OBJECTIVES:To perform an updated systematic review and meta-analysis of the efficacy and safety of intensive (INT) vs. conventional (CONV) blood glucose (BG) targets for critically ill adults on insulin infusions. DATA SOURCES:We conducted a comprehensive search of Embase and OVID Medline databases from inception to October 16, 2023. We manually excluded studies published before 2000 due to potential lack of relevance as glycemic control in the ICU was not routinely practiced before 2000. STUDY SELECTION:We included randomized controlled trials (RCTs) evaluating adult, critically ill patients on insulin infusions comparing INT vs. CONV targets for efficacy and safety outcomes. DATA EXTRACTION:Data were screened and extracted with accuracy confirmed by a second reviewer. Study methodological characteristics, patient population, interventions, and outcome data were recorded. Studies without numerical outcomes were summarized as text statements. DATA SYNTHESIS:Forty-five RCTs were included involving 32,215 patients. No differences were seen between INT and CONV targets for hospital mortality or ICU mortality. INT targets were associated with lower ICU length of stay (LOS), infections, and critical illness polyneuropathy (CIP); however, INT targets demonstrated a 3.6-fold higher risk of severe hypoglycemia. Most of the studies with significant differences contained serious inconsistencies or risk of bias. In the subgroup analyses, INT targets demonstrated favorable neurologic outcomes in neurologic ICU patients, lower ICU LOS in mixed ICU patients, and lower ICU mortality in the cardiac surgery subgroup. CONCLUSIONS:INT BG targets demonstrated mild to moderate improvements in several important morbidity secondary outcomes, including LOS, infections, and CIP, but were associated with a 3.6-fold higher risk of severe hypoglycemia. No differences were seen in ICU or hospital mortality. INT targets should not be routinely used over CONV targets when trying to minimize hypoglycemia as a marker of patient safety. However, as stated in the Society of Critical Care Medicine guidelines, a lower target within the INT range (110-140 mg/dL; 6.1-7.8 mmol/L) may be considered acceptable in select centers where the risk of hypoglycemia is documented to be negligible based on routine assessment and with the use of optimized glycemic management protocols.

Abstract Background Fluid removal during hemodialysis plays a vital role in achieving optimal patient outcomes. Traditional methods to guide dialysis prescription by estimating dry weight are often sufficient, however, lack precision when assessing the nuanced interplay of venous and arterial physiology in hemodynamically complex patients. Left ventricular outflow tract velocity–time integral (LVOT VTI) provides a dynamic marker of forward stroke volume and can offer additional insight into intravascular volume status when conventional ultrasound markers of congestion are limited. LVOT VTI reflects effective left ventricular forward flow and is sensitive to changes in preload. LVOT VTI may improve with judicious fluid removal despite intradialytic hypotension, offering a practical physiological target to guide ultrafiltration in patients with challenging hemodynamics. Case presentation We present a 70-year-old gentleman with end-stage renal disease (ESRD) who switched to hemodialysis in September 2021 from peritoneal dialysis (PD) due to poor ultrafiltration, resulting in persistent fluid overload, including recurrent pleural effusions. An echocardiogram performed in October 2019 revealed significant pulmonary hypertension, severe tricuspid regurgitation (TR), and moderate mitral regurgitation (MR). Fluid management during hemodialysis proved challenging due to persistent predialysis hypotension and further drops in blood pressure during dialysis sessions. To address these challenges and guide fluid removal more precisely, we utilised point-of-care ultrasound (POCUS) to monitor the patient’s volume status. By prioritising stroke volume surrogates, specifically velocity-time integrals (LVOT VTI), over blood pressure as a guide for fluid removal, we were able to safely increase fluid removal per session and reduce his dry weight by 4 kg over 4 weeks. This was accompanied by significant improvement in his symptoms from fluid overload. Conclusion Fluid removal was guided by POCUS to address the patient’s complex hemodynamics. Despite intradialytic hypotension, we observed a significant increase in the patient’s LVOT VTI with ongoing fluid removal. This metric may serve as an adjunctive tool to guide dialysis prescription for select cases.

