Background: Due to demographic change and, more recently, coronavirus disease (COVID-19), the importance of modern intensive care units (ICU) is becoming apparent. One of the key components of an ICU is the continuous monitoring of patients' vital parameters. However, existing advances in informatics, signal processing, or engineering that could alleviate the burden on ICUs have not yet been applied. This could be due to the lack of user involvement in research and development. Objective: This study focused on the satisfaction of ICU staff with current patient monitoring and their suggestions for future improvements. We aimed to identify aspects of monitoring that interrupt patient care, display devices for remote monitoring, use cases for artificial intelligence (AI), and whether ICU staff members are willing to improve their digital literacy or contribute to the improvement of patient monitoring. We further aimed to identify differences in the responses of different professional groups. Methods: This survey study was performed with ICU staff from 4 ICUs of a German university hospital between November 2019 and January 2020. We developed a web-based 36-item survey questionnaire, by analyzing a preceding qualitative interview study with ICU staff, about the clinical requirements of future patient monitoring. Statistical analyses of questionnaire results included median values with their bootstrapped 95% confidence intervals, and chi-square tests to compare the distributions of item responses of the professional groups. Results: In total, 86 of the 270 ICU physicians and nurses completed the survey questionnaire. The majority stated they felt confident using the patient monitoring equipment, but that high rates of false-positive alarms and the many sensor cables interrupted patient care. Regarding future improvements, respondents asked for wireless sensors, a reduction in the number of false-positive alarms, and hospital standard operating procedures for alarm management. Responses to the display devices proposed for remote patient monitoring were divided. Most respondents indicated it would be useful for earlier alerting or when they were responsible for multiple wards. AI for ICUs would be useful for early detection of complications and an increased risk of mortality; in addition, the AI could propose guidelines for therapy and diagnostics. Transparency, interoperability, usability, and staff training were essential to promote the use of AI. The majority wanted to learn more about new technologies for the ICU and required more time for learning. Physicians had fewer reservations than nurses about AI-based intelligent alarm management and using mobile phones for remote monitoring. Conclusions: This survey study of ICU staff revealed key improvements for patient monitoring in intensive care medicine. Hospital providers and medical device manufacturers should focus on reducing false alarms, implementing hospital alarm standard operating procedures, introducing wireless sensors, preparing for the use of AI, and enhancing the digital literacy of ICU staff. Our results may contribute to the user-centered transfer of digital technologies into practice to alleviate challenges in intensive care medicine. Trial Registration: ClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173

Background: Cardiac surgery patients represent a high-risk cohort in intensive care units (ICUs). Central venous pressure (CVP) measurement seems to remain an integral part in hemodynamic monitoring, especially in cardio-surgical ICUs. However, its value as a prognostic marker for organ failure is still unclear. Therefore, we analyzed postoperative CVP values after adult cardiac surgery in a large cohort with regard to its prognostic value for morbidity and mortality. Methods: All adult patients admitted to our ICUs between 2006 and 2019 after cardiac surgery were eligible for inclusion in the study (n = 11,198). We calculated the median initial CVP (miCVP) after admission to the ICU, which returned valid values for 9802 patients. An ROC curve analysis for optimal cut-off miCVP to predict ICU mortality was conducted with consecutive patient allocation into a (a) low miCVP (LCVP) group (≤11 mmHg) and (b) high miCVP (HCVP) group (>11 mmHg). We analyzed the impact of high miCVP on morbidity and mortality by propensity score matching (PSM) and logistic regression. Results: ICU mortality was increased in HCVP patients. In addition, patients in the HCVP group required longer mechanical ventilation, had a higher incidence of acute kidney injury, were more frequently treated with renal replacement therapy, and showed a higher risk for postoperative liver dysfunction, parametrized by a postoperative rise of ≥ 10 in MELD Score. Multiple regression analysis confirmed HCVP has an effect on postoperative ICU-mortality and intrahospital mortality, which seems to be independent. Conclusions: A high initial CVP in the early postoperative ICU course after cardiac surgery is associated with worse patient outcome. Whether or not CVP, as a readily and constantly available hemodynamic parameter, should promote clinical efforts regarding diagnostics and/or treatment, warrants further investigations.

