Clustering of critically ill patients using an individualized learning approach enables dose optimization of mobilization in the ICU

Identification and quantification of physical activity in critically ill burn patients: A feasibility study

BACKGROUND

Physical activity is essential in burn care to counteract the effects of severe burns and inactivity during hospitalization. However, detailed knowledge of performed physical activities is lacking. This study evaluated the feasibility of a dual accelerometer-based method to assess type, frequency, and duration of physical activity in critically ill burn patients during hospitalization.

METHODS

A prospective observational study was conducted at the burn center of the Martini Hospital, Groningen, The Netherlands. Eligible were patients with a total body surface area (TBSA) burned of ≥ 15 % or an indication for intensive care. Patients wore two accelerometers, one on the chest and one on the diagonally opposite thigh. An algorithm converted accelerometer data into type, frequency, and duration of activities common for intensive care patients. An activity diary was used to assess non-wear time and its content, e.g., surgery.

RESULTS

Five patients (20-60 years, 13-31 % TBSA burned, LOS 30-65 days) were included. Per patient, 14-49 days (17,380-61,796 min) could be analyzed of which 7-14 % was non-wear time. During wear time, 86-95 % of activities could be identified and quantified. However, processing the data was labor-intensive.

CONCLUSION

The dual accelerometer-based method proved feasible for research purposes. For clinical application, further refinement of data processing is required.

Collaborative International Nursing Writing Group: A Nonpatriarchal Approach

This paper offers a critical discussion of the evolution and impact of an international nursing writing group, developed to support nursing faculty and academics globally. Amid the challenges posed by the COVID-19 pandemic, the writing group adapted and thrived online with a flattened power structure, and shared influence and acknowledgment of mutual capacity and contribution. The writing group attracted participants from various countries, with international members enriching collaboration, and fostering a global network of nursing scholars. By embracing diverse perspectives and promoting nonpatriarchal approaches to scholarly writing, the International Writing Group became a unique and valuable platform for academic growth and cross-cultural exchange in nursing.

Machine learning methods in physical therapy: A scoping review of applications in clinical context

BACKGROUND

Machine learning (ML) efficiently processes large datasets, showing promise in enhancing clinical practice within physical therapy.

OBJECTIVE

The aim of this scoping review is to provide an overview of studies using ML approaches in clinical settings of physical therapy.

DATA SOURCES

A scoping review was performed in PubMed, EMBASE, PEDro, Cochrane, Web of Science, and Scopus.

SELECTION CRITERIA

We included studies utilizing ML methods. ML was defined as the utilization of computational systems to encode patterns and relationships, enabling predictions or classifications with minimal human interference.

DATA EXTRACTION AND DATA SYNTHESIS

Data were extracted regarding methods, data types, performance metrics, and model availability.

RESULTS

Forty-two studies were included. The majority were published after 2020 (n = 25). Fourteen studies (33.3%) were in the musculoskeletal physical therapy field, nine (21.4%) in neurological, and eight (19%) in sports physical therapy. We identified 44 different ML models, with random forest being the most used. Three studies reported on model availability. We identified several clinical applications for ML-based tools, including diagnosis (n = 14), prognosis (n = 7), treatment outcomes prediction (n = 7), clinical decision support (n = 5), movement analysis (n = 4), patient monitoring (n = 3), and personalized care plan (n = 2).

LIMITATION

Model performance metrics, costs, model interpretability, and explainability were not reported.

CONCLUSION

This scope review mapped the emerging landscape of machine learning applications in physical therapy. Despite the growing interest, the field still lacks high-quality studies on validation, model availability, and acceptability to advance from research to clinical practice.

Evaluating optimal rehabilitation strategies in ICU: study protocol for a multicentre cohort study to assess Physical Activity dosing, Muscle mass, and physICal outcomeS (IPAMICS study)

BACKGROUND

Many patients who get discharged from the intensive care unit experience physical dysfunction that persists even after discharge. Physical dysfunction is associated with skeletal muscle atrophy and accompanying intensive care unit-acquired weakness in the early stages of intensive care unit admission, and early diagnosis and prevention with early mobilization are crucial. However, the amount of physical activity required for early mobilization remains controversial in critically ill patients. This study aims to reveal the optimal mobilization quantification score dose associated with physical dysfunction after hospital discharge.