BACKGROUND:Venous congestion is a pathologic state caused by reduced arteriovenous gradients that promote injurious tissue edema. Venous congestion can be due to etiologies such as decompensated cardiac disease, renal failure, or iatrogenic fluid administration. However, the underlying pathobiology of venous congestion is poorly investigated, particularly in critical illness. We conducted a scoping review to identify candidate circulating proteins potentially associated with venous congestion pathobiology. AIM:To identify circulating proteins associated with the pathobiology of venous congestion. METHODS:The MEDLINE and EMBASE databases were searched for articles relevant to venous congestion. Studies were included if they: (1) Investigated human adult subjects ≥ 18 years of age; (2) Measured plasma or serum proteins in disease states with reported measures of venous congestion; and (3) Reported clinical or ultrasound measures of assessing venous congestion. Preferred Reporting Items for Systematic Reviews and Meta-analysis extension for scoping reviews guidelines were used. RESULTS:A total of 3860 abstracts were eligible for screening, of which 171 manuscripts underwent full-text review, and 145 texts met inclusion criteria. The median number of circulating proteins measured was 2 (interquartile range: 1-3). Most studies (116, 80%) reported measures of venous congestion in the context of cardiac disease. Five studies (3%) were performed in a critical care setting. Significant variability was noted in the reported measures of venous congestion, with physical examination often used to presume the presence of venous congestion (45% of studies). Less than 30% of studies had the objective of investigating circulating proteins, and less than 15% of studies aimed to characterize biology of venous congestion. The candidate circulating plasma proteins measured included proteins related to myocardial function, endothelial function, and inflammation. CONCLUSION:We present the first scoping review identifying circulating proteins with a possible role in mediating venous congestion at a molecular level. To date, no robust studies have comprehensively investigated the biology of venous congestion. These data provide a foundation for further studies of the biological mechanisms of venous congestion. Understanding these mechanisms may assist in the measurement of responses to volume resuscitation, stratification in clinical trials focusing on appropriate volume administration and removal, and the identification of novel therapies that target pathways implicated in this deleterious condition.

INTRODUCTION:Long-duration space missions demand reliable, portable, and autonomous medical diagnostic tools. Lung ultrasound (LUS) is ideal for space exploration due to its safety and versatility, but interpreting LUS images requires training. Artificial intelligence (AI) can assist with image interpretation by detecting lung sliding, an ultrasound sign produced by the movement of lung and chest wall during breathing. The presence of lung sliding helps to rule out pneumothorax, or a collapsed lung, which is a condition that may arise from barotrauma or rapid changes in pressurization. METHODS:LUS clips were acquired from two healthy volunteers during parabolic flight maneuvers simulating microgravity and lunar gravity, with +1-G clips used as controls. Clips were analyzed using an AI model to classify the presence of lung sliding and model performance and confidence were compared across gravity conditions. All clips were reviewed by an expert to establish whether lung sliding was present, which served as the reference standard for AI model evaluation. RESULTS:From 105 LUS clips, the model demonstrated an accuracy of 94%, with similar performance at +1 G (96%), lunar gravity (94%), and microgravity (92%). Prediction confidence varied by gravity, with median values of 93% at +1 G, 83% at lunar gravity, and 67% at microgravity. Confidence was significantly lower in microgravity compared with +1 G. DISCUSSION:These findings demonstrate that AI-assisted LUS can reliably detect lung sliding under reduced gravity, supporting its feasibility as an autonomous diagnostic support tool for spaceflight and highlighting the importance of further validation in microgravity environments. Côté M, Smith D, Orozco N, VanBerlo B, Huggard B, Arntfield R, Prager R. Artificial intelligence interpretation of point-of-care lung ultrasound in microgravity. Aerosp Med Hum Perform. 2026; 97(7):505-509.

