Real-world causal evidence for planned predictive enrichment in critical care trials: A scoping review.

BACKGROUND: Randomised clinical trials in critical care are prone to inconclusiveness due, in part, to undue optimism about effect sizes and suboptimal accounting for heterogeneous treatment effects. Although causal evidence from rich real-world critical care can help overcome these challenges by informing predictive enrichment, no overview exists. METHODS: We conducted a scoping review, systematically searching 10 general and speciality journals for reports published on or after 1 January 2018, of randomised clinical trials enrolling adult critically ill patients. We collected trial metadata on 22 variables including recruitment period, intervention type and early stopping (including reasons) as well as data on the use of causal evidence from secondary data for planned predictive enrichment. RESULTS: We screened 9020 records and included 316 unique RCTs with a total of 268,563 randomised participants. One hundred seventy-three (55%) trials tested drug interventions, 101 (32%) management strategies and 42 (13%) devices. The median duration of enrolment was 2.2 (IQR: 1.3-3.4) years, and 83% of trials randomised less than 1000 participants. Thirty-six trials (11%) were restricted to COVID-19 patients. Of the 55 (17%) trials that stopped early, 23 (42%) used predefined rules; futility, slow enrolment and safety concerns were the commonest stopping reasons. None of the included RCTs had used causal evidence from secondary data for planned predictive enrichment. CONCLUSION: Work is needed to harness the rich multiverse of critical care data and establish its utility in critical care RCTs. Such work will likely need to leverage methodology from interventional and analytical epidemiology as well as data science.

The effect of technical filtering and clinical criteria on alert rates from continuous vital sign monitoring in the general ward.

OBJECTIVES: Continuous vital sign monitoring at the general hospital ward has major potential advantages over intermittent monitoring but generates many alerts with risk of alert fatigue. We hypothesized that the number of alerts would decrease using different filters. METHODS: This study was an exploratory analysis of the alert reducing effect from adding two different filters to continuously collected vital sign data (peripheral oxygen saturation, blood pressure, heart rate, and respiratory rate) in patients admitted after major surgery or severe medical disease. Filtered data were compared to data without artifact removal. Filter one consists of artifact removal, filter two consists of artifact removal plus duration criteria adjusted for severity of vital sign deviation. Alert thresholds were based on the National Early Warning Score (NEWS) threshold. RESULTS: A population of 716 patients admitted for severe medical disease or major surgery with continuous wireless vital sign monitoring at the general ward with a mean monitoring time of 75.8 h, were included for the analysis. Without artifact removal, we found a median of 137 [IQR: 87-188] alerts per patient/day, artifact removal resulted in a median of 101 [IQR: 56-160] alerts per patient/day and with artifact removal combined with a duration-severity criterion, we found a median of 19 [IQR: 9-34] alerts per patient/day. Reduction of alerts was 86.4% (p < 0.001) for values without artifact removal (137 alerts) vs. the duration criteria and a reduction (19 alerts) of 81.5% (p < 0.001) for the criteria with artifact removal (101 alerts) vs. the duration criteria (19 alerts). CONCLUSION: We conclude that a combination of artifact removal and duration-severity criteria approach substantially reduces alerts generated by continuous vital sign monitoring.

Deviating vital signs in continuous monitoring prior to discharge and risk of readmission: an observational study.

Premature discharge may result in readmission while longer hospitalization may increase risk of complications such as immobilization and reduce hospital capacity. Continuous monitoring detects more deviating vital signs than intermittent measurements and may help identify patients at risk of deterioration after discharge. We aimed to investigate the association between deviating vital signs detected by continuous monitoring prior to discharge and risk of readmission within 30 days. Patients undergoing elective major abdominal surgery or admitted with acute exacerbation of chronic obstructive pulmonary disease were included in this study. Eligible patients had vital signs monitored continuously within the last 24 h prior to discharge. The association between sustained deviated vital signs and readmission risk was analyzed by using Mann-Whitney's U test and Chi-square test. A total of 51 out of 265 patients (19%) were readmitted within 30 days. Deviated respiratory vital signs occurred frequently in both groups: desaturation < 88% for at least ten minutes was seen in 66% of patients who were readmitted and in 62% of those who were not (p = 0.62) while desaturation < 85% for at least five minutes was seen in 58% of readmitted and 52% of non-readmitted patients (p = 0.5). At least one sustained deviated vital sign was detected in 90% and 85% of readmitted patients and non-readmitted patients, respectively (p = 0.2). Deviating vital signs prior to hospital discharge were frequent but not associated with increased risk of readmission within 30 days. Further exploration of deviating vital signs using continuous monitoring is needed.

