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Abstract The COVID-19 pandemic has posed a major challenge to healthcare systems globally. Millions of people have been infected, and millions of deaths have been reported worldwide. Glucocorticoids have attracted worldwide attention for their potential efficacy in the treatment of COVID-19. Various glucocorticoids with different dosages and treatment durations have been studied in patients with different severities, with a suitable dosage and treatment duration not yet defined. This study aimed to investigate whether in-hospital survival differs between critically ill patients treated with low-dose glucocorticoids, high-dose glucocorticoids or no glucocorticoids. All critically ill patients admitted to the intensive care unit of the Charité Hospital—Universitätsmedizin Berlin between February 2020 and December 2021 with COVID-19 pneumonia receiving supplemental oxygen were eligible to participate in this multicenter real-world data study. Patients were retrospectively assigned to one of three groups: the high corticosteroid dose (HighC) group (receiving 6 mg parenteral dexamethasone or an equivalent corticosteroid dosage for ten days), the low corticosteroid dose (LowC) group (receiving less than 6 mg parenteral dexamethasone or an equivalent corticosteroid dosage for ten days), or the no corticosteroid (NoC) group. Overall survival and risk effects were compared among groups within the total observation period, as well as at 35 days after the onset of COVID-19 symptoms. Adjusted multivariable Cox proportional hazard regression analysis was performed to compare the risk of death between the treatment groups. Out of 1561 critically ill COVID-19 patients, 1014 were included in the baseline analysis. In the survival study, 1009 patients were assigned to the NoC (n = 346), HighC (n = 552), or LowC group (n = 111). The baseline characteristics were balanced between groups, except for age, BMI, APACHE II score, SOFA and SAPS II. While the 35-day survival did not show any differences, a landmark analysis of the patients surviving beyond 35 days revealed differences between groups. The restricted mean survival time was 112 days in the LowC group [95% CI: 97 – 128], 133 days in the HighC group [95% CI: 124 – 141] and 144 days in the NoC group [95% CI: 121 – 167]. The multivariable-adjusted Cox proportional hazard analysis indicated that, regardless of age, sex, health status or invasive oxygenation, a low-dose treatment increased the hazard of death of critically ill COVID-19 patients by a factor of 2.09 ([95% CI: 0.99, 4.4], p = 0.05) and a high-dose corticosteroid treatment increased the risk by a factor of 1.07 ([95% CI: 0.53, 2.15], p = 0.85) compared to no treatment with glucocorticoids. The analysis reveals that corticosteroid treatment does not influence the survival of critically ill COVID-19 patients in the intensive care unit within 35 days. Our evaluations further suggest that regardless of ventilation status, the decision-making process for administering corticosteroid therapy should account for the individual severity of the illness.

BackgroundRemote patient management may improve prognosis in heart failure. Daily review of transmitted data for early recognition of patients at risk requires substantial resources that represent a major barrier to wide implementation. An automated analysis of incoming data for detection of risk for imminent events would allow focusing on patients requiring prompt medical intervention.MethodsWe analysed data of the Telemedical Interventional Management in Heart Failure II (TIM-HF2) randomized trial that were collected during quarterly in-patient visits and daily transmissions from non-invasive monitoring devices. By application of machine learning, we developed and internally validated a risk score for heart failure hospitalisation within seven days following data transmission as estimate of short-term patient risk for adverse heart failure events. Score performance was assessed by the area under the receiver-operating characteristic (ROCAUC) and compared with a conventional algorithm, a heuristic rule set originally applied in the randomized trial.ResultsThe machine learning model significantly outperformed the conventional algorithm (ROCAUC 0.855 vs. 0.727, p

BackgroundIn response to the high patient admission rates during the COVID-19 pandemic, provisional intensive care units (ICUs) were set up, equipped with temporary monitoring and alarm systems. We sought to find out whether the provisional ICU setting led to a higher alarm burden and more staff with alarm fatigue. ObjectiveWe aimed to compare alarm situations between provisional COVID-19 ICUs and non–COVID-19 ICUs during the second COVID-19 wave in Berlin, Germany. The study focused on measuring alarms per bed per day, identifying medical devices with higher alarm frequencies in COVID-19 settings, evaluating the median duration of alarms in both types of ICUs, and assessing the level of alarm fatigue experienced by health care staff. MethodsOur approach involved a comparative analysis of alarm data from 2 provisional COVID-19 ICUs and 2 standard non–COVID-19 ICUs. Through interviews with medical experts, we formulated hypotheses about potential differences in alarm load, alarm duration, alarm types, and staff alarm fatigue between the 2 ICU types. We analyzed alarm log data from the patient monitoring systems of all 4 ICUs to inferentially assess the differences. In addition, we assessed staff alarm fatigue with a questionnaire, aiming to comprehensively understand the impact of the alarm situation on health care personnel. ResultsCOVID-19 ICUs had significantly more alarms per bed per day than non–COVID-19 ICUs (P

Postoperative delirium (POD) contributes to severe outcomes such as death or development of dementia. Thus, it is desirable to identify vulnerable patients in advance during the perioperative phase. Previous studies mainly investigated risk factors for delirium during hospitalization and further used a linear logistic regression (LR) approach with time-invariant data. Studies have not investigated patients' fluctuating conditions to support POD precautions. In this single-center study, we aimed to predict POD in a recovery room setting with a non-linear machine learning (ML) technique using pre-, intra-, and postoperative data. The target variable POD was defined with the Nursing Screening Delirium Scale (Nu-DESC) ≥ 1. Feature selection was conducted based on robust univariate test statistics and L1 regularization. Non-linear multi-layer perceptron (MLP) as well as tree-based models were trained and evaluated-with the receiver operating characteristics curve (AUROC), the area under precision recall curve (AUPRC), and additional metrics-against LR and published models on bootstrapped testing data. The prevalence of POD was 8.2% in a sample of 73,181 surgeries performed between 2017 and 2020. Significant univariate impact factors were the preoperative ASA status (American Society of Anesthesiologists physical status classification system), the intraoperative amount of given remifentanil, and the postoperative Aldrete score. The best model used pre-, intra-, and postoperative data. The non-linear boosted trees model achieved a mean AUROC of 0.854 and a mean AUPRC of 0.418 outperforming linear LR, well as best applied and retrained baseline models. Overall, non-linear machine learning models using data from multiple perioperative time phases were superior to traditional ones in predicting POD in the recovery room. Class imbalance was seen as a main impediment for model application in clinical practice.

Abstract Background Chronic heart failure (HF) is a common clinical condition associated with adverse outcomes in elderly patients undergoing non-cardiac surgery. This study aimed to estimate a clinically applicable NT-proBNP cut-off that predicts postoperative 30-day morbidity in a non-cardiac surgical cohort. Methods One hundred ninety-nine consecutive patients older than 65 years undergoing elective non-cardiac surgery with intermediate or high surgical risk were analysed. Preoperative NT-proBNP was measured, and clinical events were assessed up to postoperative day 30. The primary endpoint was the composite morbidity endpoint (CME) consisting of rehospitalisation, acute decompensated heart failure (ADHF), acute kidney injury (AKI), and infection at postoperative day 30. Secondary endpoints included perioperative fluid balance and incidence, duration, and severity of perioperative hypotension. Results NT-proBNP of 443 pg/ml had the highest accuracy in predicting the composite endpoint; a clinical cut-off of 450 pg/ml was implemented to compare clinical endpoints. Although 35.2% of patients had NT-proBNP above the threshold, only 10.6% had a known history of HF. The primary endpoint was the composite morbidity endpoint (CME) consisting of rehospitalisation, acute decompensated heart failure (ADHF), acute kidney injury (AKI), and infection. Event rates were significantly increased in patients with NT-proBNP > 450 pg/ml (70.7% vs. 32.4%, p 450 pg/ml was an independent predictor of the CME in a multivariable Cox regression model (hazard ratio 2.92 [1.72–4.94]). Conclusions Patients with NT-proBNP > 450 pg/ml exhibited profoundly increased postoperative morbidity. Further studies should focus on interdisciplinary approaches to improve outcomes through integrated interventions in the perioperative period. Trial registration German Clinical Trials Register: DRKS00027871, 17/01/2022