Background:Pneumothorax (PTX) is a frequent complication after chest tube removal, and timely detection is important to inform monitoring and potential intervention. Chest radiograph (CXR) remains the standard modality after chest-tube-removal PTX detection, despite its limited sensitivity and frequent delays in acquisition. Lung ultrasound (LUS) has superior accuracy and portability but is highly operator dependent, limiting its usability. The objective of this study is to evaluate whether artificial intelligence-assisted LUS (AI-LUS) enables novice users to accurately detect PTX after chest tube removal, compared with expert interpretation and CXR. Research Question:Does AI-LUS improve the ability of novice users to detect findings associated with PTX after chest tube removal compared with expert interpretation and CXR? Study Design and Methods:We conducted a prospective diagnostic accuracy study in adult patients undergoing chest tube removal at a tertiary academic hospital. LUS clips were acquired by novice operators using a handheld ultrasound device. A previously trained artificial intelligence model was then calibrated and used to detect the absence or presence of lung sliding. The reference standard was expert consensus LUS interpretation, with CXR serving as a secondary reference standard. Sensitivity and specificity were calculated at 2 time points: immediately after removal and after routine CXR. Results:A total of 76 patients were enrolled, yielding 848 LUS clips across 2 time points. Data from the first 12 patients were used to calibrate the model, with the remaining 64 forming the validation cohort. Compared with expert LUS interpretation, AI-LUS demonstrated a sensitivity of 0.775, a specificity of 0.831, and a negative predictive value of 0.96 for identifying absent lung sliding. When compared with CXR, AI-LUS achieved a sensitivity of 1.0 immediately after removal and 0.923 after CXR for PTX detection. Interpretation:Our results show that novice-performed AI-LUS demonstrated moderate diagnostic accuracy for detecting absent lung sliding after chest tube removal. Its very high sensitivity and excellent negative predictive value for identifying cases with absent lung sliding associated with PTX relative to CXR highlights a potential role for AI-LUS as a rapid triage tool that may reduce reliance on routine CXR, while acknowledging that PTX inference requires clinical correlation and additional ultrasound findings.

BACKGROUND:Artificial intelligence (AI) has the potential to address training limitations and inter-operator variability that constrain the use of lung ultrasound (LUS) in austere and prehospital settings. This pilot study evaluated whether AI-based decision support could improve the diagnostic accuracy and confidence of United States Marine Corps Corpsmen in identifying absent lung sliding, a key indicator of pneumothorax, during LUS interpretation. METHODS:This pilot-prospective multi-reader, multi-case study involved five military medics, all novices in point-of-care ultrasound, each interpreting 50 de-identified LUS video clips twice, once without AI assistance (control) and once with AI assistance (ATLAS, Deep Breathe Inc., London, Canada), in randomized order with at least a 2-hour washout between sessions. Expert consensus served as a reference standard. Diagnostic performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy. Differences were analyzed using the Random-Reader Random-Case method. Per-clip reader confidence ratings were compared using the Stuart-Maxwell test. RESULTS:AI assistance significantly improved diagnostic performance across all measured outcomes. The mean AUROC increased from 0.72 (SD 0.16) without AI to 0.93 (SD 0.04) with AI (P=.03). Sensitivity rose from 0.63 (SD 0.14) to 0.90 (SD 0.09), specificity from 0.70 (SD 0.15) to 0.86 (SD 0.10), and overall accuracy from 0.67 (SD 0.10) to 0.88 (0.06) (McNemar's test, P<.001). Reader confidence also improved, with high-confidence ratings nearly doubling from 20% to 37%, and low-confidence ratings decreasing from 38% to 33%. These distributional changes were statistically significant (Stuart-Maxwell χ², P<.001). CONCLUSION:AI support markedly improved the diagnostic accuracy and confidence of novice LUS interpretation for detecting absent lung sliding. These findings suggest that real-time AI-based decision support may help improve access to high-quality LUS in military and other resource-limited care settings.

PURPOSE OF REVIEW:Cardiopulmonary monitoring is fundamental to critical care, yet traditional approaches rely on simplified thresholds that capture only a fraction of the rich information contained within waveforms, imaging, and continuous physiological data. This review examines emerging applications of artificial intelligence (AI) and machine learning (ML) that enhance waveform interpretation, automate point-of-care ultrasound (POCUS), enable predictive monitoring, and extend advanced assessment capabilities into low-resource settings. RECENT FINDINGS:AI models now identify deterioration earlier than conventional tools, derive complex hemodynamic variables from noninvasive signals, and predict events such as hypotension, cardiac arrest, and sepsis hours in advance. In POCUS, AI enables real-time acquisition guidance and automated cardiac and pulmonary interpretation, allowing novice users to obtain expert-quality studies. Cloud and edge-based architectures further support AI-driven monitoring in austere environments. Despite these advances, most AI systems remain in early development; fewer than 2% have undergone clinical integration, and challenges persist related to generalizability, bias, heterogeneous data quality, and limited prospective evaluation. SUMMARY:AI-assisted cardiopulmonary monitoring has the potential to transition critical care from reactive assessment to dynamic, anticipatory management. Realizing this promise will require rigorous validation, workflow integration, and evidence demonstrating true clinical benefit.