Quantifying physiological stability in the general ward using continuous vital signs monitoring: the circadian kernel density estimator.

Technological advances seen in recent years have introduced the possibility of changing the way hospitalized patients are monitored by abolishing the traditional track-and-trigger systems and implementing continuous monitoring using wearable biosensors. However, this new monitoring paradigm raise demand for novel ways of analyzing the data streams in real time. The aim of this study was to design a stability index using kernel density estimation (KDE) fitted to observations of physiological stability incorporating the patients' circadian rhythm. Continuous vital sign data was obtained from two observational studies with 491 postoperative patients and 200 patients with acute exacerbation of chronic obstructive pulmonary disease. We defined physiological stability as the last 24 h prior to discharge. We evaluated the model against periods of eight hours prior to events defined either as severe adverse events (SAE) or as a total score in the early warning score (EWS) protocol of ≥ 6, ≥ 8, or ≥ 10. The results found good discriminative properties between stable physiology and EWS-events (area under the receiver operating characteristics curve (AUROC): 0.772-0.993), but lower for the SAEs (AUROC: 0.594-0.611). The time of early warning for the EWS events were 2.8-5.5 h and 2.5 h for the SAEs. The results showed that for severe deviations in the vital signs, the circadian KDE model can alert multiple hours prior to deviations being noticed by the staff. Furthermore, the model shows good generalizability to another cohort and could be a simple way of continuously assessing patient deterioration in the general ward.

Deviations in continuously monitored electrodermal activity before severe clinical complications: a clinical prospective observational explorative cohort study.

Monitoring of high-risk patients in hospital wards is crucial in identifying and preventing clinical deterioration. Sympathetic nervous system activity measured continuously and non-invasively by Electrodermal activity (EDA) may relate to complications, but the clinical use remains untested. The aim of this study was to explore associations between deviations of EDA and subsequent serious adverse events (SAE). Patients admitted to general wards after major abdominal cancer surgery or with acute exacerbation of chronic obstructive pulmonary disease were continuously EDA-monitored for up to 5 days. We used time-perspectives consisting of 1, 3, 6, and 12 h of data prior to first SAE or from start of monitoring. We constructed 648 different EDA-derived features to assess EDA. The primary outcome was any SAE and secondary outcomes were respiratory, infectious, and cardiovascular SAEs. Associations were evaluated using logistic regressions with adjustment for relevant confounders. We included 714 patients and found a total of 192 statistically significant associations between EDA-derived features and clinical outcomes. 79% of these associations were EDA-derived features of absolute and relative increases in EDA and 14% were EDA-derived features with normalized EDA above a threshold. The highest F1-scores for primary outcome with the four time-perspectives were 20.7-32.8%, with precision ranging 34.9-38.6%, recall 14.7-29.4%, and specificity 83.1-91.4%. We identified statistically significant associations between specific deviations of EDA and subsequent SAE, and patterns of EDA may be developed to be considered indicators of upcoming clinical deterioration in high-risk patients.

Wireless Single-Lead versus Standard 12-Lead ECG, for ST-Segment Deviation during Adenosine Cardiac Stress Scintigraphy.

Wearable wireless electrocardiographic (ECG) monitoring is well-proven for arrythmia detection, but ischemia detection accuracy is not well-described. We aimed to assess the agreement of ST-segment deviation from single- versus 12-lead ECG and their accuracy for the detection of reversible ischemia. Bias and limits of agreement (LoA) were calculated between maximum deviations in ST segments from single- and 12-lead ECG during 82Rb PET-myocardial cardiac stress scintigraphy. Sensitivity and specificity for reversible anterior-lateral myocardial ischemia detection were assessed for both ECG methods, using perfusion imaging results as a reference. Out of 110 patients included, 93 were analyzed. The maximum difference between single- and 12-lead ECG was seen in II (-0.019 mV). The widest LoA was seen in V5, with an upper LoA of 0.145 mV (0.118 to 0.172) and a lower LoA of -0.155 mV (-0.182 to -0.128). Ischemia was seen in 24 patients. Single-lead and 12-lead ECG both had poor accuracy for the detection of reversible anterolateral ischemia during the test: single-lead ECG had a sensitivity of 8.3% (1.0-27.0%) and specificity of 89.9% (80.2-95.8%), and 12-lead ECG a sensitivity of 12.5% (3.0-34.4%) and a specificity of 91.3% (82.0-96.7%). In conclusion, agreement was within predefined acceptable criteria for ST deviations, and both methods had high specificity but poor sensitivity for the detection of anterolateral reversible ischemia. Additional studies must confirm these results and their clinical relevance, especially in the light of the poor sensitivity for detecting reversible anterolateral cardiac ischemia.

