BACKGROUND: The underrepresentation of women in leadership remains a pervasive issue, prompting a critical examination of support mechanisms within professional settings. Previous studies have identified challenges women face, ranging from limited visibility to barriers to career advancement. This survey aims to investigate perceptions regarding the effectiveness of women\'s leadership programs, mentoring initiatives, and a specialized communication course. Particularly it specifically targets addressing the challenges encountered by professional women.
METHODS: This multi-center, observational, international online survey was developed in partnership between ESICM NEXT and the ESICM Diversity and Inclusiveness Monitoring Group for Healthcare. Invitations to participate were distributed to both females and men through emails and social networks. Data were collected from April 1, 2023, through October 1, 2023.
RESULTS: Out of 354 respondents, 90 were men (25.42%) and 264 were women (74.58%). Among them, 251 completed the survey, shedding light on the persistent challenges faced by women in leadership roles, with 10%-50% of respondents holding such positions. Women\'s assertiveness is viewed differently, with 65% recognizing barriers such as harassment. Nearly half of the respondent\'s experience interruptions in meetings. Only 47.4% receiving conference invitations, with just over half accepting them. A mere 12% spoke at ESICM conferences in the last three years, receiving limited support from directors and colleagues, indicating varied obstacles for female professionals. Encouraging family participation, reducing fees, providing childcare, and offering economic support can enhance conference involvement. Despite 55% applying for ESICM positions, barriers like mobbing, harassment, lack of financial support, childcare, and language barriers were reported. Only 14% had access to paid family leave, while 32% benefited from subsidized childcare. Participation in the Effective Communication Course on Career Advancement Goals and engagement in women\'s leadership and mentoring programs could offer valuable insights and growth opportunities. Collaborating with Human Resources and leadership allies is crucial for overcoming barriers and promoting women\'s career growth.
CONCLUSIONS: The urgency of addressing identified barriers to female leadership in intensive care medicine is underscored by the survey\'s comprehensive insights. A multifaceted and intersectional approach, considering sexism, structural barriers, and targeted strategies, is essential.

BACKGROUND: Artificial intelligence, through improved data management and automated summarisation, has the potential to enhance intensive care unit (ICU) care. Large language models (LLMs) can interrogate and summarise large volumes of medical notes to create succinct discharge summaries. In this study, we aim to investigate the potential of LLMs to accurately and concisely synthesise ICU discharge summaries.
METHODS: Anonymised clinical notes from ICU admissions were used to train and validate a prompting structure in three separate LLMs (ChatGPT, GPT-4 API and Llama 2) to generate concise clinical summaries. Summaries were adjudicated by staff intensivists on ability to identify and appropriately order a pre-defined list of important clinical events as well as readability, organisation, succinctness, and overall rank.
RESULTS: In the development phase, text from five ICU episodes was used to develop a series of prompts to best capture clinical summaries. In the testing phase, a summary produced by each LLM from an additional six ICU episodes was utilised for evaluation. Overall ability to identify a pre-defined list of important clinical events in the summary was 41.5 ± 15.2% for GPT-4 API, 19.2 ± 20.9% for ChatGPT and 16.5 ± 14.1% for Llama2 (p = 0.002). GPT-4 API followed by ChatGPT had the highest score to appropriately order a pre-defined list of important clinical events in the summary as well as readability, organisation, succinctness, and overall rank, whilst Llama2 scored lowest for all. GPT-4 API produced minor hallucinations, which were not present in the other models.
CONCLUSIONS: Differences exist in large language model performance in readability, organisation, succinctness, and sequencing of clinical events compared to others. All encountered issues with narrative coherence and omitted key clinical data and only moderately captured all clinically meaningful data in the correct order. However, these technologies suggest future potential for creating succinct discharge summaries.

Background Clinical role transition is known to pose a challenge to doctors in training. The intensive care unit (ICU) is noted to be a demanding workplace environment, although relatively little is known about the experience of doctors as they transition to intensive care medicine (ICM) at the postgraduate level. Thus, this study aimed to explore the experience of registrar-grade doctors undergoing this transition and to examine the interplay between personal and professional life at this time. Methodology This qualitative study was guided by transcendental phenomenology and multiple and multidimensional transitions theory (MMT). Data were collected via 11 semi-structured interviews and analysed using a six-step analysis. Peer debriefing and a reflexive diary were used. Results The experience of doctors undergoing the transition to ICM was found to be influenced by the clinical environment of the ICU, a demanding and potentially stressful workplace, and one in which collegial support was valued. The previous experience of the individual undergoing the transition was relevant, and the impact of this transition on their professional development and identity was notable. Consistent with MMT, the interplay between personal and professional life was significant, as participants outlined the impact of anxiety before a shift, the emotional and cognitive burden of a shift, and the effect of this transition on their personal relationships. Conclusions This study observes the ICU to be an extremely challenging workplace environment, with a notable influence on the personal lives of those working therein. Nevertheless, ICM offers invaluable opportunities for learning and both personal and professional growth.