Abstract Background Obesity is a known cardiovascular risk factor and associated with higher postoperative complication rates in patients undergoing cardiac surgery. In heart failure (HF), conflicting evidence in terms of survival has been reported, whereas sarcopenia is associated with poor prognosis. An increasing number of HF patients require left ventricular assist device (LVAD) implantations. The postoperative mortality has improved in recent years but is still relatively high. The impact of body composition on outcome in this population remains unclear. The aim of this investigation was to examine the preoperative computed tomography (CT) body composition as a predictor of the postoperative outcome in advanced HF patients, who receive LVAD implantations. Methods Preoperative CT scans of 137 patients who received LVADs between 2015 and 2020 were retrospectively analysed using an artificial intelligence (AI)‐powered automated software tool based on a convolutional neural network, U‐net, developed for image segmentation (Visage Version 7.1, Visage Imaging GmbH, Berlin, Germany). Assessment of body composition included visceral and subcutaneous adipose tissue areas (VAT and SAT), psoas and total abdominal muscle areas and sarcopenia (defined by lumbar skeletal muscle indexes). The body composition parameters were correlated with postoperative major complication rates, survival and postoperative 6‐min walk distance (6MWD) and quality of life (QoL). Results The mean age of patients was 58.21 ± 11.9 years; 122 (89.1%) were male. Most patients had severe HF requiring inotropes (Interagency Registry for Mechanically Assisted Circulatory Support [INTERMACS] profile I–III, 71.9%) secondary to coronary artery diseases or dilated cardiomyopathy (96.4%). Forty‐four (32.1%) patients were obese (body mass index ≥ 30 kg/m2), 96 (70.1%) were sarcopene and 19 (13.9%) were sarcopene obese. Adipose tissue was associated with a significantly higher risk of postoperative infections (VAT 172.23 cm2 [54.96, 288.32 cm2] vs. 124.04 cm2 [56.57, 186.25 cm2], P = 0.022) and in‐hospital mortality (VAT 168.11 cm2 [134.19, 285.27 cm2] vs. 135.42 cm2 [49.44, 227.91 cm2], P = 0.033; SAT 227.28 cm2 [139.38, 304.35 cm2] vs. 173.81 cm2 [97.65, 254.16 cm2], P = 0.009). Obese patients showed no improvement of 6MWD and QoL within 6 months postoperatively (obese: +0.94 ± 161.44 months, P = 0.982; non‐obese: +166.90 ± 139.00 months, P

Abstract Real‐world evidence (RWE) trials have a key advantage over conventional randomized controlled trials (RCTs) due to their potentially better generalizability. High generalizability of study results facilitates new biological insights and enables targeted therapeutic strategies. Random sampling of RWE trial participants is regarded as the gold standard for generalizability. Additionally, the use of sample correction procedures can increase the generalizability of trial results, even when using nonrandomly sampled real‐world data (RWD). This study presents descriptive evidence on the extent to which the design of currently planned or already conducted RWE trials takes sampling into account. It also examines whether random sampling or procedures for correcting nonrandom samples are considered. Based on text mining of publicly available metadata provided during registrations of RWE trials on clinicaltrials.gov, EU‐PAS, and the OSF‐RWE registry, it is shown that the share of RWE trial registrations with information on sampling increased from 65.27% in 2002 to 97.43% in 2022, with a corresponding increase from 14.79% to 28.30% for trials with random samples. For RWE trials with nonrandom samples, there is an increase from 0.00% to 0.95% of trials in which sample correction procedures are used. We conclude that the potential benefits of RWD in terms of generalizing trial results are not yet being fully realized.

BackgroundThe high number of unnecessary alarms in intensive care settings leads to alarm fatigue among staff and threatens patient safety. To develop and implement effective and sustainable solutions for alarm management in intensive care units (ICUs), an understanding of staff interactions with the patient monitoring system and alarm management practices is essential. ObjectiveThis study investigated the interaction of nurses and physicians with the patient monitoring system, their perceptions of alarm management, and smart alarm management solutions. MethodsThis explorative qualitative study with an ethnographic, multimethods approach was conducted in an ICU of a German university hospital. Using triangulation in data collection, 102 hours of field observations, 12 semistructured interviews with ICU staff members, and the results of a participatory task were analyzed. The data analysis followed an inductive, grounded theory approach. ResultsNurses and physicians reported interacting with the continuous vital sign monitoring system for most of their work time and tasks. There were no established standards for alarm management; instead, nurses and physicians stated that alarms were addressed through ad hoc reactions, a practice they viewed as problematic. Staff members’ perceptions of intelligent alarm management varied, but they highlighted the importance of understandable and traceable suggestions to increase trust and cognitive ease. ConclusionsStaff members’ interactions with the omnipresent patient monitoring system and its alarms are essential parts of ICU workflows and clinical decision-making. Alarm management standards and workflows have been shown to be deficient. Our observations, as well as staff feedback, suggest that changes are warranted. Solutions for alarm management should be designed and implemented with users, workflows, and real-world data at the core.

BackgroundThe COVID-19 pandemic posed significant challenges to global health systems. Efficient public health responses required a rapid and secure collection of health data to improve the understanding of SARS-CoV-2 and examine the vaccine effectiveness (VE) and drug safety of the novel COVID-19 vaccines. ObjectiveThis study (COVID-19 study on vaccinated and unvaccinated subjects over 16 years; eCOV study) aims to (1) evaluate the real-world effectiveness of COVID-19 vaccines through a digital participatory surveillance tool and (2) assess the potential of self-reported data for monitoring key parameters of the COVID-19 pandemic in Germany. MethodsUsing a digital study web application, we collected self-reported data between May 1, 2021, and August 1, 2022, to assess VE, test positivity rates, COVID-19 incidence rates, and adverse events after COVID-19 vaccination. Our primary outcome measure was the VE of SARS-CoV-2 vaccines against laboratory-confirmed SARS-CoV-2 infection. The secondary outcome measures included VE against hospitalization and across different SARS-CoV-2 variants, adverse events after vaccination, and symptoms during infection. Logistic regression models adjusted for confounders were used to estimate VE 4 to 48 weeks after the primary vaccination series and after third-dose vaccination. Unvaccinated participants were compared with age- and gender-matched participants who had received 2 doses of BNT162b2 (Pfizer-BioNTech) and those who had received 3 doses of BNT162b2 and were not infected before the last vaccination. To assess the potential of self-reported digital data, the data were compared with official data from public health authorities. ResultsWe enrolled 10,077 participants (aged ≥16 y) who contributed 44,786 tests and 5530 symptoms. In this young, primarily female, and digital-literate cohort, VE against infections of any severity waned from 91.2% (95% CI 70.4%-97.4%) at week 4 to 37.2% (95% CI 23.5%-48.5%) at week 48 after the second dose of BNT162b2. A third dose of BNT162b2 increased VE to 67.6% (95% CI 50.3%-78.8%) after 4 weeks. The low number of reported hospitalizations limited our ability to calculate VE against hospitalization. Adverse events after vaccination were consistent with previously published research. Seven-day incidences and test positivity rates reflected the course of the pandemic in Germany when compared with official numbers from the national infectious disease surveillance system. ConclusionsOur data indicate that COVID-19 vaccinations are safe and effective, and third-dose vaccinations partially restore protection against SARS-CoV-2 infection. The study showcased the successful use of a digital study web application for COVID-19 surveillance and continuous monitoring of VE in Germany, highlighting its potential to accelerate public health decision-making. Addressing biases in digital data collection is vital to ensure the accuracy and reliability of digital solutions as public health tools.

BackgroundThe question of the utility of face masks in preventing acute respiratory infections has received renewed attention during the COVID-19 pandemic. However, given the inconclusive evidence from existing randomized controlled trials, evidence based on real-world data with high external validity is missing. ObjectiveTo add real-world evidence, this study aims to examine whether mask mandates in 51 countries and mask recommendations in 10 countries increased self-reported face mask use and reduced SARS-CoV-2 reproduction numbers and COVID-19 case growth rates. MethodsWe applied an event study approach to data pooled from four sources: (1) country-level information on self-reported mask use was obtained from the COVID-19 Trends and Impact Survey, (2) data from the Oxford COVID-19 Government Response Tracker provided information on face mask mandates and recommendations and any other nonpharmacological interventions implemented, (3) mobility indicators from Google’s Community Mobility Reports were also included, and (4) SARS-CoV-2 reproduction numbers and COVID-19 case growth rates were retrieved from the Our World in Data—COVID-19 data set. ResultsMandates increased mask use by 8.81 percentage points (P=.006) on average, and SARS-CoV-2 reproduction numbers declined on average by −0.31 units (P=.008). Although no significant average effect of mask mandates was observed for growth rates of COVID-19 cases (−0.98 percentage points; P=.56), the results indicate incremental effects on days 26 (−1.76 percentage points; P=.04), 27 (−1.89 percentage points; P=.05), 29 (−1.78 percentage points; P=.04), and 30 (−2.14 percentage points; P=.02) after mandate implementation. For self-reported face mask use and reproduction numbers, incremental effects are seen 6 and 13 days after mandate implementation. Both incremental effects persist for >30 days. Furthermore, mask recommendations increased self-reported mask use on average (5.84 percentage points; P

Abstract BackgroundThe severity of neonatal abstinence syndrome (NAS) may be assessed with the Finnegan score (FS). Since the FS is laborious and subjective, alternative ways of assessment may improve quality of care. ObjectiveIn this pilot study, we examined associations between the FS and routine monitoring data obtained from the electronic health record system. MethodsThe study included 205 neonates with NAS after intrauterine (n=23) or postnatal opioid exposure (n=182). Routine monitoring data were analyzed at 60±10 minutes (t–1) and 120±10 minutes (t–2) before each FS assessment. Within each time period, the mean for each variable was calculated. Readings were also normalized to individual baseline data for each patient and parameter. Mixed effects models were used to assess the effect of different variables. ResultsPlots of vital parameters against the FS showed heavily scattered data. When controlling for several variables, the best-performing mixed effects model displayed significant effects of individual baseline-controlled mean heart rate (estimate 0.04, 95% CI 0.02‐0.07) and arterial blood pressure (estimate 0.05, 95% CI 0.01‐0.08) at t–1 with a goodness of fit (R2m ConclusionsRoutine electronic data can be extracted and analyzed for their correlation with FS data. Mixed effects models show small but significant effects after normalizing vital parameters to individual baselines.

