PurposeWhether the quality of the ethical climate in the intensive care unit (ICU) improves the identification of patients receiving excessive care and affects patient outcomes is unknown.MethodsIn this prospective observational study, perceptions of excessive care (PECs) by clinicians working in 68 ICUs in Europe and the USA were collected daily during a 28-day period. The quality of the ethical climate in the ICUs was assessed via a validated questionnaire. We compared the combined endpoint (death, not at home or poor quality of life at 1 year) of patients with PECs and the time from PECs until written treatment-limitation decisions (TLDs) and death across the four climates defined via cluster analysis.ResultsOf the 4747 eligible clinicians, 2992 (63%) evaluated the ethical climate in their ICU. Of the 321 and 623 patients not admitted for monitoring only in ICUs with a good (n = 12, 18%) and poor (n = 24, 35%) climate, 36 (11%) and 74 (12%), respectively were identified with PECs by at least two clinicians. Of the 35 and 71 identified patients with an available combined endpoint, 100% (95% CI 90.0–1.00) and 85.9% (75.4–92.0) (P = 0.02) attained that endpoint. The risk of death (HR 1.88, 95% CI 1.20–2.92) or receiving a written TLD (HR 2.32, CI 1.11–4.85) in patients with PECs by at least two clinicians was higher in ICUs with a good climate than in those with a poor one. The differences between ICUs with an average climate, with (n = 12, 18%) or without (n = 20, 29%) nursing involvement at the end of life, and ICUs with a poor climate were less obvious but still in favour of the former.ConclusionEnhancing the quality of the ethical climate in the ICU may improve both the identification of patients receiving excessive care and the decision-making process at the end of life.Electronic supplementary materialThe online version of this article (10.1007/s00134-018-5231-8) contains supplementary material, which is available to authorized users.

Introduction: The length of stay of critically ill patients in the intensive care unit (ICU) is an indication of patient ICU resource usage and varies considerably. Planning of postoperative ICU admissions is important as ICUs often have no nonoccupied beds available. Problem statement: Estimation of the ICU bed availability for the next coming days is entirely based on clinical judgement by intensivists and therefore too inaccurate. For this reason, predictive models have much potential for improving planning for ICU patient admission. Objective: Our goal is to develop and optimize models for patient survival and ICU length of stay (LOS) based on monitored ICU patient data. Furthermore, these models are compared on their use of sequential organ failure (SOFA) scores as well as underlying raw data as input features. Methodology: Different machine learning techniques are trained, using a 14,480 patient dataset, both on SOFA scores as well as their underlying raw data values from the first five days after admission, in order to predict i) the patient LOS, and ii) the patient mortality. Furthermore, to help physicians in assessing the prediction credibility, a probabilistic model is tailored to the output of our best-performing model, assigning a belief to each patient status prediction. A two-by-two grid is built, using the classification outputs of the mortality and prolonged stay predictors to improve the patient LOS regression models. Results: For predicting patient mortality and a prolonged stay, the best performing model is a support vector machine (SVM) with G A,D = 65.9% (area under the curve (AUC) of 0.77) and G S ,L = 73.2% (AUC of 0.82). In terms of LOS regression, the best performing model is support vector regression, achieving a mean absolute error of 1.79 days and a median absolute error of 1.22 days for those patients surviving a nonprolonged stay. Conclusion: Using a classification grid based on the predicted patient mortality and prolonged stay, allows more accurate modeling of the patient LOS. The detailed models allow to support the decisions made by physicians in an ICU setting.

Introduction: Cardiopulmonary resuscitation (CPR) is often started irrespective of comorbidity or cause of arrest. We aimed to determine the prevalence of perception of inappropriate CPR of the last cardiac arrest encountered by clinicians working in emergency departments and out-of-hospital, factors associated with perception, and its relation to patient outcome. Methods: A cross-sectional survey was conducted in 288 centres in 24 countries. Factors associated with perception of CPR and outcome were analyzed by Cochran-Mantel-Haenszel tests and conditional logistic models. Results: Of the 4018 participating clinicians, 3150 (78.4%) perceived their last CPR attempt as appropriate, 548 (13.6%) were uncertain about its appropriateness and 320 (8.0%) perceived inappropriateness; survival to hospital discharge was 370/2412 (15.3%), 8/481 (1.7%) and 8/294 (2.7%) respectively. After adjusting for country, team and clinician's characteristics, the prevalence of perception of inappropriate CPR was higher for a non-shockable initial rhythm (OR 3.76 [2.13-6.64]; P < .0001), a non-witnessed arrest (2.68 [1.89-3.79]; P < .0001), in older patients (2.94 [2.18-3.96]; P < .0001, for patients > 79 years) and in case of a "poor" first physical impression of the patient (3.45 [2.36-5.05]; P < .0001). In accordance, non-shockable and nonwitnessed arrests were both associated with lower survival to hospital discharge (0.33 [0.26−0.41]; P < 0.0001 and 0.25 [0.15−0.41]; P < 0.0001, respectively), as were older patient age (0.25 [0.14−0.44]; P < 0.0001 for patients > 79 years) and a "poor" first physical impression (0.26 [0.19-0.35]; P < 0.0001). Conclusions: The perception of inappropriate CPR increased when objective indicators of poor prognosis were present and was associated with a low survival to hospital discharge. Factoring clinical judgment into the decision to (not) attempt CPR may reduce harm inflicted by excessive resuscitation attempts.

The emergence of antibiotic-resistant bacteria after exposure to anti-pseudomonal beta-lactam antibiotics was not lower following de-escalation.

BackgroundPreparing an antibiotic stewardship program requires detailed information on overall antibiotic use, prescription indication and ecology. However, longitudinal data of this kind are scarce. Computerization of the patient chart has offered the potential to collect complete data of high resolution. To gain insight in our global antibiotic use, we aimed to explore antibiotic prescription in our intensive care unit (ICU) from various angles over a prolonged time period.MethodsWe studied all adult patients admitted to Ghent University Hospital ICU from 1 January 2013 until 31 December 2016. Antibiotic prescription data were prospectively merged with diagnostic (suspected focus, severity and probability of infection at the time of prescription, or prophylaxis) and microbiology data by ICU physicians during daily workflow through dedicated software. Definite focus of infection and probability of infection (classified as high/moderate/low) were reassessed by dedicated ICU physicians at patient discharge.ResultsDuring the study period, 8763 patients were admitted and overall antibiotic consumption amounted to 1232 days of therapy (DOT)/1000 patient days. Antibacterial DOT (84% of total DOT) were linked with infection in 80%; the predominant foci were the respiratory tract (49%) and the abdomen (19%). A microbial cause was identified in 56% (3169/5686). Moderate/low probability infections accounted for 42% of antibacterial DOT prescribed for respiratory tract infections; for abdominal infections, this figure was 15%. The median treatment duration of moderate/low probability respiratory infections was 4 days (IQR 3–7). Antifungal DOT (16% of total DOT) were linked with infection in 47% of total antifungal DOT. Antifungal prophylaxis was primarily administered in the surgical ICU (76%), with a median duration of 4 DOT (IQR 2–9).ConclusionsBy prospectively combining antibiotic, microbiology and clinical data we were able to construct a longitudinal, multifaceted dataset on antibiotic use and infection diagnosis. A complete overview of this kind may allow the identification of antibiotic prescription patterns that require future antibiotic stewardship attention.Electronic supplementary materialThe online version of this article (10.1186/s13054-018-2178-7) contains supplementary material, which is available to authorized users.