OBJECTIVE: Perioperative in-hospital cardiac arrests (Perioperative IHCAs) may have better outcomes than IHCAs in the ward (Ward IHCAs), due to enhanced monitoring and faster response. However, quantitative comparisons of their long-term outcomes are lacking, posing challenges for prognostication.
METHODS: This retrospective multicentre study included adult intensive care unit (ICU) admissions from theatre/recovery or wards with a diagnosis of cardiac arrest between January 2018 and March 2022. We used data from 175 ICUs in the ANZICS adult patient database. The primary outcome was a survival time of up to 4 years. We used the Cox proportional hazards model adjusted for Sequential Organ Failure Assessment (SOFA) score, age, sex, comorbidities, hospital type, treatment limitation on admission to the ICU, and ICU treatments. Subgroup analyses examined age (≥ 65 years), intubation within the first 24 h, elective vs. emergency admission, and survival on discharge.
RESULTS: Of 702,675 ICU admissions, 5,659 IHCAs were included (Perioperative IHCA 38%; Ward IHCA 62%). Perioperative IHCA group were younger, less frail, and less comorbid. Perioperative IHCA were most frequent in patients admitted to ICU after cardiovascular, gastrointestinal, or trauma surgeries. Perioperative IHCA group had longer 4-year survival (59.9% vs. 33.0%, p < 0.001) than the Ward IHCA group, even after adjustments (adjusted hazard ratio [HR]: 0.63, 95% confidence interval [CI] 0.57-0.69). This was concordant across all subgroups. Of note, older patients with Perioperative IHCA survived longer than both younger and older patients with Ward IHCA.
CONCLUSIONS: Patients admitted to the ICU following Perioperative IHCA had longer survival than Ward IHCA. Future studies on IHCA should distinguish these patients.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic posed major challenges to the healthcare system worldwide and led to particular stress among healthcare workers. The aim of this analysis was to investigate the level of global mental stress of direct healthcare workers in Germany during the COVID-19 pandemic.
METHODS: In this prospective cross-sectional study with four measurement points (T1: 4-5/2020, T2:11/2020-1/2021, 5-7/2021, 2-5/2022), psychological distress symptoms were recorded in an online survey with the Patient Health Questionnaire (PHQ-4) among hospital staff working in direct patient care (N = 5408 datasets). The total dataset was exploratively analyzed according to field of activity, gender, and professional group affiliation.
RESULTS: Clinically relevant psychological distress (PHQ-4 ≥ 5) was present in 29.3% (n = 419/1429) of intensive care staff. A comparison of the four cross-sectional surveys showed a significant increase in the rate of clinically relevant mental distress in the first pandemic year (23.2% at T1 vs. 30.6% at T2; p < 0.01), which stabilized at a high level in the second pandemic year (33.6% at T3 and 32.0% at T4). Women did not differ from men in this respect (n = 280/919, 30.4% vs. n = 139/508, 27.4%; p = 0.74). Nursing staff were significantly more often psychologically stressed than physicians (n = 339/1105, 30.7% vs. n = 80/324, 24.7%; p = 0.03). Intensive care staff did not show significantly higher stress than staff working in nonintensive care areas (n = 419/1429, 29.3% vs. n = 1149/3979, 28.7%, p = 0.21).
CONCLUSIONS: German healthcare workers reported high levels of mental distress during the pandemic, which increased during the course of the pandemic, but no significant difference was found between intensive care and nonintensive care staff in our sample. This may be due to the fact that the pandemic in Germany was comparatively moderate internationally and neither a collapse of the healthcare system in general nor a collapse of intensive care structures in particular took place.
UNASSIGNED: HINTERGRUND: Die Coronavirus(COVID-19)-Pandemie hat das Gesundheitswesen weltweit vor große Herausforderungen gestellt und zu besonderen Belastungen des medizinisch tätigen Personals geführt. Das Ziel dieser Analyse war es zu untersuchen, wie hoch in Deutschland die globale psychische Belastung von in der direkten Krankenversorgung tätigem Personal im Verlauf der COVID-19-Pandemie war.
METHODS: In dieser prospektiven Querschnittstudie mit vier Messzeitpunkten (T1: 4–5/2020, T2: 11/2020–1/2021, T3: 5–7/2021, T4: 2–5/2022) wurden psychische Belastungssymptome im Rahmen eines Online-Surveys mit dem Patient Health Questionnaire (PHQ-4) bei Krankenhauspersonal, das in der direkten Patientenversorgung tätig war, erfasst (N = 5408 Datensätze). Der Gesamtdatensatz wurde explorativ nach Tätigkeitsbereich, Geschlecht und Berufsgruppenzugehörigkeit analysiert sowie der Verlauf über die vier Messzeitpunkte hinweg betrachtet.
UNASSIGNED: Eine klinisch relevante psychische Belastung (PHQ-4 ≥ 5) lag bei 29,3 % (n = 419/1429) des intensivmedizinischen Personals vor. Im Vergleich der vier Querschnittserhebungen zeigte sich eine signifikante Steigerung der Rate klinisch relevanter psychischer Belastung im ersten Pandemiejahr (23,2 % zu T1 vs. 30,6 % zu T2; p < 0,01), welche sich im zweiten Pandemiejahr auf hohem Niveau stabilisierte (33,6 % zu T3 bzw. 32,0 % zu T4). Frauen unterschieden sich dabei nicht von den Männern (n = 280/919 vs. n = 139/508 bzw. 30,4 % vs. 27,4 %; p = 0,74). Pflegekräfte waren signifikant häufiger psychisch belastet als ärztliches Personal (n = 339/1105 vs. n = 80/324 bzw. 30,7 % vs. 24,7 %; p = 0,03). Das Intensivpersonal wies keine signifikant höhere Belastung auf als das in nichtintensivmedizinischen Funktionsbereichen tätige Personal (n = 419/1429 vs. n = 1149/3979 bzw. 29,3 % vs. 28,7 %, p = 0,21).
UNASSIGNED: Mitarbeitende im deutschen Gesundheitswesen gaben in der Pandemie eine hohe und im Verlauf ansteigende psychische Belastung an, jedoch fand sich in unserer Stichprobe kein signifikanter Unterschied zwischen intensivmedizinischem und nichtintensivmedizinischem Personal. Dies ist möglicherweise dadurch bedingt, dass die Pandemie in Deutschland international vergleichsweise moderat verlief und weder ein Kollaps des Gesundheitssystems im Allgemeinen noch eine Dekompensation des intensivmedizinischen Versorgungssektors im Besonderen stattfand.