Background: Etomidate is typically used as an induction agent in cardiac surgery because it has little impact on hemodynamics. It is a known suppressor of adrenocortical function and may increase the risk for post-operative infections, sepsis, and mortality. The aim of this study was to evaluate whether etomidate increases the risk of postoperative sepsis (primary outcome) and infections (secondary outcome) compared to propofol. Methods: This was a retrospective before–after trial (IRB EA1/143/20) performed at a tertiary medical center in Berlin, Germany, between 10/2012 and 01/2015. Patients undergoing cardiac surgery were investigated within two observation intervals, during which etomidate and propofol were the sole induction agents. Results: One-thousand, four-hundred, and sixty-two patients, and 622 matched pairs, after caliper propensity-score matching, were included in the final analysis. Sepsis rates did not differ in the matched cohort (etomidate: 11.5% vs. propofol: 8.2%, p = 0.052). Patients in the etomidate interval were more likely to develop hospital-acquired pneumonia (etomidate: 18.6% vs. propofol: 14.0%, p = 0.031). Conclusion: Our study showed that a single-dose of etomidate is not statistically associated with higher postoperative sepsis rates after cardiac surgery, but is associated with a higher incidence of hospital-acquired pneumonia. However, there is a notable trend towards a higher sepsis rate.

BACKGROUND Due to demographic change and, more recently, coronavirus disease (COVID-19), the importance of modern intensive care units (ICU) is becoming apparent. One of the key components of an ICU is the continuous monitoring of patients' vital parameters. However, existing advances in informatics, signal processing, or engineering that could alleviate the burden on ICUs have not yet been applied. This could be due to the lack of user involvement in research and development. OBJECTIVE This study focused on the satisfaction of ICU staff with current patient monitoring and their suggestions for future improvements. We aimed to identify aspects of monitoring that interrupt patient care, display devices for remote monitoring, use cases for artificial intelligence (AI), and whether ICU staff members are willing to improve their digital literacy or contribute to the improvement of patient monitoring. We further aimed to identify differences in the responses of different professional groups. METHODS This survey study was performed with ICU staff from 4 ICUs of a German university hospital between November 2019 and January 2020. We developed a web-based 36-item survey questionnaire, by analyzing a preceding qualitative interview study with ICU staff, about the clinical requirements of future patient monitoring. Statistical analyses of questionnaire results included median values with their bootstrapped 95% confidence intervals, and chi-square tests to compare the distributions of item responses of the professional groups. RESULTS In total, 86 of the 270 ICU physicians and nurses completed the survey questionnaire. The majority stated they felt confident using the patient monitoring equipment, but that high rates of false-positive alarms and the many sensor cables interrupted patient care. Regarding future improvements, respondents asked for wireless sensors, a reduction in the number of false-positive alarms, and hospital standard operating procedures for alarm management. Responses to the display devices proposed for remote patient monitoring were divided. Most respondents indicated it would be useful for earlier alerting or when they were responsible for multiple wards. AI for ICUs would be useful for early detection of complications and an increased risk of mortality; in addition, the AI could propose guidelines for therapy and diagnostics. Transparency, interoperability, usability, and staff training were essential to promote the use of AI. The majority wanted to learn more about new technologies for the ICU and required more time for learning. Physicians had fewer reservations than nurses about AI-based intelligent alarm management and using mobile phones for remote monitoring. CONCLUSIONS This survey study of ICU staff revealed key improvements for patient monitoring in intensive care medicine. Hospital providers and medical device manufacturers should focus on reducing false alarms, implementing hospital alarm standard operating procedures, introducing wireless sensors, preparing for the use of AI, and enhancing the digital literacy of ICU staff. Our results may contribute to the user-centered transfer of digital technologies into practice to alleviate challenges in intensive care medicine. CLINICALTRIAL ClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173