Associations between clinical interventions and transcutaneous blood gas values in postoperative patients.

PURPOSE: Postoperative monitoring of circulation and respiration is pivotal to guide intervention strategies and ensure patient outcomes. Transcutaneous blood gas monitoring (TCM) may allow for noninvasive assessment of changes in cardiopulmonary function after surgery, including a more direct assessment of local micro-perfusion and metabolism. To form the basis for studies assessing the clinical impact of TCM complication detection and goal-directed-therapy, we examined the association between clinical interventions in the postoperative period and changes in transcutaneous blood gasses. METHODS: Two-hundred adult patients who have had major surgery were enrolled prospectively and monitored with transcutaneous blood gas measurements (oxygen (TcPO2) and carbon dioxide (TcPCO2)) for 2 h in the post anaesthesia care unit, with recording of all clinical interventions. The primary outcome was changes in TcPO2, secondarily TcPCO2, from 5 min before a clinical intervention versus 5 min after, analysed with paired t-test. RESULTS: Data from 190 patients with 686 interventions were analysed. During clinical interventions, a mean change in TcPO2 of 0.99 mmHg (95% CI-1.79-0.2, p = 0.015) and TcPCO2 of-0.67 mmHg (95% CI 0.36-0.98, p < 0.001) was detected. CONCLUSION: Clinical interventions resulted in significant changes in transcutaneous oxygen and carbon dioxide. These findings suggest future studies to assess the clinical value of changes in transcutaneous PO2 and PCO2 in a postoperative setting. TRIAL REGISTRY: Clinical trial number: NCT04735380. CLINICAL TRIAL REGISTRY: https://clinicaltrials.gov/ct2/show/NCT04735380.

Continuous wireless pre- and postoperative vital sign monitoring reveal new, severe desaturations after vascular surgery.

OBJECTIVES: Postoperative deviating physiologic values (vital signs) may represent postoperative stress or emerging complications. But they can also reflect chronic preoperative values. Distinguishing between the two circumstances may influence the utility of using vital signs in patient monitoring. Thus, we aimed to describe the occurrence of vital sign deviations before and after major vascular surgery, hypothesising that preoperative vital sign deviations were longer in duration postoperatively. METHODS: In this prospective observational study, arterial vascular patients were continuously monitored wirelessly - from the day before until 5 days after surgery. Recorded values were: heart rate, respiration rate, peripheral arterial oxygen saturation (SpO2 ) and blood pressure. The outcomes were 1. cumulative duration of SpO2 < 85% / 24 h, and 2. cumulative duration per 24 h of vital sign deviations. RESULTS: Forty patients were included with a median monitoring time of 21 h preoperatively and 42 h postoperatively. The median duration of SpO2 < 85% preoperatively was 14.4 min/24 h whereas it was 28.0 min/24 h during day 0 in the ward (p = .09), and 16.8 min/24 h on day 1 in the ward (p = 0.61). Cumulative duration of SpO2 < 80% was significantly longer on day 0 in the ward 2.4 min/24 h (IQR 0.0-4.6) versus 6.7 min/24 h (IQR 1.8-16.2) p = 0.01. CONCLUSION: Deviating physiology is common in patients before and after vascular surgery. A longer duration of severe desaturation was found on the first postoperative day in the ward compared to preoperatively, whereas moderate desaturations were reflected in postoperative desaturations. Cumulative duration outside thresholds is, in some cases, exacerbated after surgery.

Forecasting of Continuous Vital Sign Using Multivariate Auto-Regressive Models.

This project assessed the use of multivariate auto-regressive (MAR) models to create forecasts of continuous vital signs in hospitalized patients. A total of 20 hours continuous (1/60Hz) heart rate and respiration rate from eight postoperative patients, where used to fit a centered MAR model for forecasting in windows of 15 minutes. The model was fitted using Markov Chain Monte Carlo sampling, and the model was evaluated on data from five additional patients. The results demonstrate an average RMSE in the forecast window of 11.4 (SD: 7.30) beats per minute for heart rate and 3.3 (SD:1.3) breaths per minute for respiration rate. These results indicate potential for forecasting vital signs in a clinical setting.