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UNASSIGNED: This article describes the statistical analysis plan for the Biomarker-guided intervention to prevent AKI after major surgery (BigpAK-2) trial.
UNASSIGNED: Adaptive trial design with an interim analysis after enrolment of 618 evaluable patients.
UNASSIGNED: The BigpAK.-2 trial is an international, prospective, randomised controlled multicentre study.
UNASSIGNED: The BigpAK-2 study enrols patients after major surgery who are admitted to the intensive care or high dependency unit and are at high-risk for postoperative AKI as identified by urinary biomarkers (tissue inhibitor of metalloproteinases-2 and insulin-like growth factor binding protein 7 ([TIMP-2]∗[IGFBP7]) will be enrolled.
UNASSIGNED: Patients are randomly and evenly allocated to standard of care (control) group or the implementation of a nephroprotective care bundle (intervention group), as recommended by the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. The KDIGO care bundle recommends discontinuation of nephrotoxic agents if possible, ensuring adequate volume status and perfusion pressure, considering functional haemodynamic monitoring, regular monitoring of serum creatinine and urine output, avoiding hyperglycemia, and considering alternatives to radiocontrast procedures when possible.
UNASSIGNED: The BigpAK-2 study investigates whether the biomarker-gudied implementation of the KDIGO care bundle reduces the incidence of moderate or severe AKI (stage 2 or 3), according to the KDIGO 2012 criteria, within 72 h after surgery.
UNASSIGNED: AKI is a common and often severe complication after major surgery. As no specific treatments exist, prevention of AKI is of high importance. The BigpAK-2 study investigates a promising approach to prevent AKI after major surgery.
UNASSIGNED: The trial was registered prior to start at clinicaltrials.gov; NCT04647396.

