Background
Switching from controlled to assisted ventilation is crucial in the trajectory of intensive care unit (ICU) stay, but no guidelines exist. We described current practices, analyzed patient characteristics associated with switch success or failure, and explored the feasibility to predict switch failure.

Methods
In this retrospective study, we obtained highly granular longitudinal ICU data sets from three medical centers, covering demographics, severity scores, vital signs, ventilation, and laboratory parameters. The primary endpoint was switch success, considering a switch attempt to be successful if a patient did not return to controlled ventilation for the next 72 h while alive, and to be failed otherwise. We compared the characteristics of patients with successful vs. failed first switch attempts at ICU admission, immediately before, and 3 h after the attempt. We trained LASSO logistic regression models to predict switch failure.

Results
In 4524/6715 (67%) patients attempting a switch, the first attempt failed. The first switch attempt, regardless of success or failure, was generally made at normalized PaCO2 and pH levels, with PEEP < 10 cmH2O and PaO2/FiO2 indicating mild injury. Despite very similar baseline disease severity, switch failure was associated with significantly worse outcomes, including a 28-day mortality of 27% vs. 16% and median ventilator-free days of 16 vs. 22 (p < 0.001). Failed attempts were initiated significantly earlier than successful ones (median 1.8 vs. 1.3 days, p < 0.001). Before the switch, PaO2/FiO2, if measured at PEEP > 10 cmH2O, and respiratory system compliance was lower in patients with switch failure (median 185 vs. 205 mmHg, p < 0.001; 39 vs. 41 mL/cmH2O, P = 0.001), and post-switch, patients with switch failure experienced greater deterioration in gas exchange and minimal improvement in ventilatory parameters post-switch. Contrary to our hypotheses, patient characteristics for failed vs. successful switches were surprisingly similar, resulting in prediction models with limited discriminative performance.

Conclusions
Approximately two-thirds of attempts to switch patients to assisted ventilation fail, which are associated with significantly worse clinical outcomes, despite similar baseline disease severity. Contrary to our hypotheses, patients with successful and failed attempts showed similar characteristics, making switch failure difficult to predict. These findings underscore the importance of preventing switch failures and, given the retrospective nature of this study, highlight the need for prospective studies to better understand the reasons for switch failure and when spontaneous breathing can be safely initiated.

Aims
The sound environment, or soundscape, of intensive care units (ICUs) can be stressful for patients. Soundscapes are defined as acoustic environments as perceived by people. Single-patient rooms mitigate noise, but may deprive patients of essential auditory cues. This might harm basic psychological needs, such as safety. Experiences, needs and emotions regarding soundscapes of single-patient ICU rooms remain unexplored. We aimed to understand how patients experienced these soundscapes.

Methods
This mixed-methods, single-center study involved semi-structured interviews and questionnaires five days after ICU discharge. Patients experienced the soundscapes during their ICU stay, shared experiences in interviews, and selected one to rate on need fulfilment (with a researcher-developed questionnaire), and emotions (with a validated questionnaire). Using thematic analysis, we analyzed interview transcripts by labelling sound-related experiences, sounds, and emotions.

Findings
We interviewed 26 patients. We labelled 259 sound-related experiences, 264 sounds, and 281 emotions, from which six themes emerged: Orientation through sound; Coping with disruptions; Human auditory presence; Monotony and variation; Associations and hallucinations; Communication behind closed doors. Eight patient-selected experiences involved positive emotions. Need fulfilment scores varied: scores were low for communication-related experiences, but relatively high for those involving human presence.

Conclusions
Our findings demonstrated that experiences with single-patient ICU room soundscapes can be positive or negative. For future implementation of such rooms, three insights merit consideration: positive sounds originating from corridors may be unnecessarily removed; alarms in single-patient rooms serve key supportive functions for patients; there is a lack of accessible and appropriate auditory stimulation. We recommend that these insights are taken into consideration to ensure more positive and restorative ICU stays.

Implications for Clinical Practice
In ICUs, balanced approaches that consider both positive and negative aspects of soundscapes may benefit patients in future interventions for noise mitigation. Diversifying the variety of sounds inside patient rooms could further support well-being.