Causal inference for planning randomised critical care trials: Protocol for a scoping review.

BACKGROUND: Randomised clinical trials in critical care are prone to inconclusiveness owing, in part, to undue optimism about effect sizes and suboptimal accounting for heterogeneous treatment effects. Planned predictive enrichment based on secondary critical care data (often very rich with respect to both data types and temporal granularity) and causal inference methods may help overcome these challenges, but no overview exists about their use to this end. METHODS: We will conduct a scoping review to assess the extent and nature of the use of causal inference from secondary data for planned predictive enrichment of randomised clinical trials in critical care. We will systematically search 10 general and specialty journals for reports published on or after 1 January 2018, of randomised clinical trials enrolling adult critically ill patients. We will collect trial metadata (e.g., recruitment period and phase) and, when available, information pertaining to the focus of the review (predictive enrichment based on causal inference estimates from secondary data): causal inference methods, estimation techniques and software used; types of patient populations; data provenance, types and models; and the availability of the data (public or not). The results will be reported in a descriptive manner. DISCUSSION: The outlined scoping review aims to assess the use of causal inference methods and secondary data for planned predictive enrichment in randomised critical care trials. This will help guide methodological improvements to increase the utility, and facilitate the use, of causal inference estimates when planning such trials in the future.

Prediction of Serious Adverse Events from Nighttime Vital Signs Values.

The period directly following surgery is critical for patients as they are at risk of infections and other types of complications, often summarized as severe adverse events (SAE). We hypothesize that impending complications might alter the circadian rhythm and, therefore, be detectable during the night before. We propose a SMOTE-enhanced XGBoost prediction model that classifies nighttime vital signs depending on whether they precede a serious adverse event or come from a patient that does not have a complication at all, based on data from 450 postoperative patients. The approach showed respectable results, producing a ROC-AUC score of 0.65 and an accuracy of 0.75. These findings demonstrate the need for further investigation.

Prediction of serious outcomes based on continuous vital sign monitoring of high-risk patients.

Continuous monitoring of high-risk patients and early prediction of severe outcomes is crucial to prevent avoidable deaths. Current clinical monitoring is primarily based on intermittent observation of vital signs and the early warning scores (EWS). The drawback is lack of time series dynamics and correlations among vital signs. This study presents an approach to real-time outcome prediction based on machine learning from continuous recording of vital signs. Systolic blood pressure, diastolic blood pressure, heart rate, pulse rate, respiration rate and peripheral blood oxygen saturation were continuously acquired by wearable devices from 292 post-operative high-risk patients. The outcomes from serious complications were evaluated based on review of patients' medical record. The descriptive statistics of vital signs and patient demographic information were used as features. Four machine learning models K-Nearest-Neighbors (KNN), Decision Trees (DT), Random Forest (RF), and Boosted Ensemble (BE) were trained and tested. In static evaluation, all four models had comparable prediction performance to that of the state of the art. In dynamic evaluation, the models trained from the static evaluation were tested with continuous data. RF and BE obtained the lower false positive rate (FPR) of 0.073 and 0.055 on no-outcome patients respectively. The four models KNN, DT, RF and BE had area under receiver operating characteristic curve (AUROC) of 0.62, 0.64, 0.65 and 0.64 respectively on outcome patients. RF was found to be optimal model with lower FPR on no-outcome patients and a higher AUROC on outcome patients. These findings are encouraging and indicate that additional investigations must focus on validating performance in a clinical setting before deployment of the real-time outcome prediction.

Clinical impact of vital sign abnormalities in patients admitted with acute exacerbation of chronic obstructive pulmonary disease: an observational study using continuous wireless monitoring.

