Critical care explorations最新文献

Background: Immune-related adverse events (irAEs) induced by immune checkpoint inhibitors vary widely, raising diagnostic challenges. The management of suspected irAEs requires close collaboration between oncologists and organ specialists. Rarely, irAEs can occur as overlap syndromes and may require intensive care.

Case summary: A 69-year-old man required intensive care for severe overlap syndrome induced by a programmed death-ligand 1 inhibitor. The presentation as recurrent hypercapnic coma complicated the diagnosis of polyradiculoneuritis combined with myocarditis. The finding of anti-titin autoantibodies assisted in the diagnosis. The patient was successfully treated with corticosteroids, polyvalent immunoglobulins, abatacept, and ruxolitinib. The irAEs abated but the polypharmacy contributed to the development of fatal hemorrhagic shock.

Conclusions: This is the first reported case of seropositive, overlap irAEs consisting of myocarditis and polyradiculoneuritis. Early immunosuppressive therapy can alleviate irAEs but can also lead to serious complications in frail patients. This underscores the importance of carefully balancing expected therapeutic benefits against potential risks.

Importance: This study provides nationally representative data on critical care advanced practice provider (APP) workforce structure and practice, addressing gaps in onboarding, productivity assessment, and administrative roles that influence workforce sustainability.

Objectives: To analyze the composition of the clinical practice and workforce demographics of critical care medicine APPs.

Design, setting, and participants: We utilized a cross-sectional survey, which was distributed via email through the Society of Critical Care Medicine email list, targeting critical care APPs.

Main outcomes and measures: A total of 518 critical care APPs participated in the survey, comprising 63.2% advanced practice registered nurses, 28.8% physician assistants, and 8% clinical nurse specialists. The majority of the sample APPs worked 12-hour shifts covering days, nights, and weekends, with over 50% managing 6-10 patients per day shift. Common billing practices included critical care time (60.6%) and bedside invasive procedures (64.3%). Formal onboarding was reported by 68.6% of respondents, with academic hospitals more likely to offer such programs than community hospitals (72.3% vs. 27.7%; p < 0.001). Administrative APP roles were more prevalent in academic centers (p < 0.001) and were associated with greater access to professional development opportunities (p < 0.001). A significant proportion of respondents (41.5%) reported that their productivity was not formally measured. Notably, 22% of respondents reported an intent to leave the profession, particularly those lacking leadership or professional growth opportunities (p < 0.01).

Conclusions and relevance: This study captures practice trends and demographics of the critical care APP workforce. Day-to-day tasks among all respondents were globally clinically focused, but there were disparities in workload distribution, onboarding, productivity measurement, and administrative roles across types of centers. Additionally, there was significant association between limited professional growth opportunities and intent to leave critical care. These findings underscore the need for healthcare systems to invest in structured support, leadership development, and professional growth to enhance APP retention, engagement, and productivity. Further research is needed to identify efficient workload and staffing models.

Objectives: Current research does not address the existence and impact of structured tele-critical care (TCC) training on the delivery of care in the ICU. This pilot study aimed to evaluate the association between on boarding elements focused on training and clinicians perceptions of delivery of care.

Design: Cross-sectional survey study.

Setting and participants: Critical Care professionals at four U.S. hospitals, professional meetings and Society of Critical Care Medicine's International Membership base. Participants were active clinicians who practice in the TCC setting.

Interventions: None.

Measurements and main results: A total of 432 participants responded to the survey, 227 met the eligibility criteria (52.5%) and were included in the analysis. Respondents were a multi-professional group of TCC clinicians aged between 35-55 years of age (56.4%). Seventy-four percent of respondents reported having orientation before providing TCC, 46% reported having formal mentorship, and 66% reported formal training on their institutional platform. Provision of orientation before participating in a TCC program was associated with higher ratings of feeling prepared (odds ratio [OR], 3.52; p < 0.001) and feeling accepted as part of the ICU team (OR, 2.21; p = 0.008). Mentorship was associated with feeling more prepared (OR, 8.2; p < 0.001) and higher comfort in delivering care (OR, 2.78; p = 0.016). Platform training was associated with feeling more prepared (OR, 4.66; p < 0.001), comfortable in delivering care (OR, 3.6; p = 0.002), feeling accepted as part of the team (OR, 3.18; p < 0.001), and more likely to participate in quality improvement (OR, 2.51; p = 0.001). A site visit also made a positive impact in feeling prepared (OR, 2.86; p < 0.001), comfortable (OR, 4.95; p = 0.002), feeling like recommendations were accepted (OR, 3.73; p < 0.001), more likely to recommend TCC (OR, 3.18; p = 0.001), and participating in quality improvement (OR, 3.24; p < 0.001).

Conclusions: In this pilot study, structured training utilizing orientation, mentorship, and platform training as surrogates, along with a site visit before beginning delivery of care in a TCC setting, were associated with more positive perceptions in the delivery of care domains assessed. We highlight potentially important factors that warrant further evaluation and assessment of the need for standardization across TCC programs.