Abstract When exposed to hundreds of medical device alarms per day, intensive care unit (ICU) staff can develop “alarm fatigue” (i.e., desensitisation to alarms). However, no standardised way of quantifying alarm fatigue exists. We aimed to develop a brief questionnaire for measuring alarm fatigue in nurses and physicians. After developing a list of initial items based on a literature review, we conducted 15 cognitive interviews with the target group (13 nurses and two physicians) to ensure that the items are face valid and comprehensible. We then asked 32 experts on alarm fatigue to judge whether the items are suited for measuring alarm fatigue. The resulting 27 items were sent to nurses and physicians from 15 ICUs of a large German hospital. We used exploratory factor analysis to further reduce the number of items and to identify scales. A total of 585 submissions from 707 participants could be analysed (of which 14% were physicians and 64% were nurses). The simple structure of a two-factor model was achieved within three rounds. The final questionnaire (called Charité Alarm Fatigue Questionnaire; CAFQa) consists of nine items along two scales (i.e., the “alarm stress scale” and the “alarm coping scale”). The CAFQa is a brief questionnaire that allows clinical alarm researchers to quantify the alarm fatigue of nurses and physicians. It should not take more than five minutes to administer.

Abstract Background High rates of clinical alarms in the intensive care unit can result in alarm fatigue among staff. Individualization of alarm thresholds is regarded as one measure to reduce non-actionable alarms. The aim of this study was to investigate staff’s perceptions of alarm threshold individualization according to patient characteristics and disease status. Methods This is a cross-sectional survey study (February-July 2020). Intensive care nurses and physicians were sampled by convenience. Data was collected using an online questionnaire. Results Staff view the individualization of alarm thresholds in the monitoring of vital signs as important. The extent to which alarm thresholds are adapted from the normal range varies depending on the vital sign monitored, the reason for clinical deterioration, and the professional group asked. Vital signs used for hemodynamic monitoring (heart rate and blood pressure) were most subject to alarm individualizations. Staff are ambivalent regarding the integration of novel technological features into alarm management. Conclusions All relevant stakeholders, including clinicians, hospital management, and industry, must collaborate to establish a “standard for individualization,” moving away from ad hoc alarm management to an intelligent, data-driven alarm management. Making alarms meaningful and trustworthy again has the potential to mitigate alarm fatigue – a major cause of stress in clinical staff and considerable hazard to patient safety. Trial registration The study was registered at ClinicalTrials.gov (NCT03514173) on 02/05/2018.

BackgroundManagement of sedation, analgesia, and delirium influences morbidity, mortality, and quality of life in patients treated in intensive care. Assessing quality indicators as part of a quality management and assurance program is an established method to ensure process quality. Currently, there is limited research on the effect of evaluating quality indicators on economic outcomes. The aim of the study was to investigate the adherence to an indicator on management of sedation, analgesia and delirium, and explore potential effects on hospital economics and clinical outcomes.MethodsIn this retrospective cohort study, we analyzed routine data from 20,220 patient records from the hospital information system of a tertiary university hospital, collected from January 2012 to December 2019. We compared two predefined subgroups with either high indicator adherence or low indicator adherence regarding factors like disease severity scores, comorbidities, and outcome measures. We used logistic regression models to examine the influence of quality indicator adherence on economic measures such as Diagnosis-related group (DRG) incomes, revenue margins, and costs, and clinical outcomes. Additionally, we used propensity score matching to probe our findings.ResultsOverall revenue margins in this cohort were negative (-320€). High adherence to the quality indicator was associated with a positive revenue margin (+197€) compared to low adherence (-482€). Higher adherence was also associated with lower costs. Additionally, high adherence was associated with reduced mortality (OR 0.84, 95% CI 0.75-0.95) and reduced duration of mechanical ventilation and hospital stay (17 hours and 1 day respectively).ConclusionHigher adherence to a quality indicator for sedation, analgesia, and delirium management was associated with economic returns and costs. We also found an association with lower mortality and reduced length of stay. Further research on these associations may help identify opportunities for quality improvement without increased resource use.

BackgroundThe outcome of patients undergoing major surgery treated with HES for hemodynamic optimization is unclear. This post-hoc analysis of a randomized clinical pilot trial investigated the impact of low-molecular balanced HES solutions on the coagulation system, blood loss and transfusion requirements.MethodsThe Trial was registered: EudraCT 2008-004175-22 and ethical approval was provided by the ethics committee of Berlin. Patients were randomized into three groups receiving either a 10% HES 130/0.42 solution, a 6% HES 130/0.42 solution or a crystalloid following a goal-directed hemodynamic algorithm. Endpoints were parameters of standard and viscoelastic coagulation laboratory, blood loss and transfusion requirements at baseline, at the end of surgery (EOS) and the first postoperative day (POD 1).ResultsFifty-two patients were included in the analysis (HES 10% (n = 15), HES 6% (n = 17) and crystalloid (n = 20)). Fibrinogen decreased in all groups at EOS (HES 10% 338 [298;378] to 192 [163;234] mg dl-1, pConclusionOur data suggest a stronger but transient effect of balanced, low-molecular HES on the coagulation system. Despite the decline of the use of artificial colloids in clinical practice, these results may help to inform clinicians who use HES solutions.

Objective The goal of this research was to demonstrate the efficacy of telemedicine via design, implementation and evaluation of a web-based remote patient monitoring system (WB-RPMS) across the tertiary/university teaching hospitals in a developing country Nigeria, as a tool to continue to expand access to an affordable and resilient tertiary healthcare system through the challenging times of the COVID-19 pandemic or any future disruptions. Methods This research employed an agile and human-centred design thinking philosophy, which saw the researchers iteratively collaborate with clinicians across the system development value chain. It also employed qualitative and quantitative research methods for new system evaluations. After the system's development, a 20-patient sample was randomly selected from members of the National Youth Service Corp to evaluate the WB-RPMS Patient Portal for usability and user experience through a survey based on the system usability scale. Again, the COREQ standards for reporting research result were adopted for this study. Results The evaluation of the WB-RPMS Patient Portal by a select patient sample showed that 95.0% of the respondents believed that they would like to use the system frequently. It was also discovered that 90.0% of all respondents also indicated that they found the Patient Portal to be simple; 85.0% of the respondents believed and indicated that the WB-RPMS Patient Portal was easy to use. Conclusions The result of the usability evaluation of the developed WB-RPMS Patient Portal showed that it was well received by the select patient sample and by the clinicians who participated in the development process. In fact, the performance of the system shows that it has the potential to remotely support and sustain improved access to affordable healthcare for outpatients in developing countries even during times of uncertainties and disruptions as recently occasioned by COVID-19 pandemic.

Abstract Background Hemolysis is associated with increased mortality in patients with sepsis, ARDS, or therapy with extracorporeal membrane oxygenation (ECMO). To quantify a critical threshold of hemolysis in patients with ARDS and treatment with veno-venous ECMO, we aimed to identify cutoff values for cell-free hemoglobin (CFH) and haptoglobin (Hp) plasma concentrations associated with a significant increase in ICU mortality. Methods Patients with ARDS admitted to a tertiary ARDS referral center between 01/2007 and 12/2018 and treatment with veno-venous ECMO were included. Cutoff values for mean CFH (mCFH) and mean Hp (mHp) plasma concentrations dividing the cohort into groups with significantly different ICU mortalities were calculated and patient characteristics were compared. A multiple logistic regression model with stepwise backward variable selection was included. In addition, cutoff values for vulnerable relative timespans for the respective CFH and Hp concentrations were calculated. Results A quantitative cutoff value of 11 mg/dl for mCFH separated the cohort (n = 442) regarding ICU mortality (mCFH ≤ 11 mg/dl: 38%, [95%-CI: 32.22–43.93] (n = 277) vs. mCFH > 11 mg/dl: 70%, [61.99–76.47] (n = 165), p 0.39 g/l: 38.7%, [33.01–44.72] (n = 279), p 11 mg/dl: 33%; [26.81–40.54] (n = 192) vs. > 13.3% of days with CFH > 11 mg/dl: 62%; [56.05–68.36] (n = 250), p 18.2% of therapy days (≤ 18.2% days with Hp ≤ 0.39 g/l: 27%; [19.80–35.14] (n = 138) vs. > 18.2% days with Hp ≤ 0.39 g/l: 60%; [54.43–65.70] (n = 304), p 11 mg/dl. In addition, also Hp plasma concentrations need consideration when the injurious effect of elevated CFH is evaluated.

