Alarm Management in Provisional COVID-19 Intensive Care Units: Retrospective Analysis and Recommendations for Future Pandemics.

BACKGROUND: In 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. OBJECTIVE: We 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. METHODS: Our 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. RESULTS: COVID-19 ICUs had significantly more alarms per bed per day than non-COVID-19 ICUs (P<.001), and the majority of the staff lacked experience with the alarm system. The overall median alarm duration was similar in both ICU types. We found no COVID-19-specific alarm patterns. The alarm fatigue questionnaire results suggest that staff in both types of ICUs experienced alarm fatigue. However, physicians and nurses who were working in COVID-19 ICUs reported a significantly higher level of alarm fatigue (P=.04). CONCLUSIONS: Staff in COVID-19 ICUs were exposed to a higher alarm load, and the majority lacked experience with alarm management and the alarm system. We recommend training and educating ICU staff in alarm management, emphasizing the importance of alarm management training as part of the preparations for future pandemics. However, the limitations of our study design and the specific pandemic conditions warrant further studies to confirm these findings and to explore effective alarm management strategies in different ICU settings.

Telemedicine in the age of the pandemics: The prospects of web-based remote patient monitoring systems for orthopaedic ambulatory care management in the developing economies.

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.

Short-term vital parameter forecasting in the intensive care unit: A benchmark study leveraging data from patients after cardiothoracic surgery.

Patients in an Intensive Care Unit (ICU) are closely and continuously monitored, and many machine learning (ML) solutions have been proposed to predict specific outcomes like death, bleeding, or organ failure. Forecasting of vital parameters is a more general approach to ML-based patient monitoring, but the literature on its feasibility and robust benchmarks of achievable accuracy are scarce. We implemented five univariate statistical models (the naïve model, the Theta method, exponential smoothing, the autoregressive integrated moving average model, and an autoregressive single-layer neural network), two univariate neural networks (N-BEATS and N-HiTS), and two multivariate neural networks designed for sequential data (a recurrent neural network with gated recurrent unit, GRU, and a Transformer network) to produce forecasts for six vital parameters recorded at five-minute intervals during intensive care monitoring. Vital parameters were the diastolic, systolic, and mean arterial blood pressure, central venous pressure, peripheral oxygen saturation (measured by non-invasive pulse oximetry) and heart rate, and forecasts were made for 5 through 120 minutes into the future. Patients used in this study recovered from cardiothoracic surgery in an ICU. The patient cohort used for model development (n = 22,348) and internal testing (n = 2,483) originated from a heart center in Germany, while a patient sub-set from the eICU collaborative research database, an American multicenter ICU cohort, was used for external testing (n = 7,477). The GRU was the predominant method in this study. Uni- and multivariate neural network models proved to be superior to univariate statistical models across vital parameters and forecast horizons, and their advantage steadily became more pronounced for increasing forecast horizons. With this study, we established an extensive set of benchmarks for forecast performance in the ICU. Our findings suggest that supplying physicians with short-term forecasts of vital parameters in the ICU is feasible, and that multivariate neural networks are most suited for the task due to their ability to learn patterns across thousands of patients.

A scalable approach for critical care data extraction and analysis in an academic medical center.

BACKGROUND: Electronic health records are a valuable asset for research, but their use is challenging due to inconsistencies of records, heterogeneous formats and the distribution over multiple, non-integrated information systems. Hence, specialized health data engineering and data science expertise are required to enable research. To facilitate secondary use of clinical routine data collected in our intensive care wards, we developed a scalable approach, consisting of cohort generation, variable filtering and data extraction steps. OBJECTIVE: With this report we share our workflow of data request, cohort identification and data extraction. We present an algorithm for automatic data extraction from our critical care information system (CCIS) that can be adapted to other object-oriented data bases. METHODS: We introduced a data request process with functionalities for automated identification of patient cohorts and a specialized hierarchical data structure that supports filtering relevant variables from the CCIS and further systems for the specified cohorts. The data extraction algorithm takes patient pseudonyms and variable lists as inputs. Algorithms are implemented in Python, leveraging the PySpark framework running on our data lake infrastructure. RESULTS: Our data request process is in operational use since June 2022. Since then we have served 121 projects with 148 service requests in total. We discuss the hierarchical structure and the frequently used data items of our CCIS in detail and present an application example, including cohort selection, data extraction and data transformation into an analyses-ready format. CONCLUSIONS: Using clinical routine data for secondary research is challenging and requires an interdisciplinary team. We developed a scalable approach that automates steps for cohort identification, data extraction and common data pre-processing steps. Additionally, we facilitate data harmonization, integration and consult on typical data analysis scenarios, machine learning algorithms and visualizations in dashboards.