Importance: Communication failures during patient handoffs from the ICU to the ward are common and negatively impact patients. Structured handoff communication tools may improve these transitions yet remain infrequently used.

Objectives: Characterize local determinants of ICU-PAUSE handoff program perceived implementation success and describe adaptations made to the ICU-PAUSE tool and its implementation strategies during multicenter implementation.

Design, setting, and participants: Qualitative study in 11 academic hospitals' medical ICUs. Participants were intensive care physicians who volunteered to champion ICU-PAUSE implementation at their respective institutions.

Analysis: Thematic analysis of semi-structured interviews to understand determinants, artifact analysis to characterize adaptations, and retrospective post-implementation chart review of ICU-to-ward transfer notes to evaluate uptake and sustainability of the intervention.

Results: Participants reported fewer perceived implementation barriers than expected, with many anticipated barriers ultimately functioning as important facilitators. Eight sites (73%) modified the ICU-PAUSE electronic template and/or its implementation strategies; most of the 29 unique adaptations described by participants involved adding new content to the standard template. Noncontent adaptations were largely contextual to fit site-specific needs and facilitate tool adoption. One year after implementation, the ICU-PAUSE template was used in 75% of ICU-to-ward transfer notes analyzed.

Conclusions: ICU-PAUSE is a low-barrier intervention to improve ICU-ward handoff communication. This study highlights the importance of adaptability in the success of nationally scalable implementation efforts for bundled interventions like ICU-PAUSE.

Objectives: Vasoplegia is a common complication of cardiac surgery that uses cardiopulmonary bypass and contributes to morbidity and mortality, yet a consensus definition does not exist. The objective of this study was to evaluate the diagnostic criteria and definitions used to characterize vasoplegia and how different criteria influence incidence estimates.

Data sources: Ovid Embase, Ovid MEDLINE, Scopus, Web of Science Core Collection, ClinicalTrials.gov, Ovid Cochrane Central Register of Controlled Trials, and the World Health Organization's International Clinical Trials Registry Platform clinical trials registry.

Study selection: Randomized clinical trials and observational studies reporting on vasoplegia in adults undergoing any type of cardiac surgery that used cardiopulmonary bypass.

Data extraction: Proportional meta-analysis using a random-effects model and the inverse variance method was used to calculate the pooled incidence of vasoplegia and its clinical outcomes.

Data synthesis: A total of 68 studies encompassing 56,580 patients were identified, from which 63 unique vasoplegia definitions were used. Blood pressure (n = 57 studies, 84%) and cardiac output (n = 50 studies, 74%) were among the most common criteria used in vasoplegia definitions; however, there was a vast variety of threshold values applied within these criteria and all other criteria comprising the definitions. The pooled incidence of vasoplegia was 21% (95% CI, 17-25%), acute kidney injury was 32% (95% CI, 21-45%), and mortality was 12% (95% CI, 9-16%). Subgroup analysis revealed that transplantation and left ventricular assist device implantation surgeries, and those with baseline left ventricular ejection fraction less than 40% had a significantly greater incidence of vasoplegia.

Conclusions: The published literature varies greatly in the criteria used to define vasoplegia associated with on-pump cardiac surgery. Generation and adoption of a unified definition for vasoplegia must be an international priority.

Objective: Acute respiratory distress syndrome (ARDS) is estimated to be prevalent in 10% of ICU patients and results in high mortality rates of up to 45%. The recognition of ARDS can be complex and is often delayed or missed entirely. Recognition of increased ARDS risk among critically ill patients may prompt judicious care management strategies and initiation of preventative therapies known to improve survival.

Design: Retrospective observational cohort study.

Setting: In-patient tertiary hospital.

Patients: Among 1160 patients (2017-2018), 761 had adequate duration and quality of monitoring waveform data for analysis.

Interventions: None.

Measurements and main results: This is an observational, retrospective, institutional review board-approved study of patients admitted to ICUs at a tertiary hospital system. Physiologic data were captured among critically ill patients who developed ARDS (n = 62) and matched controls (n = 699) during their hospitalization. Machine learning algorithms were evaluated against statistical features from continuous electrocardiogram (ECG) and sparse clinical data. Waveform-derived cardiorespiratory features, namely measures relating to heart rate variability were found to be robust and reliable features that predicted ARDS up to 2 days before onset. The combined model consisting of waveform features and clinical data with 12-hour prediction horizon achieved an area under the receiver operating characteristic curve and positive predictive value of 0.92 (95% CI, 0.91-0.93) and 0.58 (95% CI, 0.55-0.62), surpassing a model with the clinical data removed (0.86 [95% CI, 0.85-0.88] and 0.49 [95% CI, 0.46-0.52]) and the Lung Injury Prediction Score's maximum of 0.88 and 0.18.