Abstract Intensive care units (ICU) are often overflooded with alarms from monitoring devices which constitutes a hazard to both staff and patients. To date, the suggested solutions to excessive monitoring alarms have remained on a research level. We aimed to identify patient characteristics that affect the ICU alarm rate with the goal of proposing a straightforward solution that can easily be implemented in ICUs. Alarm logs from eight adult ICUs of a tertiary care university-hospital in Berlin, Germany were retrospectively collected between September 2019 and March 2021. Adult patients admitted to the ICU with at least 24 h of continuous alarm logs were included in the study. The sum of alarms per patient per day was calculated. The median was 119. A total of 26,890 observations from 3205 patients were included. 23 variables were extracted from patients' electronic health records (EHR) and a multivariable logistic regression was performed to evaluate the association of patient characteristics and alarm rates. Invasive blood pressure monitoring (adjusted odds ratio (aOR) 4.68, 95%CI 4.15–5.29, p

Abstract Background Pleural effusions commonly occur in patients recovering from cardiac surgery; however, the impact on outcomes is not well characterized. The purpose of this study is to characterize the clinical outcomes of cardiac surgery patients with pleural effusion. Methods All patients undergoing cardiac surgery between 2006 and 2019 at a tertiary care university hospital were included in this observational, cross-sectional analysis using propensity matching. Results Of 11,037 patients that underwent cardiac surgery during the study period, 6461 (58.5%) had no pleural effusion (Group 0), 3322 (30.1%) had pleural effusion only (Group 1), and 1254 (11.4%) required at least one secondary drainage procedure after the index operation (Group 2). After propensity matching, the mortality of patients who underwent secondary drainage procedures was 6.1% higher than in Group 1 (p

Abstract Background Mobilisation and exercise intervention in general are safe and feasible in critically ill patients. For patients requiring catecholamines, however, doses of norepinephrine safe for mobilisation in the intensive care unit (ICU) are not defined. This study aimed to describe mobilisation practice in our hospital and identify doses of norepinephrine that allowed a safe mobilisation. Methods We conducted a retrospective single-centre cohort study of 16 ICUs at a university hospital in Germany with patients admitted between March 2018 and November 2021. Data were collected from our patient data management system. We analysed the effect of norepinephrine on level (ICU Mobility Scale) and frequency (units per day) of mobilisation, early mobilisation (within 72 h of ICU admission), mortality, and rate of adverse events. Data were extracted from free-text mobilisation entries using supervised machine learning (support vector machine). Statistical analyses were done using (generalised) linear (mixed-effect) models, as well as chi-square tests and ANOVAs. Results A total of 12,462 patients were analysed in this study. They received a total of 59,415 mobilisation units. Of these patients, 842 (6.8%) received mobilisation under continuous norepinephrine administration. Norepinephrine administration was negatively associated with the frequency of mobilisation (adjusted difference -0.07 mobilisations per day; 95% CI − 0.09, − 0.05; p ≤ 0.001) and early mobilisation (adjusted OR 0.83; 95% CI 0.76, 0.90; p ≤ 0.001), while a higher norepinephrine dose corresponded to a lower chance to be mobilised out-of-bed (adjusted OR 0.01; 95% CI 0.00, 0.04; p ≤ 0.001). Mobilisation with norepinephrine did not significantly affect mortality (p > 0.1). Higher compared to lower doses of norepinephrine did not lead to a significant increase in adverse events in our practice (p > 0.1). We identified that mobilisation was safe with up to 0.20 µg/kg/min norepinephrine for out-of-bed (IMS ≥ 2) and 0.33 µg/kg/min for in-bed (IMS 0–1) mobilisation. Conclusions Mobilisation with norepinephrine can be done safely when considering the status of the patient and safety guidelines. We demonstrated that safe mobilisation was possible with norepinephrine doses up to 0.20 µg/kg/min for out-of-bed (IMS ≥ 2) and 0.33 µg/kg/min for in-bed (IMS 0–1) mobilisation.

Abstract Background Due to the increasing use of online health information, symptom checkers have been developed to provide an individualized assessment of health complaints and provide potential diagnoses and an urgency estimation. It is assumed that they support patient empowerment and have a positive impact on patient-physician interaction and satisfaction with care. Particularly in the emergency department (ED), symptom checkers could be integrated to bridge waiting times in the ED, and patients as well as physicians could take advantage of potential positive effects. Our study therefore aims to assess the impact of symptom assessment application (SAA) usage compared to no SAA usage on the patient-physician interaction in self-referred walk-in patients in the ED population. Methods In this multi-center, 1:1 randomized, controlled, parallel-group superiority trial, 440 self-referred adult walk-in patients with a non-urgent triage category will be recruited in three EDs in Berlin. Eligible participants in the intervention group will use a SAA directly after initial triage. The control group receives standard care without using a SAA. The primary endpoint is patients’ satisfaction with the patient-physician interaction assessed by the Patient Satisfaction Questionnaire. Discussion The results of this trial could influence the implementation of SAA into acute care to improve the satisfaction with the patient-physician interaction. Trial registration German Clinical Trials Registry DRKS00028598 . Registered on 25.03.2022

PurposeThough a subgroup analysis has shown improved survival for patients suffering severely reduced ventricular function undergoing coronary artery bypass grafting, RCTs were not able to demonstrate overall beneficial effects of perioperative Levosimendan in cardiac surgery. This might be due to Levosimendan’s pharmacokinetics reaching a steady-state concentration only 4–8 h after administration. Thus, this study now analysed the influence of timing of Levosimendan administration on perioperative outcome in cardiac surgery patients preoperatively presenting with severely reduced ventricular function and therefore considered at high-risk for intra- or postoperative low cardiac output syndrome. We hypothesized that prolonged preoperative Levosimendan administration (“preconditioning”) would reduce mortality.MethodsAll adult patients undergoing cardiac surgery between 2006 and 2018 perioperatively receiving Levosimendan were included in this retrospective, observational cohort study (n = 498). Patients were stratified into 3 groups: Levosimendan started on the day prior to surgery (“preop”), Levosimendan started on the day of surgery (“intraop”) or post ICU admission (“postop”). After propensity score matching (PSM) was performed, outcomes defined according to proposed standard definitions for perioperative outcome research were compared between groups.ResultsAfter PSM, there were no significant differences in patients’ characteristics, comorbidities and type/priority of surgery between groups. Compared to intraop or postop Levosimendan treatment, preop treated patients had significantly lower in-hospital-mortality (preop vs. intraop. vs. postop = 16,7% vs. 33,3% vs. 42,3%), duration of mechanical ventilation and rate of continuous renal replacement therapy.ConclusionsProlonged preoperative treatment with Levosimendan of cardiac surgery patients preoperatively presenting with severely reduced left ventricular function might be beneficial in terms of postoperative outcome. Our results are in line with recent experts’ recommendations concerning the prolonged perioperative use of Levosimendan. We strongly recommend that future randomized trials include this “preconditioning” treatment as an experimental arm.

BackgroundPrevious studies have revealed that users of symptom checkers (SCs, apps that support self-diagnosis and self-triage) are predominantly female, are younger than average, and have higher levels of formal education. Little data are available for Germany, and no study has so far compared usage patterns with people’s awareness of SCs and the perception of usefulness. ObjectiveWe explored the sociodemographic and individual characteristics that are associated with the awareness, usage, and perceived usefulness of SCs in the German population. MethodsWe conducted a cross-sectional online survey among 1084 German residents in July 2022 regarding personal characteristics and people’s awareness and usage of SCs. Using random sampling from a commercial panel, we collected participant responses stratified by gender, state of residence, income, and age to reflect the German population. We analyzed the collected data exploratively. ResultsOf all respondents, 16.3% (177/1084) were aware of SCs and 6.5% (71/1084) had used them before. Those aware of SCs were younger (mean 38.8, SD 14.6 years, vs mean 48.3, SD 15.7 years), were more often female (107/177, 60.5%, vs 453/907, 49.9%), and had higher formal education levels (eg, 72/177, 40.7%, vs 238/907, 26.2%, with a university/college degree) than those unaware. The same observation applied to users compared to nonusers. It disappeared, however, when comparing users to nonusers who were aware of SCs. Among users, 40.8% (29/71) considered these tools useful. Those considering them useful reported higher self-efficacy (mean 4.21, SD 0.66, vs mean 3.63, SD 0.81, on a scale of 1-5) and a higher net household income (mean EUR 2591.63, SD EUR 1103.96 [mean US $2798.96, SD US $1192.28], vs mean EUR 1626.60, SD EUR 649.05 [mean US $1756.73, SD US $700.97]) than those who considered them not useful. More women considered SCs unhelpful (13/44, 29.5%) compared to men (4/26, 15.4%). ConclusionsConcurring with studies from other countries, our findings show associations between sociodemographic characteristics and SC usage in a German sample: users were on average younger, of higher socioeconomic status, and more commonly female compared to nonusers. However, usage cannot be explained by sociodemographic differences alone. It rather seems that sociodemographics explain who is or is not aware of the technology, but those who are aware of SCs are equally likely to use them, independently of sociodemographic differences. Although in some groups (eg, people with anxiety disorder), more participants reported to know and use SCs, they tended to perceive them as less useful. In other groups (eg, male participants), fewer respondents were aware of SCs, but those who used them perceived them to be more useful. Thus, SCs should be designed to fit specific user needs, and strategies should be developed to help reach individuals who could benefit but are not aware of SCs yet.

BackgroundIncreasing digitalization in the medical domain gives rise to large amounts of health care data, which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data and AI oftentimes cannot unlock their full potential at scale, owing to nonstandardized data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the health care system. Despite the existence of standardized data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remain limited. ObjectiveIn this paper, we developed a data harmonization pipeline (DHP) for clinical data sets relying on the common FHIR data standard. MethodsWe validated the performance and usability of our FHIR-DHP with data from the Medical Information Mart for Intensive Care IV database. ResultsWe present the FHIR-DHP workflow in respect of the transformation of “raw” hospital records into a harmonized, AI-friendly data representation. The pipeline consists of the following 5 key preprocessing steps: querying of data from hospital database, FHIR mapping, syntactic validation, transfer of harmonized data into the patient-model database, and export of data in an AI-friendly format for further medical applications. A detailed example of FHIR-DHP execution was presented for clinical diagnoses records. ConclusionsOur approach enables the scalable and needs-driven data modeling of large and heterogenous clinical data sets. The FHIR-DHP is a pivotal step toward increasing cooperation, interoperability, and quality of patient care in the clinical routine and for medical research.

