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Purpose To provide consensus, and a list of experts’ recommendations regarding the basic skills for head-to-toe ultrasonography in the intensive care setting. Methods The Executive Committee of the European Society of Intensive Care (ESICM) commissioned the project and supervised the methodology and structure of the consensus. We selected an international panel of 19 expert clinicians–researchers in intensive care unit (ICU) with expertise in critical care ultrasonography (US), plus a non-voting methodologist. The panel was divided into five subgroups (brain, lung, heart, abdomen and vascular ultrasound) which identified the domains and generated a list of questions to be addressed by the panel. A Delphi process based on an iterative approach was used to obtain the final consensus statements. Statements were classified as a strong recommendation (84% of agreement), weak recommendation (74% of agreement), and no recommendation (less than 74%), in favor or against. Results This consensus produced a total of 74 statements (7 for brain, 20 for lung, 20 for heart, 20 for abdomen, 7 for vascular Ultrasound). We obtained strong agreement in favor for 49 statements (66.2%), 8 weak in favor (10.8%), 3 weak against (4.1%), and no consensus in 14 cases (19.9%). In most cases when consensus was not obtained, it was felt that the skills were considered as too advanced. A research agenda and discussion on training programs were implemented from the results of the consensus. Conclusions This consensus provides guidance for the basic use of critical care US and paves the way for the development of training and research projects.

Intensive care survivors often experience post-intensive care sequelae, which are frequently gathered together under the term “post-intensive care syndrome” (PICS). The consequences of PICS on quality of life, health-related costs and hospital readmissions are real public health problems. In the present Viewpoint, we summarize current knowledge and gaps in our understanding of PICS and approaches to management.

Abstract Rationale Communication with family of critically ill patients is often poor and associated with family distress. Objectives To determine if an intensive care unit (ICU) communication facilitator reduces family distress and intensity of end-of-life care. Methods We conducted a randomized trial at two hospitals. Eligible patients had a predicted mortality greater than or equal to 30% and a surrogate decision maker. Facilitators supported communication between clinicians and families, adapted communication to family needs, and mediated conflict. Measurements and Main Results Outcomes included depression, anxiety, and post-traumatic stress disorder (PTSD) among family 3 and 6 months after ICU and resource use. We identified 488 eligible patients and randomized 168. Of 352 eligible family members, 268 participated (76%). Family follow-up at 3 and 6 months ranged from 42 to 47%. The intervention was associated with decreased depressive symptoms at 6 months (P = 0.017), but there were no significant differences in psychological symptoms at 3 months or anxiety or PTSD at 6 months. The intervention was not associated with ICU mortality (25% control vs. 21% intervention; P = 0.615) but decreased ICU costs among all patients (per patient: $75,850 control, $51,060 intervention; P = 0.042) and particularly among decedents ($98,220 control, $22,690 intervention; P = 0.028). Among decedents, the intervention reduced ICU and hospital length of stay (28.5 vs. 7.7 d and 31.8 vs. 8.0 d, respectively; P < 0.001). Conclusions Communication facilitators may be associated with decreased family depressive symptoms at 6 months, but we found no significant difference at 3 months or in anxiety or PTSD. The intervention reduced costs and length of stay, especially among decedents. This is the first study to find a reduction in intensity of end-of-life care with similar or improved family distress. Clinical trial registered with www.clinicaltrials.gov (NCT 00720200).

Background 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. Methods 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. Results 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). Conclusions 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.