METHODS

This is a multicenter prospective cohort study planned in 22 facilities; all consecutive patients admitted to the participating facilities between June 2024 and May 2025 will be included. Adult patients on ventilator management for at least 2 days and who will consent to this study will be included. Patients' mobility level and duration will be documented by the mobilization quantification score during their intensive care unit stay, and physical dysfunction will be assessed using muscle mass changes from day one to seven with ultrasonography and the Short-Form 12 Health Survey at 3 months after hospital discharge. The primary outcome is physical dysfunction at 3 months.

RESULTS AND CONCLUSION

Mobilization quantification score dose and muscle mass evaluation with ultrasonography will enable the quantification of the early mobilization intervention. This study will lay the foundation for future randomised studies.

Artificial intelligence and machine learning: Definition of terms and current concepts in critical care research

With increasing computing power, artificial intelligence (AI) and machine learning (ML) have prospered, which facilitate the analysis of large datasets, especially those found in critical care. It is important to define these terminologies, to inform a standardized approach to critical care research. This manuscript hopes to clarify these terms with examples from medical literature. Three major components that are required for a successful ML implementation: (i) reliable dataset, (ii) ML algorithm, and (iii) unbiased model evaluation, are discussed. A reliable dataset can be structured or unstructured with limited noise, outliers, and missing values. ML, a subset of AI, is typically focused on supervised or unsupervised learning tasks in which the output is based on inputs and derived from iterative pattern recognition algorithms, while AI is the overall ability of a machine to "think" or mimic human behavior; and to analyze data free from human influence. Even with successful implementation, advanced AI and ML algorithms have faced challenges in adoption into practice, mainly due to their lack of interpretability, which hinders trust, buy-in, and engagement from clinicians. Consequently, traditional algorithms, such as linear and logistic regression, that may have reduced predictive power but are highly interpretable, continue to be widely used.

Cluster analysis of thoracic muscle mass using artificial intelligence in severe pneumonia

Severe pneumonia results in high morbidity and mortality despite advanced treatments. This study investigates thoracic muscle mass from chest CT scans as a biomarker for predicting clinical outcomes in ICU patients with severe pneumonia. Analyzing electronic medical records and chest CT scans of 778 ICU patients with severe community-acquired pneumonia from January 2016 to December 2021, AI-enhanced 3D segmentation was used to assess thoracic muscle mass. Patients were categorized into clusters based on muscle mass profiles derived from CT scans, and their effects on clinical outcomes such as extubation success and in-hospital mortality were assessed. The study identified three clusters, showing that higher muscle mass (Cluster 1) correlated with lower in-hospital mortality (8% vs. 29% in Cluster 3) and improved clinical outcomes like extubation success. The model integrating muscle mass metrics outperformed conventional scores, with an AUC of 0.844 for predicting extubation success and 0.696 for predicting mortality. These findings highlight the strong predictive capacity of muscle mass evaluation over indices such as APACHE II and SOFA. Using AI to analyze thoracic muscle mass via chest CT provides a promising prognostic approach in severe pneumonia, advocating for its integration into clinical practice for better outcome predictions and personalized patient management.

Exploring the Unknown: Evaluating ChatGPT's Performance in Uncovering Novel Aspects of Plastic Surgery and Identifying Areas for Future Innovation

BACKGROUND

Artificial intelligence (AI) has emerged as a powerful tool in various medical fields, including plastic surgery. This study aims to evaluate the performance of ChatGPT, an AI language model, in elucidating historical aspects of plastic surgery and identifying potential avenues for innovation.

METHODS

A comprehensive analysis of ChatGPT's responses to a diverse range of plastic surgery-related inquiries was performed. The quality of the AI-generated responses was assessed based on their relevance, accuracy, and novelty. Additionally, the study examined the AI's ability to recognize gaps in existing knowledge and propose innovative solutions. ChatGPT's responses were analysed by specialist plastic surgeons with extensive research experience, and quantitatively analysed with a Likert scale.

RESULTS

ChatGPT demonstrated a high degree of proficiency in addressing a wide array of plastic surgery-related topics. The AI-generated responses were found to be relevant and accurate in most cases. However, it demonstrated convergent thinking and failed to generate genuinely novel ideas to revolutionize plastic surgery. Instead, it suggested currently popular trends that demonstrate great potential for further advancements. Some of the references presented were also erroneous as they cannot be validated against the existing literature.