Early detection of abnormal vital signs is critical for timely management of acute hospitalised patients and continuous monitoring may improve this. We aimed to assess the association between preceding vital sign abnormalities and serious adverse events (SAE) in patients hospitalised with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Two hundred patients' vital signs were wirelessly and continuously monitored with peripheral oxygen saturation, heart rate, and respiratory rate during the first 4 days after admission for AECOPD. Non-invasive blood pressure was also measured every 30-60 min. The primary outcome was occurrence of SAE according to international definitions within 30 days and physiological data were analysed for preceding vital sign abnormalities. Data were presented as the mean cumulative duration of vital sign abnormalities per 24 h and analysed using Wilcoxon rank sum test. SAE during ongoing continuous monitoring occurred in 50 patients (25%). Patients suffering SAE during the monitoring period had on average 455 min (SD 413) per 24 h of any preceding vital sign abnormality versus 292 min (SD 246) in patients without SAE, p = 0.08, mean difference 163 min [95% CI 61-265]. Mean duration of bradypnea (respiratory rate < 11 min-1) was 48 min (SD 173) compared with 30 min (SD 84) in patients without SAE, p = 0.01. In conclusion, the duration of physiological abnormalities was substantial in patients with AECOPD. There were no statistically significant differences between patients with and without SAE in the overall duration of preceding physiological abnormalities.Study registration: http://ClinicalTrials.gov (NCT03660501). Date of registration: Sept 6 2018.

Wireless Single-Lead ECG Monitoring to Detect New-Onset Postoperative Atrial Fibrillation in Patients After Major Noncardiac Surgery: A Prospective Observational Study.

BACKGROUND: New-onset postoperative atrial fibrillation (POAF) is associated with several cardiovascular complications and higher mortality. Several pathophysiological processes such as hypoxia can trigger POAF, but these are sparsely elucidated, and POAF is often asymptomatic. In patients undergoing major gastrointestinal cancer surgery, we aimed to describe the frequency of POAF as automatically estimated and detected via wireless repeated sampling monitoring and secondarily to describe the association between preceding vital sign deviations and POAF. METHOD: Patients ≥60 years of age undergoing major gastrointestinal cancer surgery were continuously monitored for up to 4 days postoperatively. Electrocardiograms were obtained every minute throughout the monitoring period. Clinical staff were blinded to all measurements. As for the primary outcome, POAF was defined as 30 consecutive minutes or more detected by a purpose-built computerized algorithm and validated by cardiologists. The primary exposure variable was any episode of peripheral oxygen saturation (Spo2) <85% for >5 consecutive minutes before POAF. RESULTS: A total of 30,145 hours of monitoring was performed in 398 patients, with a median of 92 hours per patient (interquartile range [IQR], 54-96). POAF was detected in 26 patients (6.5%; 95% confidence interval [CI], 4.5-9.4) compared with 14 (3.5%; 95% CI, 1.94-5.83) discovered by clinical staff in the monitoring period. POAF was followed by 9.4 days hospitalization (IQR, 6.5-16) versus 6.5 days (IQR, 2.5-11) in patients without POAF. Preceding episodes of Spo2 <85% for >5 minutes (OR, 1.02; 95% CI, 0.24-4.00; P = .98) or other vital sign deviations were not significantly associated with POAF. CONCLUSIONS: New-onset POAF occurred in 6.5% (95% CI, 4.5-9.4) of patients after major gastrointestinal cancer surgery, and 1 in 3 cases was not detected by the clinical staff (35%; 95% CI, 17-56). POAF was not preceded by vital sign deviations.

Continuous monitoring of vital sign abnormalities; association to clinical complications in 500 postoperative patients.

BACKGROUND: Patients undergoing major surgery are at risk of complications, so-called serious adverse events (SAE). Continuous monitoring may detect deteriorating patients by recording abnormal vital signs. We aimed to assess the association between abnormal vital signs inspired by Early Warning Score thresholds and subsequent SAEs in patients undergoing major abdominal surgery. METHODS: Prospective observational cohort study continuously monitoring heart rate, respiratory rate, peripheral oxygen saturation, and blood pressure for up to 96 h in 500 postoperative patients admitted to the general ward. Exposure variables were vital sign abnormalities, primary outcome was any serious adverse event occurring within 30 postoperative days. The primary analysis investigated the association between exposure variables per 24 h and subsequent serious adverse events. RESULTS: Serious adverse events occurred in 37% of patients, with 38% occurring during monitoring. Among patients with SAE during monitoring, the median duration of vital sign abnormalities was 272 min (IQR 110-447), compared to 259 min (IQR 153-394) in patients with SAE after monitoring and 261 min (IQR 132-468) in the patients without any SAE (p = .62 for all three group comparisons). Episodes of heart rate ≥110 bpm occurred in 16%, 7.1%, and 3.9% of patients in the time before SAE during monitoring, after monitoring, and without SAE, respectively (p < .002). Patients with SAE after monitoring experienced more episodes of hypotension ≤90 mm Hg/24 h (p = .001). CONCLUSION: Overall duration of vital sign abnormalities at current thresholds were not significantly associated with subsequent serious adverse events, but more patients with tachycardia and hypotension had subsequent serious adverse events. TRIAL REGISTRATION: Clinicaltrials.gov, identifier NCT03491137.