UNASSIGNED: Patient-ventilator asynchrony (PVA) frequently occurs in mechanically ventilated patients within the ICU and has the potential for harm. Depending solely on the health care team cannot accurately and promptly identify PVA. To address this issue, our team has developed a cloud-based platform for monitoring mechanical ventilation (MV), comprising the PVA-RemoteMonitor system and the 24-h MV analysis report. We conducted a survey to evaluate physicians\' satisfaction and acceptance of the platform in 14 ICUs.
UNASSIGNED: Data from medical records, clinical information systems, and ventilators were uploaded to the cloud platform and underwent data processing. The data were analyzed to monitor PVA and displayed in the front-end. The 24-h analysis report for MV was generated for clinical reference. Critical care physicians in 14 hospitals\' ICUs that involved in the platform participated in a questionnaire survey, among whom 10 physicians were interviewed to investigate physicians\' acceptance and opinions of this system.
UNASSIGNED: The PVA-RemoteMonitor system exhibited a high level of specificity in detecting flow insufficiency, premature cycle, delayed cycle, reverse trigger, auto trigger, and overshoot, with sensitivities of 90.31 %, 98.76 %, 99.75 %, 99.97 %, 100 %, and 99.69 %, respectively. The 24-h analysis report supplied essential data about PVA and respiratory mechanics. 86.2 % (75/87) of physicians supported the application of this platform.
UNASSIGNED: The PVA-RemoteMonitor system accurately identified PVA, and the MV analysis report provided guidance in controlling PVA. Our platform can effectively assist ICU physicians in the management of ventilated patients.

OBJECTIVE: Delirium, an acute confusional state, affects 20-80% of patients in Intensive Care Units (ICUs), one in three medically hospitalized patients, and up to 50% of all patients who have had surgery. Its development is associated with short- and long-term morbidity, and increased risk of death. Yet, we lack any rapid, objective, and automated method to diagnose delirium. Here, we detail the prospective deployment of a novel dual-camera contextual eye-tracking platform. We then use the data from this platform to contemporaneously classify delirium.
RESULTS: We recruited 42 patients, resulting in 210 (114 with delirium, 96 without) recordings of hospitalized patients in ICU across two centers, as part of a prospective multi-center feasibility pilot study. All recordings made with our platform were usable for analysis. We divided the collected data into training and validation cohorts based on the data originating center. We trained two Temporal Convolutional Network (TCN) models that can classify delirium using a pre-existing manual scoring system (Confusion Assessment Method in ICU (CAM-ICU)) as the training target. The first model uses eye movements only which achieves an Area Under the Receiver Operator Curve (AUROC) of 0.67 and a mean Average Precision (mAP) of 0.68. The second model uses the point of regard, the part of the scene the patient is looking at, and increases the AUROC to 0.76 and the mAP to 0.81. These models are the first to classify delirium using continuous non-invasive eye-tracking but will require further clinical prospective validation prior to use as a decision-support tool.
CONCLUSIONS: Eye-tracking is a biological signal that can be used to identify delirium in patients in ICU. The platform, alongside the trained neural networks, can automatically, objectively, and continuously classify delirium aiding in the early detection of the deteriorating patient. Future work is aimed at prospective evaluation and clinical translation.

UNASSIGNED: Acute kidney injury (AKI) is a common complication in patients undergoing major vascular surgery. Despite significant research efforts in this area, the incidence of AKI remains high, posing a significant challenge to healthcare systems, especially in situations where resources are limited. Early prediction of AKI severity and individualized postoperative care is therefore essential.
UNASSIGNED: The primary objective of this exploratory study was to assess the diagnostic value of urine cell-cycle arrest biomarkers [(TIMP-2) × (IGFBP7)] and soluble urokinase plasminogen activator receptor (suPAR) for predicting moderate or severe AKI within 24 h after open aortic surgery, and compared to routine kidney biomarkers. Seventy-five patients undergoing elective aortic surgery were included. Clinical parameters, urine and blood samples were collected preoperatively, immediately postoperatively, and 24 h later. AKI was defined using KDIGO criteria. Individual and combined diagnostic performance of biomarkers were evaluated.
UNASSIGNED: Of the 75 patients, 61% developed AKI, of which 28% developed moderate or severe AKI within 24 h of surgery. Baseline demographics, comorbidities and kidney parameters did not differ between patients with moderate or severe AKI (AKI II/III) and none or mild AKI (AKI 0/I), except for higher preoperative suPAR levels in later AKI II/III patients. Urine osmolality, Cystatin C and serum creatinine had the highest predictive power for AKI II/III with AUCs of 0.75-0.72. (TIMP-2) × (IGFBP7), and neither (TIMP-2) × (IGFBP7) nor suPAR individually showed superior diagnostic value. Combining CysC or SCr with urine osmolality and 6 h urine output gave the best performance with AUCs of 0.86 (95% CI, 0.74-0.96) and 0.85 (95% CI, 0.75-0.95) respectively.
UNASSIGNED: Our study suggests that routine parameters like urine osmolality, CysC, SCr and 6 h urine output perform best in predicting postoperative AKI after aortic surgery compared to the new biomarkers (TIMP-2) × (IGFBP7) and suPAR. Combining biomarkers, particularly CysC or SCr with urine output, urine osmolality, may enhance diagnostic accuracy. Further validation in larger cohorts and clinical settings is warranted to establish their clinical utility.