CONCLUSION

Although ChatGPT requires major improvements, this study highlights its potential as an effective tool for uncovering novel aspects of plastic surgery and identifying areas for future innovation. By leveraging the capabilities of AI language models, plastic surgeons may drive advancements in the field. Further studies are needed to cautiously explore the integration of AI-driven insights into clinical practice and to evaluate their impact on patient outcomes.

LEVEL OF EVIDENCE V

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Agonal breathing upon hospital arrival as a prognostic factor in patients experiencing out-of-hospital cardiac arrest

Background

Agonal breathing is a relatively common symptom that follows cardiac arrest when the brainstem function is preserved. Agonal breathing is associated with favorable survival in patients experiencing out-of-hospital cardiac arrest (OHCA). While previous studies focused on agonal breathing observed in the pre-hospital setting for all study subjects, we focused on agonal breathing observed upon hospital arrival. In this multicenter prospective study, we aimed to assess the prognosis of patients exhibiting agonal breathing upon hospital arrival were compared. We hypothesized that agonal breathing at hospital arrival would be associated with favorable neurological outcomes among patients with OHCA.

Methods

The data on incidence of agonal breathing were prospectively collected for all evaluable participants in a multicenter, observational study in Japan (SOS-KANTO [Survey of Survivors after Out-of-Hospital Cardiac Arrest in Kanto Area] 2017 Study). Groups with and without agonal breathing were compared upon hospital arrival. Propensity-score with inverse probability of treatment weighting (IPTW) analysis was performed to adjust for confounding factors. The primary outcome was a favorable neurological outcome (Cerebral Performance Category 1-2) at 1 month.

Results

A total of 6,457 participants out of the 9,909 registered in SOS-KANTO 2017 (in which 42 facilities participated) were selected for the current study. There were 128 patients (2.0%) in the with-agonal breathing group and 6,329 (98.0%) in the withoutagonal breathing group. The primary outcome was 1.1% in the with-agonal breathing group and 0.6% in the without-agonal breathing group (risk difference, 0.55; 95% confidence interval, 0.23-0.87) after IPTW analysis.

Conclusion

In this multicenter prospective study, agonal breathing at hospital arrival was significantly associated with better neurological outcomes and increased survival at 1 month. Thus, agonal breathing at hospital arrival may be a useful prognostic predictor for patients experiencing OHCA.

Investigating the attenuating effects of metformin-loaded selenium nanoparticles coupled with Myrtus communis L. flower extract on CaOx deposition in male Sprague Dawley rat kidneys via regulating MAPK signaling pathway

Kidney stones are a foremost clinical concern in urology with CaOx crystals accounting for roughly 80% of these renal formations. This research endeavor seeks to ascertain the protective effects of Metformin-encapsulated selenium nanoparticles (M@Se NPs), combined with a 55% hydroethanolic flower extract from Myrtus communis L. (MCL) in countering the formation of kidney stones in Male Sprague Dawley rats. The particle's diameter was measured to be 39 nm and 13.8 nm from DLS and HR-TEM analysis. Rat groups administered with the MCL-M@Se NPs (1:1.5:1) exhibited reduced renal stone formation in urine and serum analysis compared to the negative control group. Histological evaluations of kidney samples using H&E, and MTS staining indicated a subdued presence of ECM deposition in contrast to other rat groups. Conclusively, the protective mechanism of MCL-M@Se NPs against CaOx stone damage can be confidently attributed to the obstruction of the MAPK signaling pathway.

Advanced neuromonitoring powered by ICM+ and its place in the Brand New AI World, reflections at the 20th anniversary boundary

Introduction

Adoption of the ICM+® brain monitoring software by clinical research centres worldwide has been continuously growing over the past 20 years. This has necessitated ongoing updates to accommodate evolving neuromonitoring research needs, including recent explosion of artificial intelligence (AI).

Research question

We sought to provide an update on the current features of the software. In particular, we aimed to highlight the new options of integrating AI models.

Material and methods

We reviewed all currently available ICM+ analytical areas and discussed potential AI based extensions in each. We tested a proof-of-concept integration of an AI model and evaluated its performance for real-time data processing.