Background: Clinical practice guidelines are important tools for clinical decision support, but monitoring guideline adherence manually is highly resource-intensive. Therefore, we developed an automated system for evaluating guideline adherence based on computer-interpretable representations of guidelines. We implemented the system across multiple university hospitals and assessed its validity and performance by comparing its guideline adherence evaluations to those conducted by medical professionals. Methods: We selected six representative clinical guideline recommendations from across 41 intensive care guidelines and translated these text-based recommendations into a computer-interpretable, Fast Healthcare Interoperability Resources (FHIR)-based format using an iterative consensus approach. Clinical data from five university hospitals were transformed into the Observational Medical Outcomes Partnership (OMOP) common data model. A decision support system was developed to interpret FHIR-encoded recommendations and apply them to OMOP-formatted patient data. We evaluated the system retrospectively on intensive care data covering 3.5 years and validated its performance by comparing system-generated decisions with human decisions in three hospitals. We created and iteratively refined a user interface for individual and ward-level adherence visualization. Findings: We expert-reviewed more than 18,000 patient days to assess the applicability of and adherence to the recommendations. The system demonstrated 97.0% accuracy in identifying guideline applicability and adherence, with significantly higher accuracy than human reviewers (accuracy 86.6%, p<0.001, McNemar’s Test). The automated system processed more than 2000 patient days per second for a total of 2,200,000 patient days across 82,000 intensive care episodes, compared to humans’ two patient days per minute. Interpretation: We demonstrate that an automated adherence monitoring system outperforms human reviewers in both accuracy and time efficiency. Using FHIR-encoded recommendations enables flexibility and scalability across hospitals with different data infrastructures. Future work should focus on integrating unstructured patient data and expanding the range of encoded recommendations. Funding: Federal Ministry of Education and Research of Germany.

Background: Clinical guidelines (CGs) standardize care through evidence-based recommendations, while clinical decision support systems (CDSS) can assist in applying these guidelines to individual patients. The scientific basis for the decisions offered by decision support systems is often not explicitly stated or not clearly specified in the literature on CDSS. Therefore, a systematic examination of the literature is needed to map the current state of CDSS, with a particular focus on the integration of CGs. Objective: This study aims to systematically collect, describe, and synthesize evidence of randomized controlled studies of interventions using CDSS with a well-defined integration of evidence-based CGs and evaluating direct medical outcomes. Methods: This systematic review adheres to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklists. The eligibility criteria for this review are defined using the patient, intervention, control, outcome, and study design framework, including studies involving patients with any medical condition or disease. Study interventions need to include guideline-based CDSS, encompassing all types of interventions used for treatment. Each intervention must provide a sufficiently accessible technical description, including the types of data and algorithms used for decision-support procedures. The guidelines used within these CDSS interventions must be derived from a clearly defined, evidence-based guideline development process published by a discernible guideline-producing body. Studies must use a randomized controlled study design. Only studies evaluating the effectiveness of the interventions on direct medical outcomes are included. Web of Science, including MEDLINE, and Scopus will be searched with search expressions aligned with the eligibility criteria. Results: On August 11, 2022, the initial search was conducted on Web of Science and Scopus. From a total of 6203 records, 1347 were removed prior to screening as duplicates, 2506 records were excluded during the first screening step, and 2291 were excluded during the second step. Next, 41 papers were excluded based on full-text review, and 18 papers were finally included in the review following this initial search. This review explores whether CDSS based on CGs can improve clinical outcomes, although their effectiveness may vary depending on various factors. Potential limitations, such as high study heterogeneity, have already been identified. An update of the review has been started in April 2025. Conclusions: To our knowledge, this is the first rigorous systematic review on the effectiveness of guideline-based decision support systems in which the technical integration and algorithmic embedding of CGs have been described or can be inferred from secondary literature. With this review, we aim to address this gap by providing a detailed analysis of existing research and identifying best practices, challenges, and areas for future investigation.