards routine use in clinical practice.
Early mobility in mechanically ventilated patients is safe, feasible, and may improve functional outcomes. We sought to determine the prevalence and character of mobility for ICU patients with acute ...respiratory failure in U.S. ICUs.
Two-day cross-sectional point prevalence study.
Forty-two ICUs across 17 Acute Respiratory Distress Syndrome Network hospitals.
Adult patients (≥ 18 yr old) with acute respiratory failure requiring mechanical ventilation.
We defined therapist-provided mobility as the proportion of patient-days with any physical or occupational therapy-provided mobility event. Hierarchical regression models were used to identify predictors of out-of-bed mobility.
Hospitals contributed 770 patient-days of data. Patients received mechanical ventilation on 73% of the patient-days mostly (n = 432; 56%) ventilated via an endotracheal tube. The prevalence of physical therapy/occupational therapy-provided mobility was 32% (247/770), with a significantly higher proportion of nonmechanically ventilated patients receiving physical therapy/occupational therapy (48% vs 26%; p ≤ 0.001). Patients on mechanical ventilation achieved out-of-bed mobility on 16% (n = 90) of the total patient-days. Physical therapy/occupational therapy involvement in mobility events was strongly associated with progression to out-of-bed mobility (odds ratio, 29.1; CI, 15.1-56.3; p ≤ 0.001). Presence of an endotracheal tube and delirium were negatively associated with out-of-bed mobility.
In a cohort of hospitals caring for acute respiratory failure patients, physical therapy/occupational therapy-provided mobility was infrequent. Physical therapy/occupational therapy involvement in mobility was strongly predictive of achieving greater mobility levels in patients with respiratory failure. Mechanical ventilation via an endotracheal tube and delirium are important predictors of mobility progression.
The optimization of intensive care unit (ICU) care impacts clinical outcomes and resource utilization. In 2017, our surgical ICU (SICU) adopted a “closed-collaborative” model. The aim of this study ...is to compare patient outcomes in the closed-collaborative model versus the previous open model in a cohort of trauma surgical patients admitted to our adult level 1 trauma center.
A retrospective review of trauma patients in the SICU from August 1, 2015 to July 31, 2019 was performed. Patients were divided into those admitted prior to August 1, 2017 (the “open” cohort) and those admitted after August 1, 2017 (the “closed-collaborative” cohort). Demographic variables and clinical outcomes were analyzed. Trauma severity was assessed using injury severity score (ISS).
We identified 1669 patients (O: 895; C: 774). While no differences in demographics were observed, the closed-collaborative cohort had a higher overall ISS (O: 21.5 ± 12.14; C: 25.10 ± 2.72; P < 0.0001). There were no significant differences between the two cohorts in the incidence of strokes (O: 1.90%; C: 2.58%, P = 0.3435), pulmonary embolism (O: 0.78%; C: 0.65%; P = 0.7427), sepsis (O: 5.25%; C: 7.49%; P = 0.0599), median ICU charges (O: $7784.50; C: $8986.53; P = 0.5286), mortality (O: 11.40%; C: 13.18%; P = 0.2678), or ICU length of stay (LOS) (O: 4.85 ± 6.23; C: 4.37 ± 4.94; P = 0.0795).
Patients in the closed-collaborative cohort had similar clinical outcomes despite having a sicker cohort of patients. We hypothesize that the closed-collaborative ICU model was able to maintain equivalent outcomes due to the dedicated multidisciplinary critical care team caring for these patients. Further research is warranted to determine the optimal model of ICU care for trauma patients.
•Impact of surgical ICU modeling on trauma surgical patient outcomes.•Compared to an open model, a closed-collaborative SICU model provides.oEquivalent care (mortality and complications) despite having sicker patients.oSignificant reduction in ICU LOS in the sickest cohort of patients (ISS ≥15).oNonstatistically significant reduction in ICU charges in the sickest cohort of patients (ISS ≥15).
In the recent years, digital intensive care unit (ICU) diaries have emerged as more advantageous than paper diaries. Despite the advantages of digital diaries, the successful implementation and ...maintenance of this digital intervention present significant challenges in clinical practice. Therefore, understanding the facilitators and barriers among stakeholders influencing this process becomes imperative for devising a tailored strategy to integrate digital diaries effectively within ICU settings.
The aim of this study was to explore facilitators and barriers for implementation of a digital ICU diary from the perspectives of ICU professionals, ICU survivors, and their relatives.
A qualitative design was used, incorporating focus-group interviews with professionals from four Dutch ICUs, along with individual interviews with ICU survivors and relatives. The study spanned from October 2022 to April 2023. Data analysis utilised a mixed inductive–deductive approach, particularly through directed content analysis. The Consolidated Framework for Implementation Research 2.0 guided both data collection and analysis processes.
We conducted five focus-group interviews among ICU professionals (n = 32) and 10 individual or dual interviews involving five ICU survivors and nine relatives. Key facilitators for implementing a digital diary according to ICU professionals encompassed a user-friendly interface accessible independent of time and place, with a seamless login process requiring minimal steps, comprehensive training covering all aspects of its use, and feedback from the experiences of both patients and relatives. Barriers for ICU professionals included many steps required to access the digital diary, as well as resistance to (co)writing diary entries. In contrast, professionals’ involvement in writing diary entries was highly appreciated among ICU survivors and relatives. An ambiguous factor arose regarding sharing the digital diary with others; both ICU survivors and relatives found it valuable, yet it also raised privacy concerns.
This study offers insights into the most important factors influencing the implementation of a digital ICU diary. Strikingly, some factors serve as both barriers and facilitators. When developing the implementation strategy, the identified facilitators can be used to overcome the barriers faced by ICU professionals, ICU survivors, and their relatives in adopting a digital diary.
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