Results

ICM+ current analytical tools serve both real-time (bed-side) and offline (file based) analysis, including the calculation engine, Signal Calculator, Custom Statistics, Batch tools, ScriptLab and charting. The ICM+ Python plugin engine allows to execute custom Python scripts and take advantage of complex AI frameworks. For the proof-of-concept, we used a neural network convolutional model with 207,000 trainable parameters that classifies morphology of intracranial pressure (ICP) pulse waveform into 5 pulse categories (normal to pathological plus artefactual). When evaluated within ICM+ plugin script on a Windows 10 laptop the classification of a 5 min ICP waveform segment took only 0.19s with a 2.3s of initial, one-off, model loading time required.

Conclusions

Modernised ICM+ analytical tools, reviewed in this manuscript, include integration of custom AI models allowing them to be shared and run in real-time, facilitating rapid prototyping and validating of new AI ideas at the bed-side.

Hyperkalaemia in bleeding trauma patients: A potential marker of disease severity - A retrospective cohort study

Background

Hyperkalaemia is a common electrolyte abnormality seen in critically ill patients. In haemorrhagic shock, it may contribute to cardiac arrest and has been identified as a potential marker for tissue hypoxia. However, the significance of its role in haemorrhagic shock and its contribution to mortality remains unclear. This study aimed to examine the potential underlying pathophysiology and evaluate the incidence and characteristics of patients with hyperkalaemia on hospital arrival in bleeding trauma patients before transfusions and its mortality.

Methods

A retrospective cohort study was conducted on adult patients with traumatic bleeding admitted to a European Major Trauma Centre between January 2016 and December 2021. Patients were classified according to their serum potassium levels on arrival, and relevant clinical parameters between non-hyperkalaemic and hyperkalaemic patients were compared.

Results

Among the 83 patients in this study, 8 (9.6 %) presented with hyperkalaemia on arrival. The median shock index showed a higher tendency in the hyperkalaemic group. Hyperkalaemia was found to be more common among younger patients who sustained penetrating trauma. Mortality rates were higher in the hyperkalaemic group, but the difference was not statistically significant.

Conclusion

Our results suggest that hyperkalaemia occurs frequently in bleeding trauma patients on hospital arrival pre-transfusions, indicating a more severe illness. Our findings provide insights into the pathophysiology and characteristics of hyperkalaemia in bleeding trauma patients. Further studies are required to investigate the mechanisms by which hyperkalaemia contributes to mortality in haemorrhagic shock patients.

Critical care beyond organ support: the importance of geriatric rehabilitation

Very old critically ill patients pose a growing challenge for intensive care. Critical illness and the burden of treatment in the intensive care unit (ICU) can lead to a long-lasting decline of functional and cognitive abilities, especially in very old patients. Multi-complexity and increased vulnerability to stress in these patients may lead to new and worsening disabilities, requiring careful assessment, prevention and rehabilitation. The potential for rehabilitation, which is crucial for optimal functional outcomes, requires a systematic, multi-disciplinary approach and careful long-term planning during and following ICU care. We describe this process and provide recommendations and checklists for comprehensive and timely assessments in the context of transitioning patients from ICU to post-ICU and acute hospital care, and review the barriers to the provision of good functional outcomes.

Association of active mobilisation variables with adverse events and mortality in patients requiring mechanical ventilation in the intensive care unit: a systematic review and meta-analysis

BACKGROUND

Mobilisation during critical illness is now included in multiple clinical practice guidelines. However, a large, randomised trial and systematic review have recently identified an increased probability of adverse events and mortality in patients who received early active mobilisation in the intensive care unit (ICU). We aimed to determine the effects of mobilisation compared with usual care on adverse events and mortality in an acute ICU setting. In subgroup analyses, we specifically aimed to investigate possible sources of harm, including the timing and duration of mobilisation achieved, ventilation status, and admission diagnosis.