Conclusions: Waveform markers can combine with Electronic Medical Records (EMR) data to improve predictability of ARDS before onset. The markers appear to modulate the sparser EMR data. They also provide, in and of themselves, sufficient dynamical information for comparable results to models with EMR data. Further prospective validation is needed to evaluate the robustness of the model and potential clinical utility.

Background: Neurofibromatosis type 1 (NF1) is an autosomal dominant genetic disorder, characterized by neurocutaneous lesions. NF1 has a high degree of clinical variability, which can include multiple neoplasia as well as cutaneous, vascular, osseous, and cognitive features. When vascular involvement occurs, NF1 can lead to aneurysms or arteriovenous malformations, which may rupture and cause life-threatening complications.

Case summary: We present a case of primary subarachnoid hemorrhage, complicated by spontaneous and rapidly progressing hemorrhage from the left subclavian artery resulting in upper airway obstruction and hypoxia in a patient with NF1. Treatment of this patient included surgical airway management, emergency hematoma evacuation, and vascular reconstructive surgery. Close collaboration between radiology, vascular surgery, and anesthesiology was essential to prevent patient's death.

Conclusions: Awareness of rare diseases such as NF1 is essential in critical care settings. Patients presenting with café-au-lait spots or cutaneous neurofibromas are at risk of vascular complications due to vascular fragility. This case of dual bleeding sources and airway obstruction from a neck hematoma underscores the need for interdisciplinary management. The role of proactive vascular screening in critically ill NF1 patients remains uncertain. Future approaches may incorporate advanced imaging and biomarker development to better stratify vascular risk and guide individualized care.

Objectives: Above cuff vocalization (ACV) is used in patients with a tracheostomy in the ICU despite limited evidence. This early-stage decision-analytic model (DAM) for ACV evaluates the expected cost-effectiveness exploring the impact of uncertainty to identify key drivers of cost and effect and critical further research priorities.

Perspective: U.K. National Health Service.

Setting: Hypothetical cohort of general ICU patients with a tracheostomy, 63 years old, 64% male.

Methods: A de novo decision-analytic health economic model comparing ACV to usual care (UC). Model parameters were acquired from the literature review and expert opinion. One-way sensitivity analyses were conducted to identify key drivers of cost-effectiveness.

Results: The daily cost of ACV in the ICU ranged from £75 to 89 (USD 101-120), with most of this cost attributable to staff resources for delivery. The base-case scenario revealed ACV is potentially cost-effective, dominating UC with cost savings of £9,488 (USD 12,808) and 0.395 Quality-Adjusted Life Years gained. Most sensitivity analyses revealed that ACV dominated UC, costing less and being more effective. When ACV had a negative impact on ICU and ward length of stay (LoS), or had no effect on the speed of weaning, it was not cost-effective. The primary driver of cost was whether ACV affected the speed of weaning and ICU LoS. The two primary drivers of effect were: i) whether ACV impacted which end state a patient transitioned to and ii) whether ACV had a sustained positive impact on quality of life.

Conclusions: Despite the substantial input required from speech-language pathologists-a typically scarce resource in ICU settings-ACV demonstrates strong potential for cost-effectiveness. There is no reason for decision-makers to de-adopt ACV, and delaying adoption may result in loss of opportunity costs. Improved reporting of mortality and utility data in critical care research would increase the reliability of early-stage DAMs.

Objectives: This systematic review evaluates artificial intelligence (AI)-based predictive models developed for early sepsis detection in adult hospitalized patients. It explores model types, input features, validation strategies, performance metrics, clinical integration, and implementation challenges.

Data sources: A systematic search was conducted across PubMed, Scopus, Web of Science, Google Scholar, and CENTRAL for studies published between January 2015 and March 2025.

Study selection: Eligible studies included those developing or validating AI models for adult inpatient sepsis prediction using electronic health record data and reporting at least one performance metric (area under the curve [AUC], sensitivity, specificity, or F1 score). Studies focusing on pediatric populations, lacking quantitative evaluation, or unpublished in peer-reviewed journals were excluded.

Data extraction: Data extraction followed preferred reporting items for systematic reviews and meta-analyses guidelines. Extracted variables included study design, patient population, model type, input features, validation approach, and performance outcomes.

Data synthesis: A total of 52 studies met the inclusion criteria. Most used retrospective designs, with limited prospective or real-time clinical validation. Commonly used algorithms included random forests, neural networks, support vector machines, and deep learning architectures (long short-term memory, convolutional neural network). Input data varied from structured sources (vital signs, laboratory values, demographics) to unstructured clinical notes processed via natural language processing. Reported AUC values ranged from 0.79 to 0.96, indicating strong predictive performance across models.

Conclusions: AI models demonstrate significant promise for early sepsis detection, outperforming conventional scoring systems in many cases. However, generalizability, interpretability, and clinical implementation remain major challenges. Future research should emphasize externally validated, explainable, and scalable AI solutions integrated into real-time clinical workflows.