The association between vital signs abnormalities during postanaesthesia care unit stay and deterioration in the general ward following major abdominal cancer surgery assessed by continuous wireless monitoring.

Objective: Vital signs abnormalities in the post-anaesthesia care unit (PACU) may identify patients at risk of severe postoperative complications in the general ward, but are sparsely investigated by continuous monitoring. We aimed to assess if the severity of vital signs abnormalities in the PACU was correlated to the duration of severe vital signs abnormalities and serious adverse events (SAEs) in the general ward. Design: Prospective cohort study. Primary exposure was PACU vital signs abnormalities assessed by a standardised PACU recovery score. Participants: Adult patients, aged ≥ 60 years, who underwent major abdominal cancer surgery. Main outcome measures: The duration of severe vital signs abnormalities were assessed by continuous wireless vital signs monitoring and, secondly, by any SAE within the first 96 hours in the general ward. Results: One-hundred patients were included, and 92 patients with a median of 91 hours (interquartile range, 71-95 hours) of vital signs recording were analysed. The maximum vital signs abnormalities in the PACU were not significantly correlated to overall vital signs abnormalities in the general ward (R = 0.13; P = 0.22). Severe circulatory abnormalities in the overall PACU stay and at discharge were significantly correlated to the duration of circulatory vital signs abnormalities on the ward (R = 0.32 [P = 0.00021] and R = 0.26 [P = 0.014], respectively). Seventeen patients (18%) experienced SAEs, without significant association to the PACU stay (area under the receiver operating characteristic [AUROC], 0.59; 95% CI, 0.46-0.73). Conclusion: Vital signs abnormalities in the PACU did not show a tendency towards predicting overall severe vital signs abnormalities or SAEs during the first days in the general ward. Circulatory abnormalities in the PACU showed a tendency towards predicting circulatory complications in the ward.

Prediction of severe adverse event from vital signs for post-operative patients.

Monitoring post-operative patients is important for preventing severe adverse events (SAE), which increases morbidity and mortality. Conventional bedside monitoring system has demonstrated the difficulty in long term monitoring of those patients because majority of them are ambulatory. With development of wearable system and advanced data analytics, those patients would benefit greatly from continuous and predictive monitoring. In this study, we aim to predict SAE based on monitoring of vital signs. Heart rate, respiration rate, and blood oxygen saturation were continuously acquired by wearable devices and blood pressure was measured intermittently from 453 post-operative patients. SAEs from various complications were extracted from patients' database. The trends of vital signs were first extracted with moving average. Then four descriptive statistics were calculated from trend of each modality as features. Finally, a machine learning approach based on support vector machine was employed for prediction of SAE. It has shown the averaged accuracy of 89%, sensitivity of 80%, specificity of 93% and the area under receiver operating characteristic curve (AUROC) of 93%. These findings are promising and demonstrate the feasibility of predicting SAE from vital signs acquired with wearable devices and measured intermittently.

Ethics in extracorporeal life support: a narrative review.

During 50 years of extracorporeal life support (ECLS), this highly invasive technology has left a considerable imprint on modern medicine, and it still confronts researchers, clinicians and policymakers with multifarious ethical challenges. After half a century of academic discussion about the ethics of ECLS, it seems appropriate to review the state of the argument and the trends in it. Through a comprehensive literature search on PubMed, we identified three ethical discourses: (1) trials and evidence accompanying the use of ECLS, (2) ECLS allocation, decision-making and limiting care, and (3) death on ECLS and ECLS in organ donation. All included articles were carefully reviewed, arguments extracted and grouped into the three discourses. This article provides a narrative synthesis of these arguments, evaluates the opportunities for mediation and substantiates the necessity of a shared decision-making approach at the limits of medical care.

[Consequences of sleep deprivation on healthcare workers].

Healthcare workers doing night shifts are at risk of lack of sleep or/and circadian rhythm disturbances. The ability to make complex rational decisions is reduced with sleep deprivation; thus, one should try to take the proper precautions. This can be done by reducing the complexity and decision speed as much as possible at nights. Furthermore, as suggested in this review, several individual and organisational measures can reduce the risk of circadian rhythm disorders and make the body ready for a new shift more quickly. Driving motor vehicles should be avoided after night shifts with insufficient sleep.