PurposePremorbid conditions affect prognosis of acutely-ill aged patients. Several lines of evidence suggest geriatric syndromes need to be assessed but little is known on their relative effect on the 30-day survival after ICU admission. The primary aim of this study was to describe the prevalence of frailty, cognition decline and activity of daily life in addition to the presence of comorbidity and polypharmacy and to assess their influence on 30-day survival.MethodsProspective cohort study with 242 ICUs from 22 countries. Patients 80 years or above acutely admitted over a six months period to an ICU between May 2018 and May 2019 were included. In addition to common patients’ characteristics and disease severity, we collected information on specific geriatric syndromes as potential predictive factors for 30-day survival, frailty (Clinical Frailty scale) with a CFS > 4 defining frail patients, cognitive impairment (informant questionnaire on cognitive decline in the elderly (IQCODE) with IQCODE ≥ 3.5 defining cognitive decline, and disability (measured the activity of daily life with the Katz index) with ADL ≤ 4 defining disability. A Principal Component Analysis to identify co-linearity between geriatric syndromes was performed and from this a multivariable model was built with all geriatric information or only one: CFS, IQCODE or ADL. Akaike’s information criterion across imputations was used to evaluate the goodness of fit of our models.ResultsWe included 3920 patients with a median age of 84 years (IQR: 81–87), 53.3% males). 80% received at least one organ support. The median ICU length of stay was 3.88 days (IQR: 1.83–8). The ICU and 30-day survival were 72.5% and 61.2% respectively. The geriatric conditions were median (IQR): CFS: 4 (3–6); IQCODE: 3.19 (3–3.69); ADL: 6 (4–6); Comorbidity and Polypharmacy score (CPS): 10 (7–14). CFS, ADL and IQCODE were closely correlated. The multivariable analysis identified predictors of 1-month mortality (HR; 95% CI): Age (per 1 year increase): 1.02 (1.–1.03, p = 0.01), ICU admission diagnosis, sequential organ failure assessment score (SOFA) (per point): 1.15 (1.14–1.17, p < 0.0001) and CFS (per point): 1.1 (1.05–1.15, p < 0.001). CFS remained an independent factor after inclusion of life-sustaining treatment limitation in the model.ConclusionWe confirm that frailty assessment using the CFS is able to predict short-term mortality in elderly patients admitted to ICU. Other geriatric syndromes do not add improvement to the prediction model. Since CFS is easy to measure, it should be routinely collected for all elderly ICU patients in particular in connection to advance care plans, and should be used in decision making.

Abstract Rationale Intensive care unit (ICU)-acquired weakness is a frequent complication of critical illness. It is unclear whether it is a marker or mediator of poor outcomes. Objectives To determine acute outcomes, 1-year mortality, and costs of ICU-acquired weakness among long-stay (≥8 d) ICU patients and to assess the impact of recovery of weakness at ICU discharge. Methods Data were prospectively collected during a randomized controlled trial. Impact of weakness on outcomes and costs was analyzed with a one-to-one propensity-score-matching for baseline characteristics, illness severity, and risk factor exposure before assessment. Among weak patients, impact of persistent weakness at ICU discharge on risk of death after 1 year was examined with multivariable Cox proportional hazards analysis. Measurements and Main Results A total of 78.6% were admitted to the surgical ICU; 227 of 415 (55%) long-stay assessable ICU patients were weak; 122 weak patients were matched to 122 not-weak patients. As compared with matched not-weak patients, weak patients had a lower likelihood for live weaning from mechanical ventilation (hazard ratio [HR], 0.709 [0.549–0.888]; P = 0.009), live ICU (HR, 0.698 [0.553–0.861]; P = 0.008) and hospital discharge (HR, 0.680 [0.514–0.871]; P = 0.007). In-hospital costs per patient (+30.5%, +5,443 Euro per patient; P = 0.04) and 1-year mortality (30.6% vs. 17.2%; P = 0.015) were also higher. The 105 of 227 (46%) weak patients not matchable to not-weak patients had even worse prognosis and higher costs. The 1-year risk of death was further increased if weakness persisted and was more severe as compared with recovery of weakness at ICU discharge (P < 0.001). Conclusions After careful matching the data suggest that ICU-acquired weakness worsens acute morbidity and increases healthcare-related costs and 1-year mortality. Persistence and severity of weakness at ICU discharge further increased 1-year mortality. Clinical trial registered with www.clinicaltrials.gov (NCT 00512122).

PurposeDespite its promise to enhance patient outcomes and support clinical decision making, clinical use of artificial intelligence (AI) models at the bedside remains limited. Translation of advancements in AI research into tangible clinical benefits is necessary to improve neonatal and pediatric care for critically ill patients. This systematic review seeks to assess the maturity of AI models in neonatal and pediatric intensive care unit (NICU and PICU) treatment, and their risk of bias and objectives.MethodsWe conducted a systematic search in Medline ALL, Embase, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and Google Scholar. Studies using AI models during NICU or PICU stay were eligible for inclusion. Study design, objective, dataset size, level of validation, risk of bias, and technological readiness of the models were extracted.ResultsOut of the 1257 identified studies 262 were included. The majority of studies was conducted in the NICU (66%) and most had a high risk of bias (77%). An insufficient sample size was the main cause for this high risk of bias. No studies were identified that integrated an AI model in routine clinical practice and the majority of the studies remained in the prototyping and model development phase.ConclusionThe majority of AI models remain within the testing and prototyping phase and have a high risk of bias. Bridging the gap between designing and clinical implementation of AI models is needed to warrant safe and trustworthy AI models. Specific guidelines and approaches can help improve clinical outcome with usage of AI.