BackgroundData provenance refers to the origin, processing, and movement of data. Reliable and precise knowledge about data provenance has great potential to improve reproducibility as well as quality in biomedical research and, therefore, to foster good scientific practice. However, despite the increasing interest on data provenance technologies in the literature and their implementation in other disciplines, these technologies have not yet been widely adopted in biomedical research. ObjectiveThe aim of this scoping review was to provide a structured overview of the body of knowledge on provenance methods in biomedical research by systematizing articles covering data provenance technologies developed for or used in this application area; describing and comparing the functionalities as well as the design of the provenance technologies used; and identifying gaps in the literature, which could provide opportunities for future research on technologies that could receive more widespread adoption. MethodsFollowing a methodological framework for scoping studies and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, articles were identified by searching the PubMed, IEEE Xplore, and Web of Science databases and subsequently screened for eligibility. We included original articles covering software-based provenance management for scientific research published between 2010 and 2021. A set of data items was defined along the following five axes: publication metadata, application scope, provenance aspects covered, data representation, and functionalities. The data items were extracted from the articles, stored in a charting spreadsheet, and summarized in tables and figures. ResultsWe identified 44 original articles published between 2010 and 2021. We found that the solutions described were heterogeneous along all axes. We also identified relationships among motivations for the use of provenance information, feature sets (capture, storage, retrieval, visualization, and analysis), and implementation details such as the data models and technologies used. The important gap that we identified is that only a few publications address the analysis of provenance data or use established provenance standards, such as PROV. ConclusionsThe heterogeneity of provenance methods, models, and implementations found in the literature points to the lack of a unified understanding of provenance concepts for biomedical data. Providing a common framework, a biomedical reference, and benchmarking data sets could foster the development of more comprehensive provenance solutions.

Introduction Postoperative delirium (POD) is seen in approximately 15% of elderly patients and is related to poorer outcomes. In 2017, the Federal Joint Committee (Gemeinsamer Bundesausschuss) introduced a ‘quality contract’ (QC) as a new instrument to improve healthcare in Germany. One of the four areas for improvement of in-patient care is the ‘Prevention of POD in the care of elderly patients’ (QC-POD), as a means to reduce the risk of developing POD and its complications.The Institute for Quality Assurance and Transparency in Health Care identified gaps in the in-patient care of elderly patients related to the prevention, screening and treatment of POD, as required by consensus-based and evidence-based delirium guidelines. This paper introduces the QC-POD protocol, which aims to implement these guidelines into the clinical routine. There is an urgent need for well-structured, standardised and interdisciplinary pathways that enable the reliable screening and treatment of POD. Along with effective preventive measures, these concepts have a considerable potential to improve the care of elderly patients.Methods and analysis The QC-POD study is a non-randomised, pre–post, monocentric, prospective trial with an interventional concept following a baseline control period. The QC-POD trial was initiated on 1 April 2020 between Charité-Universitätsmedizin Berlin and the German health insurance company BARMER and will end on 30 June 2023. Inclusion criteria: patients 70 years of age or older that are scheduled for a surgical procedure requiring anaesthesia and insurance with the QC partner (BARMER). Exclusion criteria included patients with a language barrier, moribund patients and those unwilling or unable to provide informed consent. The QC-POD protocol provides perioperative intervention at least two times per day, with delirium screening and non-pharmacological preventive measures.Ethics and dissemination This protocol was approved by the ethics committee of the Charité-Universitätsmedizin, Berlin, Germany (EA1/054/20). The results will be published in a peer-reviewed scientific journal and presented at national and international conferences.Trial registration number NCT04355195.

Abstract Background Increased plasma concentrations of circulating cell-free hemoglobin (CFH) are supposed to contribute to the multifactorial etiology of acute kidney injury (AKI) in critically ill patients while the CFH-scavenger haptoglobin might play a protective role. We evaluated the association of CFH and haptoglobin with AKI in patients with an acute respiratory distress syndrome (ARDS) requiring therapy with VV ECMO. Methods Patients with CFH and haptoglobin measurements before initiation of ECMO therapy were identified from a cohort of 1044 ARDS patients and grouped into three CFH concentration groups using a risk stratification. The primary objective was to assess the association of CFH and haptoglobin with KDIGO stage 3 AKI. Further objectives included the identification of a target haptoglobin concentration to protect from CFH-associated AKI. Measurements and main results Two hundred seventy-three patients fulfilled the inclusion criteria. Of those, 154 patients (56.4%) had AKI at ECMO initiation. The incidence of AKI increased stepwise with increasing concentrations of CFH reaching a plateau at 15 mg/dl. Compared to patients with low [

Background Healthcaare delivery will change through the increasing use of artificial intelligence (AI). Physicians are likely to be among the professions most affected, though to what extent is not yet clear. Objective We analyzed physicians’ and AI experts’ stances towards AI-induced changes. This concerned (1) physicians’ tasks, (2) job replacement risk, and (3) implications for the ways of working, including human–AI interaction, changes in job profiles, and hierarchical and cross-professional collaboration patterns. Methods We adopted an exploratory, qualitative research approach, using semi-structured interviews with 24 experts in the fields of AI and medicine, medical informatics, digital medicine, and medical education and training. Thematic analysis of the interview transcripts was performed. Results Specialized tasks currently performed by physicians in all areas of medicine would likely be taken over by AI, including bureaucratic tasks, clinical decision support, and research. However, the concern that physicians will be replaced by an AI system is unfounded, according to experts; AI systems today would be designed only for a specific use case and could not replace the human factor in the patient–physician relationship. Nevertheless, the job profile and professional role of physicians would be transformed as a result of new forms of human–AI collaboration and shifts to higher-value activities. AI could spur novel, more interprofessional teams in medical practice and research and, eventually, democratization and de-hierarchization. Conclusions The study highlights changes in job profiles of physicians and outlines demands for new categories of medical professionals considering AI-induced changes of work. Physicians should redefine their self-image and assume more responsibility in the age of AI-supported medicine. There is a need for the development of scenarios and concepts for future job profiles in the health professions as well as their education and training.

BackgroundAlthough medical decision-making may be thought of as a task involving health professionals, many decisions, including critical health–related decisions are made by laypersons alone. Specifically, as the first step to most care episodes, it is the patient who determines whether and where to seek health care (triage). Overcautious self-assessments (ie, overtriaging) may lead to overutilization of health care facilities and overcrowded emergency departments, whereas imprudent decisions (ie, undertriaging) constitute a risk to the patient’s health. Recently, patient-facing decision support systems, commonly known as symptom checkers, have been developed to assist laypersons in these decisions. ObjectiveThe purpose of this study is to identify factors influencing laypersons’ ability to self-triage and their risk averseness in self-triage decisions. MethodsWe analyzed publicly available data on 91 laypersons appraising 45 short fictitious patient descriptions (case vignettes; N=4095 appraisals). Using signal detection theory and descriptive and inferential statistics, we explored whether the type of medical decision laypersons face, their confidence in their decision, and sociodemographic factors influence their triage accuracy and the type of errors they make. We distinguished between 2 decisions: whether emergency care was required (decision 1) and whether self-care was sufficient (decision 2). ResultsThe accuracy of detecting emergencies (decision 1) was higher (mean 82.2%, SD 5.9%) than that of deciding whether any type of medical care is required (decision 2, mean 75.9%, SD 5.25%; t>90=8.4; P89=3.7; P

BackgroundAnticoagulation therapy with heparin is a frequent treatment in intensive care units and is monitored by activated partial thromboplastin clotting time (aPTT). It has been demonstrated that reaching an established anticoagulation target within 24 hours is associated with favorable outcomes. However, patients respond to heparin differently and reaching the anticoagulation target can be challenging. Machine learning algorithms may potentially support clinicians with improved dosing recommendations. ObjectiveThis study evaluates a range of machine learning algorithms on their capability of predicting the patients’ response to heparin treatment. In this analysis, we apply, for the first time, a model that considers time series. MethodsWe extracted patient demographics, laboratory values, dialysis and extracorporeal membrane oxygenation treatments, and scores from the hospital information system. We predicted the numerical values of aPTT laboratory values 24 hours after continuous heparin infusion and evaluated 7 different machine learning models. The best-performing model was compared to recently published models on a classification task. We considered all data before and within the first 12 hours of continuous heparin infusion as features and predicted the aPTT value after 24 hours. ResultsThe distribution of aPTT in our cohort of 5926 hospital admissions was highly skewed. Most patients showed aPTT values below 75 s, while some outliers showed much higher aPTT values. A recurrent neural network that consumes a time series of features showed the highest performance on the test set. ConclusionsA recurrent neural network that uses time series of features instead of only static and aggregated features showed the highest performance in predicting aPTT after heparin treatment.