Artificial Intelligence (AI) in healthcare holds transformative potential but faces critical challenges in ethical accountability and systemic inequities. Biases in AI models, such as lower diagnosis rates for Black women or gender stereotyping in Large Language Models, highlight the urgent need to address historical and structural inequalities in data and development processes. Disparities in clinical trials and datasets, often skewed toward high-income, English-speaking regions, amplify these issues. Moreover, the underrepresentation of marginalized groups among AI developers and researchers exacerbates these challenges. To ensure equitable AI, diverse data collection, federated data-sharing frameworks, and bias-correction techniques are essential. Structural initiatives, such as fairness audits, transparent AI model development processes, and early registration of clinical AI models, alongside inclusive global collaborations like TRAIN-Europe and CHAI, can drive responsible AI adoption. Prioritizing diversity in datasets and among developers and researchers, as well as implementing transparent governance will foster AI systems that uphold ethical principles and deliver equitable healthcare outcomes globally.

Due to the significant environmental impact of healthcare, there is an urgent need to accelerate its circular transition. We provide an overview of the ESCH-R project study design and methodology for accelerating the transition from a linear to a circular healthcare sector through the development and implementation of circular interventions in the Netherlands. Using a transdisciplinary approach, we will apply the 10-R ladder framework for a circular economy to hospitals. Methods are presented to analyze current clinical practices, policies and requirements for sustainable behavioral change, from material flows and operations to policy and regulations. We describe methods for the development of circular interventions, including business models, contract templates, and product redesigns. Finally, our approach to dissemination and education is presented. The described study design and methods can be used by other hospital (settings) to identify environmental hotspots for circular interventions in their own healthcare practice and for the cross-transfer of knowledge and anticipated challenges in implementing circular strategies. Ultimately, the ESCH-R project will deliver innovative, scalable approaches for hospitals to reduce procurement of raw materials, retain value of medical products, and reduce waste streams, CO2 emissions and pollution.

Background: Prolonged weaning from mechanical ventilation is associated with poor clinical outcome. Therefore, choosing the right moment for weaning and extubation is essential. Electrical Impedance Tomography (EIT) is a promising innovative lung monitoring technique, but its role in supporting weaning decisions is yet uncertain. We aimed to evaluate physiological trends during a T-piece spontaneous breathing trail (SBT) as measured with EIT and the relation between EIT parameters and SBT success or failure. Methods: This is an observational study in which twenty-four adult patients receiving mechanical ventilation performed an SBT. EIT monitoring was performed around the SBT. Multiple EIT parameters including the end-expiratory lung impedance (EELI), delta Tidal Impedance (ΔZ), Global Inhomogeneity index (GI), Rapid Shallow Breathing Index (RSBI_EIT), Respiratory Rate (RR_EIT) and Minute Ventilation (MV_EIT) were computed on a breath-by-breath basis from stable tidal breathing periods. Results: EELI values dropped after the start of the SBT (p < 0.001) and did not recover to baseline after restarting mechanical ventilation. The ΔZ dropped (p < 0.001) but restored to baseline within seconds after restarting mechanical ventilation. Five patients failed the SBT, the GI (p = 0.01) and transcutaneous CO₂ (p < 0.001) values significantly increased during the SBT in patients who failed the SBT compared to patients with a successful SBT. Conclusion: EIT has the potential to assess changes in ventilation distribution and quantify the inhomogeneity of the lungs during the SBT. High lung inhomogeneity was found during SBT failure. Insight into physiological trends for the individual patient can be obtained with EIT during weaning from mechanical ventilation, but its role in predicting weaning failure requires further study.