Rationale Lung ultrasound, the most precise diagnostic tool for pleural effusions, is underutilized due to healthcare providers' limited proficiency. To address this, deep learning models can be trained to recognize pleural effusions. However, current models lack the ability to diagnose effusions in diverse clinical contexts, which presents significant challenges. Objective To develop and validate a deep learning model for detecting pleural effusions in lung ultrasound images, with adaptable performance characteristics tailored to specific clinical scenarios. Methods A retrospective study was conducted at two Canadian tertiary hospitals to evaluate the detection of pleural effusions of varying sizes and complexities using lung ultrasound. A deep learning model incorporating a frame-level convolutional neural network and a clip-level prediction algorithm was developed and validated against expert annotations. Results The model was evaluated using a holdout dataset of 103 lung ultrasound clips from 46 patients with pleural effusion and 136 clips from 83 patients without effusion. The general model achieved a sensitivity of 0.90 for small-to-large effusions, with a specificity of 0.89. The large effusion model demonstrated a sensitivity of 0.97 for large effusions while maintaining a specificity of 0.90. The trauma model showed high sensitivity to all effusions, including trace (0.91) and small (0.97) effusions. Conclusion Our research highlights the development of a deep learning model that effectively detects pleural effusions of varying sizes and complexities on lung ultrasound in different clinical settings. This tool has the potential to enhance emergency physicians' ability to quickly and accurately diagnose effusions, particularly in time-sensitive situations.

Background: Chest point of care ultrasound (POCUS) is a first-line diagnostic test to identify lung sliding, an important artifact to diagnose or rule out pneumothorax. Despite enthusiastic adoption of this modality, the interrater reliability for physicians to identify lung sliding is unknown. Additionally, the relative diagnostic performance of physicians interpreting B-mode and M-mode ultrasound is unclear. We sought to determine the interrater reliability of physicians to detect lung sliding on B-mode and M-mode POCUS. Methods: We performed a cross-sectional interrater agreement study surveying acute care physicians on their interpretation of 20 B-mode and M-mode POCUS clips. Two experienced clinicians determined the reference standard diagnosis. Respondents reported their interpretation of each POCUS B-mode clip or M-mode image. The primary outcome was the interrater agreement, determined by an intra-class correlation coefficient (ICC). Results: From September to November 2023, there were 20 survey respondents. Fourteen (70%) respondents were resident physicians. Respondents were confident or very confident in their skill performing chest POCUS in 14 (70%) cases, with 19 (90%) performing chest POCUS every week or more frequently. The ICC on B-mode was 0.44 and for M-mode was 0.43, indicating moderate agreement. There were no significant differences in interrater reliability between subgroups of confidence or experience. Conclusion: There is only moderate interrater reliability between clinicians to diagnose lung sliding. Clinicians have superior accuracy on B-mode compared to M-mode clips.

Aim: Both the arterial and venous systems independently predict mortality in septic shock, yet no bedside tools are able to integrate their assessment. Risk stratification becomes challenging when arterial parameters suggest favourable outcomes while venous parameters indicate poor prognosis, or vice versa. To address this gap, we developed the VTI-VeXUS index and conducted this proof-of-concept study to test its association with mortality. Methods: We conducted a prospective cohort study in two ICUs, enrolling adult patients with septic shock. We calculated the VTI-VeXUS index (VTI/[VeXUS+1]) from ultrasound measurements obtained within 24 h of ICU admission and stratified patients as having a high or low VTI-VeXUS index based on a cutoff of 11. We evaluated the primary outcome of mortality at 30 days using survival analysis. Results: We enrolled 62 patients. Patients with a low VTI-VeXUS index had higher rates of left ventricular dysfunction (32.3% vs. 3.2%, p = 0.006), right ventricular dysfunction (35.5% vs. 0.0%, p < 0.001), lower stroke volume (54.0 mL vs. 62.0 mL, p = 0.005), and increased 30-day mortality (adjusted HR: 3.86, 95% CI 1.23 to 12.14). Conclusions: In this exploratory proof-of-concept study, a low VTI-VeXUS index was associated with ventricular dysfunction and increased mortality. While limited by small sample size and univariate analysis, these findings suggest this novel integrated metric warrants validation in larger prospective studies.