Patient monitoring technology has been used to guide therapy and alert staff when a vital sign leaves a predefined range in the intensive care unit (ICU) for decades. However, large amounts of technically false or clinically irrelevant alarms provoke alarm fatigue in staff leading to desensitisation towards critical alarms. With this systematic review, we are following the Preferred Reporting Items for Systematic Reviews (PRISMA) checklist in order to summarise scientific efforts that aimed to develop IT systems to reduce alarm fatigue in ICUs. 69 peer-reviewed publications were included. The majority of publications targeted the avoidance of technically false alarms, while the remainder focused on prediction of patient deterioration or alarm presentation. The investigated alarm types were mostly associated with heart rate or arrhythmia, followed by arterial blood pressure, oxygen saturation, and respiratory rate. Most publications focused on the development of software solutions, some on wearables, smartphones, or headmounted displays for delivering alarms to staff. The most commonly used statistical models were tree-based. In conclusion, we found strong evidence that alarm fatigue can be alleviated by IT-based solutions. However, future efforts should focus more on the avoidance of clinically non-actionable alarms which could be accelerated by improving the data availability.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021233461, identifier: CRD42021233461.

Abstract In a pandemic with a novel disease, disease-specific prognosis models are available only with a delay. To bridge the critical early phase, models built for similar diseases might be applied. To test the accuracy of such a knowledge transfer, we investigated how precise lethal courses in critically ill COVID-19 patients can be predicted by a model trained on critically ill non-COVID-19 viral pneumonia patients. We trained gradient boosted decision tree models on 718 (245 deceased) non-COVID-19 viral pneumonia patients to predict individual ICU mortality and applied it to 1054 (369 deceased) COVID-19 patients. Our model showed a significantly better predictive performance (AUROC 0.86 [95% CI 0.86–0.87]) than the clinical scores APACHE2 (0.63 [95% CI 0.61–0.65]), SAPS2 (0.72 [95% CI 0.71–0.74]) and SOFA (0.76 [95% CI 0.75–0.77]), the COVID-19-specific mortality prediction models of Zhou (0.76 [95% CI 0.73–0.78]) and Wang (laboratory: 0.62 [95% CI 0.59–0.65]; clinical: 0.56 [95% CI 0.55–0.58]) and the 4C COVID-19 Mortality score (0.71 [95% CI 0.70–0.72]). We conclude that lethal courses in critically ill COVID-19 patients can be predicted by a machine learning model trained on non-COVID-19 patients. Our results suggest that in a pandemic with a novel disease, prognosis models built for similar diseases can be applied, even when the diseases differ in time courses and in rates of critical and lethal courses.

BackgroundValuable insights into the pathophysiology and consequences of acute psychosocial stress have been gained using standardized stress induction experiments. However, most protocols are limited to laboratory settings, are labor-intensive, and cannot be scaled to larger cohorts or transferred to daily life scenarios. ObjectiveWe aimed to provide a scalable digital tool that enables the standardized induction and recording of acute stress responses in outside-the-laboratory settings without any experimenter contact. MethodsOn the basis of well-described stress protocols, we developed the Digital Stress Test (DST) and evaluated its feasibility and stress induction potential in a large web-based study. A total of 284 participants completed either the DST (n=103; 52/103, 50.5% women; mean age 31.34, SD 9.48 years) or an adapted control version (n=181; 96/181, 53% women; mean age 31.51, SD 11.18 years) with their smartphones via a web application. We compared their affective responses using the international Positive and Negative Affect Schedule Short Form before and after stress induction. In addition, we assessed the participants’ stress-related feelings indicated in visual analogue scales before, during, and after the procedure, and further analyzed the implemented stress-inducing elements. Finally, we compared the DST participants’ stress reactivity with the results obtained in a classic stress test paradigm using data previously collected in 4 independent Trier Social Stress Test studies including 122 participants overall. ResultsParticipants in the DST manifested significantly higher perceived stress indexes than the Control-DST participants at all measurements after the baseline (P

Background: Every medical decision is based on balancing medical knowledge, ethical considerations, and patient preferences. Previous surveys have mainly covered the ethical knowledge of medical staff. The aim of this study is to evaluate the feasibility of an innovative concept regarding how ethical criteria are applied to clinical decision-making during critical illness. Methods: An online survey including a short case vignette was carried out at a university hospital among physicians specialising in intensive care medicine in Germany. After free text responses regarding further required case information, the participants were asked to rank decision criteria during the course of the case vignette. A qualitative evaluation was performed by two independent investigators, based on a transcription into categories. This was followed by a quantitative analysis of ranked criteria. Results: Our analysis has shown that doctors are initially inclined to consider medical information when making treatment decisions. When complications occur, ethical values are more often included in the decision-making. The qualitative evaluation reveiled that the patient's will was consistently regarded as the leading criterion for decision-making. In the quantitative evaluation, patient's well-being, quality of life, and patient autonomy were rated as the most important decision criteria. Economic factors were ranked least important. Conclusion: A mixed methods approach is able to reflect the complexity of ethical reasoning within the medical decision-making process, suggesting the feasibility of this concept. Clinical trial registration: The study was registered under DRKS-ID: DKRS00011905 (April 2017).

BackgroundSymptom checker apps are patient-facing decision support systems aimed at providing advice to laypersons on whether, where, and how to seek health care (disposition advice). Such advice can improve laypersons’ self-assessment and ultimately improve medical outcomes. Past research has mainly focused on the accuracy of symptom checker apps’ suggestions. To support decision-making, such apps need to provide not only accurate but also trustworthy advice. To date, only few studies have addressed the question of the extent to which laypersons trust symptom checker app advice or the factors that moderate their trust. Studies on general decision support systems have shown that framing automated systems (anthropomorphic or emphasizing expertise), for example, by using icons symbolizing artificial intelligence (AI), affects users’ trust. ObjectiveThis study aims to identify the factors influencing laypersons’ trust in the advice provided by symptom checker apps. Primarily, we investigated whether designs using anthropomorphic framing or framing the app as an AI increases users’ trust compared with no such framing. MethodsThrough a web-based survey, we recruited 494 US residents with no professional medical training. The participants had to first appraise the urgency of a fictitious patient description (case vignette). Subsequently, a decision aid (mock symptom checker app) provided disposition advice contradicting the participants’ appraisal, and they had to subsequently reappraise the vignette. Participants were randomized into 3 groups: 2 experimental groups using visual framing (anthropomorphic, 160/494, 32.4%, vs AI, 161/494, 32.6%) and a neutral group without such framing (173/494, 35%). ResultsMost participants (384/494, 77.7%) followed the decision aid’s advice, regardless of its urgency level. Neither anthropomorphic framing (odds ratio 1.120, 95% CI 0.664-1.897) nor framing as AI (odds ratio 0.942, 95% CI 0.565-1.570) increased behavioral or subjective trust (P=.99) compared with the no-frame condition. Even participants who were extremely certain in their own decisions (ie, 100% certain) commonly changed it in favor of the symptom checker’s advice (19/34, 56%). Propensity to trust and eHealth literacy were associated with increased subjective trust in the symptom checker (propensity to trust b=0.25; eHealth literacy b=0.2), whereas sociodemographic variables showed no such link with either subjective or behavioral trust. ConclusionsContrary to our expectation, neither the anthropomorphic framing nor the emphasis on AI increased trust in symptom checker advice compared with that of a neutral control condition. However, independent of the interface, most participants trusted the mock app’s advice, even when they were very certain of their own assessment. Thus, the question arises as to whether laypersons use such symptom checkers as substitutes rather than as aids in their own decision-making. With trust in symptom checkers already high at baseline, the benefit of symptom checkers depends on interface designs that enable users to adequately calibrate their trust levels during usage. Trial RegistrationDeutsches Register Klinischer Studien DRKS00028561; https://tinyurl.com/rv4utcfb (retrospectively registered).

BackgroundSymptom checkers are digital tools assisting laypersons in self-assessing the urgency and potential causes of their medical complaints. They are widely used but face concerns from both patients and health care professionals, especially regarding their accuracy. A 2015 landmark study substantiated these concerns using case vignettes to demonstrate that symptom checkers commonly err in their triage assessment. ObjectiveThis study aims to revisit the landmark index study to investigate whether and how symptom checkers’ capabilities have evolved since 2015 and how they currently compare with laypersons’ stand-alone triage appraisal. MethodsIn early 2020, we searched for smartphone and web-based applications providing triage advice. We evaluated these apps on the same 45 case vignettes as the index study. Using descriptive statistics, we compared our findings with those of the index study and with publicly available data on laypersons’ triage capability. ResultsWe retrieved 22 symptom checkers providing triage advice. The median triage accuracy in 2020 (55.8%, IQR 15.1%) was close to that in 2015 (59.1%, IQR 15.5%). The apps in 2020 were less risk averse (odds 1.11:1, the ratio of overtriage errors to undertriage errors) than those in 2015 (odds 2.82:1), missing >40% of emergencies. Few apps outperformed laypersons in either deciding whether emergency care was required or whether self-care was sufficient. No apps outperformed the laypersons on both decisions. ConclusionsTriage performance of symptom checkers has, on average, not improved over the course of 5 years. It decreased in 2 use cases (advice on when emergency care is required and when no health care is needed for the moment). However, triage capability varies widely within the sample of symptom checkers. Whether it is beneficial to seek advice from symptom checkers depends on the app chosen and on the specific question to be answered. Future research should develop resources (eg, case vignette repositories) to audit the capabilities of symptom checkers continuously and independently and provide guidance on when and to whom they should be recommended.