METHODS

In this systematic review with frequentist and Bayesian analyses, we searched MEDLINE, Embase, Cochrane Central Register of Controlled Trials, CINAHL, SPORTDiscus, SCOPUS, Web of Science, and PEDro electronic databases, as well as clinical trial registries (ICTRP and ClinicalTrials.gov), from inception to March 16, 2023, without language restrictions. Eligible studies were randomised controlled trials that examined active mobilisation compared with either no mobilisation or mobilisation commencing later, or at a lower frequency or intensity, in adults who were critically ill during or after a period of mechanical ventilation in an acute ICU setting. Two authors independently screened reports, extracted data, and assessed the risk of bias using the Cochrane risk-of-bias tool (version 1). The primary outcome was the number of adverse events that occurred during the implementation of mobilisation, with the effect of mobilisation on mortality being the secondary outcome. Risk ratios (RRs) with 95% CIs were calculated in R (version 4.0.3) using random-effects modelling, with Bayesian analysis completed to calculate the probability of treatment harm (ie, RR >1). Subgroup analyses were completed to investigate the association of various factors of mobilisation on adverse events and mortality: duration of mobilisation (longer [≥20 min per day] vs shorter [<20 min per day]), timing of commencement (early [≤72 h from ICU admission] vs late [>72 h from ICU admission]), ventilation status at commencement (all patients mechanically ventilated vs all patients extubated), and ICU admission diagnosis (surgical vs medical). This study was registered with PROSPERO, CRD42022369272.

FINDINGS

After title and abstract screening of 14 440 studies and review of 466 full texts, 67 trials with 7004 participants met inclusion criteria, with 59 trials contributing to the meta-analysis. Of the 67 included studies, 15 (22%) did not mention adverse events and 13 (19%) reported no adverse events occurring across the trial period. Overall, we found no effect of mobilisation compared with usual care on the occurrence of adverse events (RR 1·09 [95% CI 0·69-1·74], p=0·71; I2 91%; 32 731 events, 20 studies; very low certainty), with a 2·96% occurrence rate (693 events in 23 395 intervention sessions; 25 studies). Mobilisation did not have any effect on mortality (RR 0·98 [95% CI 0·87-1·12], p=0·81; I2 0%; n=6218, 58 studies; moderate certainty). Subgroup analysis was hindered by the large amount of data that could not be allocated and analysed, making the results hypothesis generating only.

INTERPRETATION

Implementation of mobilisation in the ICU was associated with a less than 3% chance of an adverse event occurring and was not found to increase adverse events or mortality overall, providing reassurance for clinicians about the safety of performing this intervention. Subgroup analyses did not clearly identify any specific variable of mobilisation implementation that increased harm.

FUNDING

None.

Secondary insults prevalence, co-occurrence and relationship with outcome after severe TBI

Introduction

Secondary insults due to high intracranial pressure (ICP), low cerebral perfusion pressure (CPP) and impaired cerebral pressure reactivity (PRx) predict outcome after severe traumatic brain injury (TBI).

Research question

What is the prevalence, co-occurrence and prognostic importance of secondary insults due to deranged ICP, CPP or PRx after TBI.

Material and methods

Severe TBI patients requiring ICP monitoring were included. Secondary insults due to ICP, PRx, and CPP were defined as having at least 1 h with a mean value above (or below for CPP) a respective threshold (ICP 20, CPP 60, and PRx 0.25). Percentage time with isolated or co-occurring insults was calculated (impaired ICP only, CPP only, PRx only, ICP and PRx, ICP and CPP, CPP and PRx, ICP CPP and PRx). Prognostic importance for mortality was assessed using a logistic regression model.

Results

822 patients were included of which 76% had elevated ICP, 92% had disturbed pressure reactivity and 55% had low CPP for at least an hour. Out of the total 115,459 h, 46,111 (40%) were spent with at least one variable within the defined secondary injury range. Odds ratios for mortality were greater for combined (impaired ICP, CPP and PRx OR 1.17 95%CI 1.09 to 1.28) than isolated insults (impaired ICP only OR 1.01 95%CI 1.00-1.02, impaired CPP only 1.00 95%CI 0.95-1.05).

Discussion and conclusion

ICP and autoregulation insults are common after TBI and often occur independently. Concurrent ICP, CPP and PRx insults portend worse prognosis than when a single variable is deranged.

From bedside to recovery: exercise therapy for prevention of post-intensive care syndrome

BACKGROUND

As advancements in critical care medicine continue to improve Intensive Care Unit (ICU) survival rates, clinical and research attention is urgently shifting toward improving the quality of survival. Post-Intensive Care Syndrome (PICS) is a complex constellation of physical, cognitive, and mental dysfunctions that severely impact patients' lives after hospital discharge. This review provides a comprehensive and multi-dimensional summary of the current evidence and practice of exercise therapy (ET) during and after an ICU admission to prevent and manage the various domains of PICS. The review aims to elucidate the evidence of the mechanisms and effects of ET in ICU rehabilitation and highlight that suboptimal clinical and functional outcomes of ICU patients is a growing public health concern that needs to be urgently addressed.