InterAgent on FHIR - Lessons Learned from Implementing an Intelligent Tutoring System with the Help of HL7 FHIR.

INTRODUCTION: For an interoperable Intelligent Tutoring System (ITS), we used resources from Fast Healthcare Interoperability Resources (FHIR) and mapped learning content with Unified Medical Language System (UMLS) codes to enhance healthcare education. This study addresses the need to enhance the interoperability and effectiveness of ITS in healthcare education. STATE OF THE ART: The current state of the art in ITS involves advanced personalized learning and adaptability techniques, integrating technologies such as machine learning to personalize the learning experience and to create systems that dynamically respond to individual learner needs. However, existing ITS architectures face challenges related to interoperability and integration with healthcare systems. CONCEPT: Our system maps learning content with UMLS codes, each scored for similarity, ensuring consistency and extensibility. FHIR is used to standardize the exchange of medical information and learning content. IMPLEMENTATION: Implemented as a microservice architecture, the system uses a recommender to request FHIR resources, provide questions, and measure learner progress. LESSONS LEARNED: Using international standards, our ITS ensures reproducibility and extensibility, enhancing interoperability and integration with existing platforms.

Predicting postoperative delirium assessed by the Nursing Screening Delirium Scale in the recovery room for non-cardiac surgeries without craniotomy: A retrospective study using a machine learning approach.

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.

The impact of adhering to a quality indicator for sedation, analgesia, and delirium management on costs, revenues, and clinical outcomes in intensive care in Germany: A retrospective observational study.

BACKGROUND: Management 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. METHODS: In 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. RESULTS: Overall 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). CONCLUSION: Higher 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.

Evaluating the Construct Validity of the Charité Alarm Fatigue Questionnaire using Confirmatory Factor Analysis.

Background: The Charité Alarm Fatigue Questionnaire (CAFQa) is a 9-item questionnaire that aims to standardize how alarm fatigue in nurses and physicians is measured. We previously hypothesized that it has 2 correlated scales, one on the psychosomatic effects of alarm fatigue and the other on staff's coping strategies in working with alarms. Objective: We aimed to validate the hypothesized structure of the CAFQa and thus underpin the instrument's construct validity. Methods: We conducted 2 independent studies with nurses and physicians from intensive care units in Germany (study 1: n=265; study 2: n=1212). Responses to the questionnaire were analyzed using confirmatory factor analysis with the unweighted least-squares algorithm based on polychoric covariances. Convergent validity was assessed by participants' estimation of their own alarm fatigue and exposure to false alarms as a percentage. Results: In both studies, the χ2 test reached statistical significance (study 1: χ226=44.9; P=.01; study 2: χ226=92.4; P<.001). Other fit indices suggested a good model fit (in both studies: root mean square error of approximation <0.05, standardized root mean squared residual <0.08, relative noncentrality index >0.95, Tucker-Lewis index >0.95, and comparative fit index >0.995). Participants' mean scores correlated moderately with self-reported alarm fatigue (study 1: r=0.45; study 2: r=0.53) and weakly with self-perceived exposure to false alarms (study 1: r=0.3; study 2: r=0.33). Conclusions: The questionnaire measures the construct of alarm fatigue as proposed in our previous study. Researchers and clinicians can rely on the CAFQa to measure the alarm fatigue of nurses and physicians.

Key Research Areas for Building and Deploying a Common Data Model for an Intensive Medicine Data Space in Europe and Beyond.

Key Research Areas (KRAs) were identified to establish a semantic interoperability framework for intensive medicine data in Europe. These include assessing common data model value, ensuring smooth data interoperability, supporting data standardization for efficient dataset use, and defining anonymization requirements to balance data protection and innovation.

Undergraduate Degree in Medical Data Science: A Conceptual Framework.