Introduction: Acute kidney injury (AKI) is an important complication in COVID-19, but its precise etiology has not fully been elucidated. Insights into AKI mechanisms may be provided by analyzing the temporal associations of clinical parameters reflecting disease processes and AKI development. Methods: We performed an observational cohort study of 223 consecutive COVID-19 patients treated at 3 sites of a tertiary care referral center to describe the evolvement of severe AKI (Kidney Disease: Improving Global Outcomes stage 3) and identify conditions promoting its development. Descriptive statistics and explanatory multivariable Cox regression modeling with clinical parameters as time-varying covariates were used to identify risk factors of severe AKI. Results: Severe AKI developed in 70 of 223 patients (31%) with COVID-19, of which 95.7% required kidney replacement therapy. Patients with severe AKI were older, predominantly male, had more comorbidities, and displayed excess mortality. Severe AKI occurred exclusively in intensive care unit patients, and 97.3% of the patients developing severe AKI had respiratory failure. Mechanical ventilation, vasopressor therapy, and inflammatory markers (serum procalcitonin levels and leucocyte count) were independent time-varying risk factors of severe AKI. Increasing inflammatory markers displayed a close temporal association with the development of severe AKI. Sensitivity analysis on risk factors of AKI stage 2 and 3 combined confirmed these findings. Conclusion: Severe AKI in COVID-19 was tightly coupled with critical illness and systemic inflammation and was not observed in milder disease courses. These findings suggest that traditional systemic AKI mechanisms rather than kidney-specific processes contribute to severe AKI in COVID-19.

BackgroundDigital health technologies such as continuous remote monitoring and artificial intelligence–driven clinical decision support systems could improve clinical outcomes in intensive care medicine. However, comprehensive evidence and guidelines for the successful implementation of digital health technologies into specific clinical settings such as the intensive care unit (ICU) are scarce. We evaluated the implementation of a remote patient monitoring platform and derived a framework proposal for the implementation of digital health technology in an ICU. ObjectiveThis study aims to investigate barriers and facilitators to the implementation of a remote patient monitoring technology and to develop a proposal for an implementation framework for digital health technology in the ICU. MethodsThis study was conducted from May 2018 to March 2020 during the implementation of a tablet computer–based remote patient monitoring system. The system was installed in the ICU of a large German university hospital as a supplementary monitoring device. Following a hybrid qualitative approach with inductive and deductive elements, we used the Consolidated Framework for Implementation Research and the Expert Recommendations for Implementing Change to analyze the transcripts of 7 semistructured interviews with clinical ICU stakeholders and descriptive questionnaire data. The results of the qualitative analysis, together with the findings from informal meetings, field observations, and previous explorations, provided the basis for the derivation of the proposed framework. ResultsThis study revealed an insufficient implementation process due to lack of staff engagement and few perceived benefits from the novel solution. Further implementation barriers were the high staff presence and monitoring coverage in the ICU. The implementation framework includes strategies to be applied before and during implementation, targeting the implementation setting by involving all ICU stakeholders, assessing the intervention’s adaptability, facilitating the implementation process, and maintaining a vital feedback culture. Setting up a unit responsible for implementation, considering the guidance of an implementation advisor, and building on existing institutional capacities could improve the institutional context of implementation projects in the ICU. ConclusionsImplementation of digital health in the ICU should involve a thorough preimplementation assessment of the ICU’s need for innovation and its readiness to change, as well as an ongoing evaluation of the implementation conditions. Involvement of all stakeholders, transparent communication, and continuous feedback in an equal atmosphere are essential, but leadership roles must be clearly defined and competently filled. Our proposed framework may guide health care providers with concrete, evidence-based, and step-by-step recommendations for implementation practice, facilitating the introduction of digital health in intensive care. Trial RegistrationClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173

BackgroundDuring the COVID-19 pandemic, medical laypersons with symptoms indicative of a COVID-19 infection commonly sought guidance on whether and where to find medical care. Numerous web-based decision support tools (DSTs) have been developed, both by public and commercial stakeholders, to assist their decision making. Though most of the DSTs’ underlying algorithms are similar and simple decision trees, their mode of presentation differs: some DSTs present a static flowchart, while others are designed as a conversational agent, guiding the user through the decision tree’s nodes step-by-step in an interactive manner. ObjectiveThis study aims to investigate whether interactive DSTs provide greater decision support than noninteractive (ie, static) flowcharts. MethodsWe developed mock interfaces for 2 DSTs (1 static, 1 interactive), mimicking patient-facing, freely available DSTs for COVID-19-related self-assessment. Their underlying algorithm was identical and based on the Centers for Disease Control and Prevention’s guidelines. We recruited adult US residents online in November 2020. Participants appraised the appropriate social and care-seeking behavior for 7 fictitious descriptions of patients (case vignettes). Participants in the experimental groups received either the static or the interactive mock DST as support, while the control group appraised the case vignettes unsupported. We determined participants’ accuracy, decision certainty (after deciding), and mental effort to measure the quality of decision support. Participants’ ratings of the DSTs’ usefulness, ease of use, trust, and future intention to use the tools served as measures to analyze differences in participants’ perception of the tools. We used ANOVAs and t tests to assess statistical significance. ResultsOur survey yielded 196 responses. The mean number of correct assessments was higher in the intervention groups (interactive DST group: mean 11.71, SD 2.37; static DST group: mean 11.45, SD 2.48) than in the control group (mean 10.17, SD 2.00). Decisional certainty was significantly higher in the experimental groups (interactive DST group: mean 80.7%, SD 14.1%; static DST group: mean 80.5%, SD 15.8%) compared to the control group (mean 65.8%, SD 20.8%). The differences in these measures proved statistically significant in t tests comparing each intervention group with the control group (P

BackgroundContinuous monitoring of vital signs is critical for ensuring patient safety in intensive care units (ICUs) and is becoming increasingly relevant in general wards. The effectiveness of health information technologies such as patient-monitoring systems is highly determined by usability, the lack of which can ultimately compromise patient safety. Usability problems can be identified and prevented by involving users (ie, clinicians). ObjectiveIn this study, we aim to apply a human-centered design approach to evaluate the usability of a remote patient-monitoring system user interface (UI) in the ICU context and conceptualize and evaluate design changes. MethodsFollowing institutional review board approval (EA1/031/18), a formative evaluation of the monitoring UI was performed. Simulated use tests with think-aloud protocols were conducted with ICU staff (n=5), and the resulting qualitative data were analyzed using a deductive analytic approach. On the basis of the identified usability problems, we conceptualized informed design changes and applied them to develop an improved prototype of the monitoring UI. Comparing the UIs, we evaluated perceived usability using the System Usability Scale, performance efficiency with the normative path deviation, and effectiveness by measuring the task completion rate (n=5). Measures were tested for statistical significance using a 2-sample t test, Poisson regression with a generalized linear mixed-effects model, and the N-1 chi-square test. P

Abstract Background Hemophagocytic lymphohistiocytosis (HLH) is a rare though often fatal hyperinflammatory syndrome mimicking sepsis in the critically ill. Diagnosis relies on the HLH-2004 criteria and HScore, both of which have been developed in pediatric or adult non-critically ill patients, respectively. Therefore, we aimed to determine the sensitivity and specificity of HLH-2004 criteria and HScore in a cohort of adult critically ill patients. Methods In this further analysis of a retrospective observational study, patients ≥ 18 years admitted to at least one adult ICU at Charité – Universitätsmedizin Berlin between January 2006 and August 2018 with hyperferritinemia of ≥ 500 μg/L were included. Patients’ charts were reviewed for clinically diagnosed or suspected HLH. Receiver operating characteristics (ROC) analysis was performed to determine prediction accuracy. Results In total, 2623 patients with hyperferritinemia were included, of whom 40 patients had HLH. We found the best prediction accuracy of HLH diagnosis for a cutoff of 4 fulfilled HLH-2004 criteria (95.0% sensitivity and 93.6% specificity) and HScore cutoff of 168 (100% sensitivity and 94.1% specificity). By adjusting HLH-2004 criteria cutoffs of both hyperferritinemia to 3000 μg/L and fever to 38.2 °C, sensitivity and specificity increased to 97.5% and 96.1%, respectively. Both a higher number of fulfilled HLH-2004 criteria [OR 1.513 (95% CI 1.372–1.667); p

Abstract Background Frailty is a frequent and underdiagnosed functional syndrome involving reduced physiological reserves and an increased vulnerability against stressors, with severe individual and socioeconomic consequences. A routine frailty assessment was implemented at our preoperative anaesthesia clinic to identify patients at risk. Objective This study examines the relationship between frailty status and the incidence of in-hospital postoperative complications in elderly surgical patients across several surgical disciplines. Design Retrospective observational analysis. Setting Single center, major tertiary care university hospital. Data collection took place between June 2016 and March 2017. Patients Patients 65 years old or older were evaluated for frailty using Fried’s 5-point frailty assessment prior to elective non-cardiac surgery. Patients were classified into non-frail (0 criteria, reference group), pre-frail (1–2 positive criteria) and frail (3–5 positive criteria) groups. Main outcome measures The incidence of postoperative complications was assessed until discharge from the hospital, using the roster from the National VA Surgical Quality Improvement Program. Propensity score matching and logistic regression analysis were performed. Results From 1186 elderly patients, 46.9% were classified as pre-frail (n = 556), and 11.4% as frail (n = 135). The rate of complications were significantly higher in the pre-frail (34.7%) and frail groups (47.4%), as compared to the non-frail group (27.5%). Similarly, length of stay (non-frail: 5.0 [3.0;7.0], pre-frail: 7.0 [3.0;9.0], frail 8.0 [4.5;12.0]; p