MAIN BODY

This review commences with a brief overview of the current relationship between PICS and ET, describing the latest research on this topic. It subsequently summarises the use of ET in ICU, hospital wards, and post-hospital discharge, illuminating the problematic transition between these settings. The following chapters focus on the effects of ET on physical, cognitive, and mental function, detailing the multi-faceted biological and pathophysiological mechanisms of dysfunctions and the benefits of ET in all three domains. This is followed by a chapter focusing on co-interventions and how to maximise and enhance the effect of ET, outlining practical strategies for how to optimise the effectiveness of ET. The review next describes several emerging technologies that have been introduced/suggested to augment and support the provision of ET during and after ICU admission. Lastly, the review discusses future research directions.

CONCLUSION

PICS is a growing global healthcare concern. This review aims to guide clinicians, researchers, policymakers, and healthcare providers in utilising ET as a therapeutic and preventive measure for patients during and after an ICU admission to address this problem. An improved understanding of the effectiveness of ET and the clinical and research gaps that needs to be urgently addressed will greatly assist clinicians in their efforts to rehabilitate ICU survivors, improving patients' quality of survival and helping them return to their normal lives after hospital discharge.

Using AI to Write a Review Article Examining the Role of the Nervous System on Skeletal Homeostasis and Fracture Healing

PURPOSE OF REVIEW

Three review articles have been written that discuss the roles of the central and peripheral nervous systems in fracture healing. While content among the articles is overlapping, there is a key difference between them: the use of artificial intelligence (AI). In one paper, the first draft was written solely by humans. In the second paper, the first draft was written solely by AI using ChatGPT 4.0 (AI-only or AIO). In the third paper, the first draft was written using ChatGPT 4.0 but the literature references were supplied from the human-written paper (AI-assisted or AIA). This project was done to evaluate the capacity of AI to conduct scientific writing. Importantly, all manuscripts were fact checked and extensively edited by all co-authors rendering the final manuscript drafts significantly different from the first drafts.

RECENT FINDINGS

Unsurprisingly, the use of AI decreased the time spent to write a review. The two AI-written reviews took less time to write than the human-written paper; however, the changes and editing required in all three manuscripts were extensive. The human-written paper was edited the most. On the other hand, the AI-only paper was the most inaccurate with inappropriate reference usage and the AI-assisted paper had the greatest incidence of plagiarism. These findings show that each style of writing presents its own unique set of challenges and advantages. While AI can theoretically write scientific reviews, from these findings, the extent of editing done subsequently, the inaccuracy of the claims it makes, and the plagiarism by AI are all factors to be considered and a primary reason why it may be several years into the future before AI can present itself as a viable alternative for traditional scientific writing.

Early mobilisation within 72 hours after admission of critically ill patients in the intensive care unit: A systematic review with network meta-analysis

INTRODUCTION

Early mobilisation within 72 hours of intensive care unit admission counteracts complications caused by critical illness. The effect of different interventions on intensive care unit length of stay and other outcomes is unclear. We aimed to investigate the effectiveness of various early mobilisation interventions within 72 hours of admission to the intensive care unit on length of stay and other outcomes.

METHODS

A systematic review and (network) meta-analysis examining the effect of early mobilisation on length of stay in the intensive care unit and other outcomes, conducting searches in four databases. Randomised controlled trials were included from inception to 10/08/2022. Early mobilisation was defined as interventions that initiates and/or supports passive/active range-of-motion exercises within 72 hours of admission. In multi-arm studies, interventions used in other studies were declared as early intervention and were included in subgroup meta-analysis. Risk-of-bias was assessed using RoB2.

RESULTS

Of 29,680 studies screened, 18 studies with 1923 patients (three high, eleven some, four low risk-of-bias) and seven discriminable interventions of early mobilisation met inclusion criteria. Early mobilisation alone (WMD 0.78 days, 95 %CI [-1.38;-0.18], 11 studies, n = 1124) and early mobilisation with early nutrition (WMD -1.19 days, 95 %CI [-2.34;-0.03], 1 study, n = 100) were able to significantly shorten length of stay. Early mobilisation alone could also substantially shorten hospital length of stay (WMD -1.05 days, 95 %CI [-1.74;-0.36], 8 studies, n = 977). This effect in hospital length of stay was furthermore seen in the early intervention group compared with standard care (WMD -1.71 days, 95 %CI [-2.99;-0.43], 14 studies, n = 1587). Also, functionality and quality of life could significantly be improved by an early start of mobilisation.