The undergraduate degree program in medical data science aims to train future data scientists with a medical lens to tackle healthcare challenges using a data-driven approach. The program is a collaborative effort within the Berlin University Alliance, addressing the lack of healthcare-focused data science education in Berlin and Germany. The curriculum covers mathematics, informatics, medical informatics, and medicine, featuring diverse didactic formats. Graduates will be equipped to lead data science and digital transformation projects in healthcare.

Integrating FHIR and UMLS in an Intelligent Tutoring System.

Our novel Intelligent Tutoring System (ITS) architecture integrates HL7 Fast Healthcare Interoperability Resources (FHIR) for data exchange and Unified Medical Language System (UMLS) codes for content mapping.

Quality Assessment for Secondary Use of Imaging Trials.

Secondary use of health data has become an emerging topic in medical informatics. Many initiatives focus on clinical routine data, but clinical trial data has complementary strengths regarding highly structured documentation and mandatory data quality (DQ) reviews during the implementation. Clinical imaging trials investigate new imaging methods and procedures. Recently, DQ frameworks for structured data were proposed for harmonized quality assessments (QA). In this article, we investigate the application of these concepts to imaging trials and how a DQ framework could be defined for secondary use scenarios. We conclude that image quality can be assessed through both pixel data and metadata, and the latter can mostly be handled like structured study documentation in QA. For pixel data, typical quality indicators can be mapped to existing frameworks, but require additional image processing. Specific attention needs to be drawn to complete de-identification of imaging data, both on pixel data and metadata level.

Developing a Change Readiness Survey for Health Technology Implementation.

Addressing the challenges of health technology implementation, this study aims to develop a survey that assesses staff readiness for change in clinical settings. The survey items were refined from 67 to 38 through a narrative literature review, expert focus groups, and cognitive interviews. The survey suggests an approach that prioritizes the user's needs in identifying barriers and facilitators to the adoption of health technology in order to ensure successful implementation by proactively addressing potential obstacles.

In vivo effects of balanced, low molecular 6% and 10% hydroxyethyl starch compared with crystalloid volume replacement on the coagulation system in major pancreatic surgery-a sub-analysis of a prospective double-blinded, randomized controlled trial.

BACKGROUND: The 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. METHODS: The 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). RESULTS: Fifty-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, p<0.01, HES 6% 385 [302;442] to 174 [163;224] mg dl-1, p<0.01, crystalloids 408 [325;458] to 313 [248;370] mg dl-1, p = 0.01). MCF FIBTEM was decreased for both HES groups at EOS (HES 10%: 20.5 [16.0;24.8] to 6.5 [5.0;10.8] mm, p = <0.01; HES 6% 27.0 [18.8;35.2] to 7.0 [5.0;19.0] mm, p = <0.01). These changes did not persist on POD 1 for HES 10% (rise to 16.0 [13.0;24.0] mm, p = 0.88). Blood loss was not different in the groups nor transfusion requirements. CONCLUSION: Our 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.

Informing pandemic management in Germany with trustworthy living evidence syntheses and guideline development: lessons learned from the COVID-19 evidence ecosystem.

OBJECTIVES: We present the 'COVID-19 evidence ecosystem' (CEOsys) as a German network to inform pandemic management and to support clinical and public health decision-making. We discuss challenges faced when organizing the ecosystem and derive lessons learned for similar networks acting during pandemics or health-related crises. STUDY DESIGN AND SETTING: Bringing together 18 university hospitals and additional institutions, CEOsys key activities included research prioritization, conducting living systematic reviews (LSRs), supporting evidence-based (living) guidelines, knowledge translation (KT), detecting research gaps, and deriving recommendations, backed by technical infrastructure and capacity building. RESULTS: CEOsys rapidly produced 31 high-quality evidence syntheses and supported three living guidelines on COVID-19-related topics, while also developing methodological procedures. Challenges included CEOsys' late initiation in relation to the pandemic outbreak, the delayed prioritization of research questions, the continuously evolving COVID-19-related evidence, and establishing a technical infrastructure. Methodological-clinical tandems, the cooperation with national guideline groups and international collaborations were key for efficiency. CONCLUSION: CEOsys provided a proof-of-concept for a functioning evidence ecosystem at the national level. Lessons learned include that similar networks should, among others, involve methodological and clinical key stakeholders early on, aim for (inter)national collaborations, and systematically evaluate their value. We particularly call for a sustainable network.