BackgroundThe role of telemedicine in intensive care has been increasing steadily. Tele–intensive care unit (ICU) interventions are varied and can be used in different levels of treatment, often with direct implications for the intensive care processes. Although a substantial body of primary and secondary literature has been published on the topic, there is a need for broadening the understanding of the organizational factors influencing the effectiveness of telemedical interventions in the ICU. ObjectiveThis scoping review aims to provide a map of existing evidence on tele-ICU interventions, focusing on the analysis of the implementation context and identifying areas for further technological research. MethodsA research protocol outlining the method has been published in JMIR Research Protocols. This review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). A core research team was assembled to provide feedback and discuss findings. ResultsA total of 3019 results were retrieved. After screening, 25 studies were included in the final analysis. We were able to characterize the context of tele-ICU studies and identify three use cases for tele-ICU interventions. The first use case is extending coverage, which describes interventions aimed at extending the availability of intensive care capabilities. The second use case is improving compliance, which includes interventions targeted at improving patient safety, intensive care best practices, and quality of care. The third use case, facilitating transfer, describes telemedicine interventions targeted toward the management of patient transfers to or from the ICU. ConclusionsThe benefits of tele-ICU interventions have been well documented for centralized systems aimed at extending critical care capabilities in a community setting and improving care compliance in tertiary hospitals. No strong evidence has been found on the reduction of patient transfers following tele-ICU intervention. International Registered Report Identifier (IRRID)RR2-10.2196/19695

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(1) Background: Acute respiratory distress syndrome (ARDS) is a rare complication in multiply injured patients. Due to the rarity of ARDS development after trauma, little is known about outcomes of patients with trauma-associated ARDS compared to patients with non-trauma-associated ARDS. (2) Methods: This retrospective analysis included n = 1038 ARDS patients admitted to the ARDS center of Charité—Universitätsmedizin Berlin between 2007 and 2018. Patients with trauma-associated ARDS (n = 62) were compared to patients with non-trauma-associated ARDS (n = 976). In a secondary analysis, patients from the group with non-trauma-associated ARDS were 1:1 nearest neighbor matched to patients with trauma-associated ARDS. The primary outcomes were 28-day in-hospital mortality, 60-day in-hospital mortality, and overall in-hospital mortality. (3) Results: Overall in-hospital mortality in trauma-associated ARDS was 29.0% compared to 40.5% in all patients with non-trauma-associated ARDS (p = 0.074). The in-hospital mortality rate in matched patients with non-trauma-associated ARDS (33.9%) was comparable to the trauma-associated ARDS cohort (p = 0.701). Kaplan–Meier curves indicated time-sensitive variations in 28-day and 60-day in-hospital survival. (4) Conclusion: Mortality was not different in patients with trauma-associated ARDS compared to patients with non-trauma-associated ARDS. Survival rate in the Kaplan–Meier curves stabilized after the critical initial phase and throughout the further 60-day period in patients with trauma-associated ARDS compared to patients with non-trauma-associated ARDS. Since this divergence was less pronounced in the matched cohort, it may be related to the younger age, fewer comorbidities, and lower ARDS severity in patients with trauma-associated ARDS. Patients with trauma-associated ARDS remain a very different cohort compared to patients with non-trauma-associated ARDS. Therefore, the outcome comparison is limited, even after matching.

Abstract Background Serum potassium concentrations are commonly between 3.5 and 5.0 mmol/l. Standardised protocols for potassium range and supplementation in the ICU are lacking. The purpose of this retrospective analysis of ICU patients was to investigate potassium concentrations, variability and supplementation, and their association with in-hospital mortality. Methods ICU patients ≥ 18 years, with ≥ 2 serum potassium values, treated at the Charité - Universitätsmedizin Berlin between 2006 and 2018 were eligible for inclusion. We categorised into groups of mean potassium concentrations: 3.5–4.0, > 4.0–4.5, > 4.5–5.0, > 5.0–5.5, > 5.5 mmol/l and potassium variability: 1st, 2nd and ≥ 3rd standard deviation (SD). We analysed the association between the particular groups and in-hospital mortality and performed binary logistic regression analysis. Survival curves were performed according to Kaplan–Meier and tested by Log-Rank. In a subanalysis, the association between potassium supplementation and in-hospital mortality was investigated. Results In 53,248 ICU patients with 1,337,742 potassium values, the lowest mortality (3.7%) was observed in patients with mean potassium concentrations between > 3.5 and 4.0 mmol/l and a low potassium variability within the 1st SD. Binary logistic regression confirmed these results. In a subanalysis of 22,406 ICU patients (ICU admission: 2013–2018), 12,892 (57.5%) received oral and/or intravenous potassium supplementation. Potassium supplementation was associated with an increase in in-hospital mortality in potassium categories from > 3.5 to 4.5 mmol/l and in the 1st, 2nd and ≥ 3rd SD (p

Abstract Background Especially patients older than 65 years undergoing surgery are prone to develop frailty-related complications that may go far beyond the index hospitalization (e.g., cognitive impairment following postoperative delirium). However, aging-relevant information are currently not fully integrated into hospitals’ perioperative processes. Methods We introduce a temporal perspective, which focuses on the social construction of time, to better understand existing barriers to the exchange of frailty-related data, targeting complexity research. Our chosen context is perioperative care provided by a tertiary hospital in Germany that has implemented a special track for patients over 65 years old undergoing elective surgery. The research followed a participatory modelling approach between domain and modelling experts with the goal of creating a feedback loop model of the relevant system relationships and dynamics. Results The results of the study show how disparate temporal regimes, understood as frameworks for organizing actions in the light of time constraints, time pressure, and deadlines, across different clinical, ambulant, and geriatric care sectors create disincentives to cooperate in frailty-related data exchanges. Moreover, we find that shifting baselines, meaning continuous increases in cost and time pressure in individual sectors, may unintentionally reinforce – rather than discourage – disparate temporal regimes. Conclusions Together, these results may (1) help to increase awareness of the importance of frailty-related data exchanges, and (2) impel efforts aiming to transform treatment processes to go beyond sectoral boundaries, taking into account the potential benefits for frail patients arising from integrated care processes using information technology.

(1) Background: Female sex is considered a risk factor for Intensive Care Unit-Acquired Weakness (ICUAW). The aim is to investigate sex-specific aspects of skeletal muscle metabolism in the context of ICUAW. (2) Methods: This is a sex-specific sub-analysis from two prospectively conducted trials examining skeletal muscle metabolism and advanced muscle activating measures in critical illness. Muscle strength was assessed by Medical Research Council Score. The insulin sensitivity index was analyzed by hyperinsulinemic-euglycemic (HE) clamp. Muscular metabolites were studied by microdialysis. M. vastus lateralis biopsies were taken. The molecular analysis included protein degradation pathways. Morphology was assessed by myocyte cross-sectional area (MCSA). Multivariable linear regression models for the effect of sex on outcome parameters were performed. (3) Results: n = 83 (♂n = 57, 68.7%; ♀n = 26, 31.3%) ICU patients were included. ICUAW was present in 81.1%♂ and in 82.4%♀ at first awakening (p = 0.911) and in 59.5%♂ and in 70.6%♀ at ICU discharge (p = 0.432). Insulin sensitivity index was reduced more in women than in men (p = 0.026). Sex was significantly associated with insulin sensitivity index and MCSA of Type IIa fibers in the adjusted regression models. (4) Conclusion: This hypothesis-generating analysis suggests that more pronounced impairments in insulin sensitivity and lower MCSA of Type IIa fibers in critically ill women may be relevant for sex differences in ICUAW.

BackgroundSignificant improvements in clinical outcome can be achieved by implementing effective strategies to optimise pain management, reduce sedative exposure, and prevent and treat delirium in ICU patients. One important strategy is the monitoring of pain, agitation and delirium (PAD bundle). We hypothesised that there is no sufficient financial benefit to implement a monitoring strategy in a Diagnosis Related Group (DRG)-based reimbursement system, therefore we expected better clinical and decreased economic outcome for monitored patients.MethodsThis is a retrospective observational study using routinely collected data. We used univariate and multiple linear analysis, machine-learning analysis and a novel correlation statistic (maximal information coefficient) to explore the association between monitoring adherence and resulting clinical and economic outcome. For univariate analysis we split patients in an adherence achieved and an adherence non-achieved group.ResultsIn total 1,323 adult patients from two campuses of a German tertiary medical centre, who spent at least one day in the ICU between admission and discharge between 1. January 2016 and 31. December 2016. Adherence to PAD monitoring was associated with shorter hospital LoS (e.g. pain monitoring 13 vs. 10 days; pConclusionAdherence to PAD bundles is also important for clinical as well as economic outcome. It is associated with improved clinical and worse economic outcome in comparison to non-adherence in univariate analysis but not confirmed by multiple linear analysis.Trial registrationclinicaltrials.gov NCT02265263, Registered 15 October 2014.