CONCLUSION

In the network meta-analysis, early mobilisation alone and early mobilisation with early nutrition demonstrated a significant effect on intensive care length of stay. Early mobilisation could also reduce hospital length of stay and positively influence functionality and quality of life.

IMPLICATION FOR CLINICAL PRACTICE

Early mobilisation and early mobilisation with early nutrition seemed to be beneficial compared to other interventions like cycling on intensive care length of stay.

Artificial intelligence in emergency medicine. A systematic literature review

Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies.

METHODS

The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms.

FINDINGS AND DISCUSSION

The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction.

CONCLUSION

Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.

The optimal dose of mobilisation therapy in the ICU: a prospective cohort study

BACKGROUND

This study aimed to assess the impact of duration of early mobilisation on survivors of critical illness. The hypothesis was that interventions lasting over 40 min, as per the German guideline, positively affect the functional status at ICU discharge.

METHODS

Prospective single-centre cohort study conducted in two ICUs in Germany. In 684 critically ill patients surviving an ICU stay > 24 h, out-of-bed mobilisation of more than 40 min was evaluated.

RESULTS

Daily mobilisation ≥ 40 min was identified as an independent predictor of an improved functional status upon ICU discharge. This effect on the primary outcome measure, change of Mobility-Barthel until ICU discharge, was observed in three different models for baseline patient characteristics (average treatment effect (ATE), all three models p < 0.001). When mobilisation parameters like level of mobilisation, were included in the analysis, the average treatment effect disappeared [ATE 1.0 (95% CI - 0.4 to 2.4), p = 0.16].

CONCLUSIONS

A mobilisation duration of more than 40 min positively impacts functional outcomes at ICU discharge. However, the maximum level achieved during ICU stay was the most crucial factor regarding adequate dosage, as higher duration did not show an additional benefit in patients with already high mobilisation levels.

TRIAL REGISTRATION

Prospective Registry of Mobilization-, Routine- and Outcome Data of Intensive Care Patients (MOBDB), NCT03666286. Registered 11 September 2018-retrospectively registered, https://classic.

CLINICALTRIALS

gov/ct2/show/NCT03666286 .

Responsiveness of Critically Ill Adults With Multimorbidity to Rehabilitation Interventions: A Patient-Level Meta-Analysis Using Individual Pooled Data From Four Randomized Trials

OBJECTIVE

To explore if patient characteristics (pre-existing comorbidity, age, sex, and illness severity) modify the effect of physical rehabilitation (intervention vs control) for the coprimary outcomes health-related quality of life (HRQoL) and objective physical performance using pooled individual patient data from randomized controlled trials (RCTs).

DATA SOURCES

Data of individual patients from four critical care physical rehabilitation RCTs.

STUDY SELECTION

Eligible trials were identified from a published systematic review.

DATA EXTRACTION

Data sharing agreements were executed permitting transfer of anonymized data of individual patients from four trials to form one large, combined dataset. The pooled trial data were analyzed with linear mixed models fitted with fixed effects for treatment group, time, and trial.

DATA SYNTHESIS

Four trials contributed data resulting in a combined total of 810 patients (intervention n = 403, control n = 407). After receiving trial rehabilitation interventions, patients with two or more comorbidities had HRQoL scores that were significantly higher and exceeded the minimal important difference at 3 and 6 months compared with the similarly comorbid control group (based on the Physical Component Summary score (Wald test p = 0.041). Patients with one or no comorbidities who received intervention had no HRQoL outcome differences at 3 and 6 months when compared with similarly comorbid control patients. No patient characteristic modified the physical performance outcome in patients who received physical rehabilitation.

CONCLUSIONS

The identification of a target group with two or more comorbidities who derived benefits from the trial interventions is an important finding and provides direction for future investigations into the effect of rehabilitation. The multimorbid post-ICU population may be a select population for future prospective investigations into the effect of physical rehabilitation.