Generalizability in real-world trials.

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.

Alarm Management in Intensive Care: Qualitative Triangulation Study.

BACKGROUND: The 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. OBJECTIVE: This study investigated the interaction of nurses and physicians with the patient monitoring system, their perceptions of alarm management, and smart alarm management solutions. METHODS: This 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. RESULTS: Nurses 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. CONCLUSIONS: Staff 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.

COVID-19 Vaccine Effectiveness and Digital Pandemic Surveillance in Germany (eCOV Study): Web Application-Based Prospective Observational Cohort Study.

BACKGROUND: The 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. OBJECTIVE: This 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. METHODS: Using 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. RESULTS: We 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. CONCLUSIONS: Our 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.

Assessment of risk factors for adverse events in analgosedation for pediatric endoscopy: A 10-year retrospective analysis.

OBJECTIVES: Data regarding the occurrence of complications specifically during pediatric anesthesia for endoscopic procedures is limited. By evaluating such data, factors could be identified to assure proper staffing and preparation to minimize adverse events and improve patient safety during flexible endoscopy. METHODS: This retrospective cohort study included children undergoing anesthesia for gastroscopy, colonoscopy, bronchoscopy, or combined endoscopic procedures over 10-year period. The primary study aim was to evaluate the incidence of complications and identify risk factors for adverse events. RESULTS: Overall, 2064 endoscopic procedures including 1356 gastroscopies (65.7%), 93 colonoscopies (4.5%), 235 bronchoscopies (11.4%), and 380 combined procedures (18.4%) were performed. Of the 1613 patients, 151 (7.3%) patients exhibited an adverse event, with respiratory complications being the most common (65 [3.1%]). Combination of gastrointestinal endoscopies did not lead to an increased adverse event rate (gastroscopy: 5.5%, colonoscopy: 3.2%). Diagnostic endoscopy as compared to interventional had a lower rate. If bronchoscopy was performed, the rate was similar to that of bronchoscopy alone (19.5% vs. 20.4%). Age < 5.8 years or body weight less than 20 kg, bronchoscopy, American Society of Anesthesiologists status ≥ 2 or pre-existing anesthesia-relevant diseases, and urgency of the procedure were independent risk factors for adverse events. For each risk factor, the risk for events increased 2.1-fold [1.8-2.4]. CONCLUSIONS: This study identifies multiple factors that increase the rate of adverse events associated anesthesia-based endoscopy. Combined gastrointestinal procedures did not increase the risk for adverse events while combination of bronchoscopy to gastrointestinal endoscopy showed a similar risk as bronchoscopy alone.

Sex and Age Characteristics in Acute or Chronic Myocarditis A Descriptive, Multicenter Cohort Study.

BACKGROUND: Understanding the clinical features of myocarditis in various age groups is required to identify age-specific disease patterns. OBJECTIVES: The objective of this study was to examine differences in sex distribution and clinical outcomes in patients with myocarditis of various ages. METHODS: Patients with acute or chronic myocarditis in 3 centers in Berlin, Germany from 2005 to 2021 and in the United States (National Inpatient Sample) from 2010 to 2019 were included. Age groups examined included "prepubescent" (below 11 years for females and below 13 years for males), adolescents (11 [female] or 13 [male] to 18 years), young adults (18-35 years), "middle-aged adults" (35-54 years), and older adults (age >54 years). In patients admitted to the hospital, hospital mortality, length of stay, and medical complication rates were examined. RESULTS: Overall, 6,023 cases in Berlin and 9,079 cases in the U.S. cohort were included. In both cohorts, there were differences in sex distribution among the 5 age categories, and differences in the distribution were most notable in adolescents (69.3% males vs 30.7% females) and in young adults (73.8% males vs 26.3% females). Prepubescent and older adults had the highest rates of in-hospital mortality, hospital length of stay, and medical complications. In the Berlin cohort, prepubescent patients had higher levels of leukocytes (P < 0.001), antistreptolysin antibody (P < 0.001), and NT-proBNP (P < 0.001) when compared to young adults. CONCLUSIONS: In this study, we found that sex differences in myocarditis and clinical features of myocarditis were age-dependent.