Intensive care units (ICUs) are the ultimate socio-technological environments known for their loud sound levels. Alarms, noise produced by patient support devices, loud conversations, physical interactions with tools and the environment in general contribute collectively to a sound quality generally perceived as low. This paper reports the results from a study conducted inside the old and new ICUs at Erasmus Medical Center, Rotterdam, the Netherlands. Sound level measurements have been collected continuously for three weeks in several matching rooms inside the old and new ICUs at full operational level at a one-year distance from one another. The results support the idea that changes in architectural layout, procedures, and culture have contributed to a significant decrease of the measured sound levels across all daily shifts, both inside patient rooms and in the nurse stations. We further discuss the results in light of a series of contextual considerations that emerged from interviews with healthcare providers (i.e., nurses and intensivists) who actively worked in both the old and the new ICU.

Objective. Electrical impedance tomography (EIT) produces clinical useful visualization of the distribution of ventilation inside the lungs. The accuracy of EIT-derived parameters can be compromised by the cardiovascular signal. Removal of these artefacts is challenging due to spectral overlapping of the ventilatory and cardiovascular signal components and their time-varying frequencies. We designed and evaluated advanced filtering techniques and hypothesized that these would outperform traditional low-pass filters. Approach. Three filter techniques were developed and compared against traditional low-pass filtering: multiple digital notch filtering (MDN), empirical mode decomposition (EMD) and the maximal overlap discrete wavelet transform (MODWT). The performance of the filtering techniques was evaluated (1) in the time domain (2) in the frequency domain (3) by visual inspection. We evaluated the performance using simulated contaminated EIT data and data from 15 adult and neonatal intensive care unit patients. Main result. Each filter technique exhibited varying degrees of effectiveness and limitations. Quality measures in the time domain showed the best performance for MDN filtering. The signal to noise ratio was best for DLP, but at the cost of a high relative and removal error. MDN outbalanced the performance resulting in a good SNR with a low relative and removal error. MDN, EMD and MODWT performed similar in the frequency domain and were successful in removing the high frequency components of the data. Significance. Advanced filtering techniques have benefits compared to traditional filters but are not always better. MDN filtering outperformed EMD and MODWT regarding quality measures in the time domain. This study emphasizes the need for careful consideration when choosing a filtering approach, depending on the dataset and the clinical/research question.

Together we must reduce the impact of the healthcare sector and shift towards a circular economy. This paper describes the shift of three ICU environmental hotspots: gloves, gowns, and CRRT bags.

Nurses rely on patient monitoring systems for care delivery in ICUs. Monitoring systems communicate information to nurses and alert them through audiovisual alarms. However, excessive numbers of alarms often interrupt nurses in their tasks, and desensitize them to alarms. The affective consequence of this problem is that nurses are annoyed and feel frustration towards monitoring alarms. This situation leads to stress on nurses and threatens patient safety. Literature on sound annoyance distinguishes between annoyance induced by bottom-up (perceptual) and top-down (cognitive) processing. Extensive research on perceptual annoyance informs us on how to alleviate the problem by better sound design. However, addressing the cognitive aspect requires a broader understanding of annoyance as a construct. To this end, in this paper we distinguish between the annoyance induced by sensory unpleasantness of alarm sounds, and annoyance induced by frequent task interruptions. We present a conceptual framework in which we can interpret nurses’ annoyance by monitoring alarms. We further present descriptive analysis of the occurrence frequency of patient monitoring alarms in a neonatal ICU to illustrate the current state with regards to alarms. We aim to support nurses’ organizational well- being by providing an alternative hypothesis to explaining nurses’ affective states caused by auditory alarms. Future research can benefit from this paper through understanding of the context and familiarizing with the cognitive processes relevant to processing of patient monitoring alarms.

Objective Although the role of artificial intelligence (AI) in medicine is increasingly studied, most patients do not benefit because the majority of AI models remain in the testing and prototyping environment. The development and implementation trajectory of clinical AI models are complex and a structured overview is missing. We therefore propose a step-by-step overview to enhance clinicians' understanding and to promote quality of medical AI research. Methods We summarised key elements (such as current guidelines, challenges, regulatory documents and good practices) that are needed to develop and safely implement AI in medicine. Conclusion This overview complements other frameworks in a way that it is accessible to stakeholders without prior AI knowledge and as such provides a step-by-step approach incorporating all the key elements and current guidelines that are essential for implementation, and can thereby help to move AI from bytes to bedside.