Reliability and Effectiveness of the Japanese Version of the Mobilization Quantification Score

Background The mobilization quantification score (MQS) provides an opportunity to quantify the duration and intensity of mobilization therapy in the intensive care unit (ICU) and predict functional outcomes in ICU patients after surgery and stroke. MQS is a numerical measurement of early mobilization dose in the ICU, and its relationship with activities of daily living (ADL) dependence has been shown. We created and validated the Japanese version of the MQS using the endpoint ADL in a mixed population of patients in the ICU. Materials and methods In this prospective study, consecutive patients who were admitted to one of three ICUs of a tertiary care hospital in Japan, aged ≥18 years, and who received mechanical ventilation for >48 hours were enrolled. The Japanese version of the MQS was applied twice daily by an ICU physiotherapist and data recorded for analysis. The primary outcome was ADL dependence at hospital discharge, defined as a Barthel index (BI) of <70 or in-hospital death. The reliability among assessors was verified by calculating the interclass correlation coefficient (ICC) (2.1) for the average daily MQS. We performed a multiple logistic regression analysis to examine and identify a binary cutoff point for high-/low-dose rehabilitation. Results Of the 340 target patients, eight were aged <18 years, 109 had neurological complications, 11 had a BI <70 before admission, 79 had a lack of communication skills, 16 were terminally ill, eight did not complete the assessment during their ICU stay, 18 died in the ICU, and 53 denied consent. After 302 patients were excluded, 38 were included in the study. Six assessors, two at each hospital, measured the MQS in 38 patients. The ICC (2.1) for the MQS mean value was 0.98 (0.96-0.99) during the ICU stay. Logistic regression analysis using the mean MQS on admission to ICUs as an explanatory variable showed a significant association between increased MQS and decreased ADL dependence at discharge (odds ratio (OR): 0.76, confidence interval (CI): 0.61-0.96, adjusted p = 0.009). Logistic regression analysis using a high MQS on admission to ICUs as an explanatory variable showed a significant association between increased MQS and decreased ADL dependence at hospital discharge (OR: 0.14, CI: 0.03-0.66, adjusted p = 0.013). Conclusions We present a validated version of the Japanese MQS with a high inter-rater reliability that predicts ADL dependence at hospital discharge. The instrument can be used in future clinical trials in the ICU to control for the mobilization level in the ICU. The increased utilization of mobilization acutely in the ICU setting as quantified by the MQS may improve patient outcomes.

Prediction of 30-Day Mortality Using the Quick Pitt Bacteremia Score in Hospitalized Patients with Klebsiella pneumoniae Infection

Purpose

Klebsiella pneumoniae is an important causative pathogen of nosocomial infections, resulting in poor prognosis owing to its hypervirulence and antibiotic resistance. A simplified quicker version of the Pitt bacteremia score (PBS) (qPitt) for acute illness severity measurement was developed recently. The goal of this study was to explore the prognostic value of qPitt in patients with K. pneumoniae infection.

Patients and Methods

Demographic information and management strategies were retrospectively collected from the records of all adult patients who visited the emergency department between January 1, 2021, and December 31, 2021, with culture-positive K. pneumoniae. The qPitt score was calculated based on: temperature <36°C, systolic blood pressure ≤90 mmHg or vasopressor administration, respiratory rate ≥25 times/min or need of mechanical ventilation, altered mental status, and cardiac arrest event. The 30-day mortality prediction abilities of the qPitt were compared with the PBS, the sequential organ failure assessment (SOFA), and the quick sequential organ failure assessment (qSOFA) using receiver operating characteristic curves.

Results

Data from 867 patients (57.8% men) with a mean age of 66.9 were compiled. The 30-day mortality rate of the enrolled patients was 13.4%, and the area under the curve (AUC) of the scoring systems were as follows: SOFA, 0.91 (95% confidence interval [CI]=0.89-0.93), qPitt, 0.87 (95% CI=0.84-0.89), PBS, 0.87 (95% CI=0.85-0.89), and qSOFA, 0.73 (95% CI=0.70-0.76). The AUC of qPitt was significantly higher than that of qSOFA (p<0.01) and similar to that of PBS (p=0.65).The qPitt also demonstrated excellent mortality discrimination ability in non-bacteremic patients, AUC= 0.85 (95% CI=0.82-0.88).

Conclusion

The qPitt revealed excellent 30-day mortality prediction ability and also predicted mortality in non-bacteremic patients with K. pneumoniae infection. Clinicians can use this simplified scoring system to stratify patients earlier and initiate prompt treatment in high-risk patients.