Purpose To improve the outcome of the acute respiratory distress syndrome (ARDS), one needs to identify potentially modifiable factors associated with mortality. Methods The large observational study to understand the global impact of severe acute respiratory failure (LUNG SAFE) was an international, multicenter, prospective cohort study of patients with severe respiratory failure, conducted in the winter of 2014 in a convenience sample of 459 ICUs from 50 countries across five continents. A pre-specified secondary aim was to examine the factors associated with outcome. Analyses were restricted to patients (93.1 %) fulfilling ARDS criteria on day 1–2 who received invasive mechanical ventilation. Results 2377 patients were included in the analysis. Potentially modifiable factors associated with increased hospital mortality in multivariable analyses include lower PEEP, higher peak inspiratory, plateau, and driving pressures, and increased respiratory rate. The impact of tidal volume on outcome was unclear. Having fewer ICU beds was also associated with higher hospital mortality. Non-modifiable factors associated with worsened outcome from ARDS included older age, active neoplasm, hematologic neoplasm, and chronic liver failure. Severity of illness indices including lower pH, lower PaO 2 /FiO 2 ratio, and higher non-pulmonary SOFA score were associated with poorer outcome. Of the 578 (24.3 %) patients with a limitation of life-sustaining therapies or measures decision, 498 (86.0 %) died in hospital. Factors associated with increased likelihood of limitation of life-sustaining therapies or measures decision included older age, immunosuppression, neoplasia, lower pH and increased non-pulmonary SOFA scores. Conclusions Higher PEEP, lower peak, plateau, and driving pressures, and lower respiratory rate are associated with improved survival from ARDS. Trial Registration: ClinicalTrials.gov NCT02010073.

Background 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. Methods 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. Results 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%]). Conclusions 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.

Background Hospital-acquired and ventilator-associated pneumonia (HAP and VAP) are pneumonias arising > 48 h after admission or intubation respectively. Conventionally, HAP/VAP patients are given broad-spectrum empiric antibiotics at clinical diagnosis, refined after 48–72 h, once microbiology results become available. Molecular tests offer swifter results, potentially improving patient care. To investigate whether this potential is realisable, we conducted a pragmatic multi-centre RCT (‘INHALE WP3’) of rapid, syndromic polymerase chain reaction (PCR) in ICU HAP/VAP compared with standard of care. As the use of molecular tests impact on hospital resources, it is important to consider their potential value-for-money to make fully informed decisions. Consequently, INHALE WP3 included an economic evaluation, presented here. Its aim was to estimate the cost-effectiveness of an in-ICU PCR (bioMérieux BioFire FilmArray Pneumonia Panel) in HAP/VAP, informing whether to implement such technology in routine NHS care. Methods We collected data on patient resource use and costs. These data were combined with INHALE WP3’s two primary outcome measures: antibiotic stewardship at 24 h and clinical cure at 14 days. Cost-effectiveness analyses were carried out using regression models adjusting for site. Sensitivity analyses explored assumptions and sub-group analyses explored differential impacts. Results We found lower total ICU costs (including PCR costs) in the intervention (PCR-guided therapy) group. Average costs were £40,951 for standard of care compared with £33,149 for the intervention group, a difference of − £7,802 (95% CI: − £15,696, £92). For antibiotic stewardship, the PCR-guided therapy was both less costly and more effective than routine patient management. For clinical cure, we did not find PCR-guided therapy to be cost-effective due to fewer cases being cured in the intervention group. Conclusions We found lower average ICU costs with the Pneumonia Panel. The pneumonia panel was cost-effective in terms of antibiotic stewardship, but not clinical cure. Trial registration : Registered as ISRCTN16483855 on 5th August 2019.