Background: Endotracheal tube blockages (ETBs) are a common yet often overlooked cause of weaning failure, ventilator dyssynchrony, and hypoxia in the ICU, with limited studies on their prevalence, clinical factors, and outcomes. Research Question: What are the incidence, risk factors, and associated clinical and ventilator factors of ETBs in ventilated patients in the ICU? Study Design and Methods We assessed 369 endotracheal tubes (ETTs) of mechanically ventilated patients after extubation. This prospective observational study was conducted at the tertiary cardiothoracic ICUs (CICUs) and medical ICUs (MICUs) of Narayana Health City, Bengaluru, India. Tubes were inspected visually and were cut at the point of maximum blockage, and cross-sectional images captured with a 12-megapixel camera were analyzed for ETB percentage using the SketchAndCalc algorithm. Results: Of the 369 ETTs assessed, ETBs were categorized as showing low (0%-9%), moderate (10%-49%), and severe (> 50%) occlusion. In the CICU, severe ETBs was observed in < 2% of patients, compared with 4% of patients in the MICU, whereas moderate ETBs were present in 27.9% of patients in the CICU and 16.5% of patients in the MICU. On univariable analysis, suction type (beta = 9.62 [95% CI, 5.27-13.98]; P < .01), peak pressure (P-peak; beta = 1.73 [95% CI, 1.38-2.08]; P < .01), coagulopathy (beta = 9.42 [95% CI, 4.22-14.62]; P < .01), and ICU type (beta = 9.62 [95% CI, 5.28-13.96]; P < .01) were statistically significant. Multivariable regression analysis showed only P-peak (beta = 1.65 [95% CI, 1.28-2.02]; P < .01), coagulopathy (beta = 8.02 [95% CI, 3.26-12.79]; P < .01) and larger number of days receiving invasive mechanical ventilation (beta = 0.02 [95% CI, 0.01-0.03]; P < .01) to be significant factors associated with ETB percentage. Interpretation: Moderate ETB was more prevalent in patients in the ICU, with significant factors including coagulopathy, closed suction practice, and mechanical ventilation duration. P-peak alarms lacked clinical impact, despite statistical significance.

OBJECTIVE:To determine the impact of using dynamic measures of fluid responsiveness in guiding the resuscitation of adult patients with sepsis and septic shock. DATA SOURCE:We searched MEDLINE, Embase, and unpublished sources from inception to February 3, 2025. STUDY SELECTION:We included randomized controlled trials (RCTs) that evaluated the use of dynamic measures of fluid responsiveness to guide resuscitation compared with any other method in patients with sepsis and septic shock. DATA EXTRACTION:We collected data regarding study and patient characteristics, definitions of fluid responsiveness, modality for assessing fluid responsiveness, and outcome data. We performed a random-effects meta-analysis and rated the certainty of the evidence using the Grading of Recommendations Assessment, Development, and Evaluation framework. DATA SYNTHESIS:We included nine eligible RCTs (n = 698 patients). The use of dynamic measures of fluid responsiveness to guide IV fluid (IVF) administration of patients with septic shock probably reduces 28-day mortality (relative risk] 0.61; 95% CI, 0.42-0.90, moderate certainty), may reduce the risk of acute kidney injury (AKI) (RR 0.66; 95% CI, 0.44-0.98, low certainty), and cumulative fluid balance on day 3 (mean difference -1.57L; 95% CI, -2.44 L to -0.69 L, low certainty). The use of dynamic measures of fluid responsiveness has an uncertain effect on ICU mortality, ICU and hospital length of stay, need for and duration of mechanical ventilation, need for renal replacement therapy, vasoactive medication administration, duration of vasopressor use, and IVF administration on day 1. CONCLUSIONS:In adult patients with sepsis and septic shock, using dynamic measures of fluid responsiveness may improve survival and reduce the risk of AKI. Future studies should evaluate the impact of this intervention on other important clinical outcomes and determine the comparative efficacy of specific modalities for assessing fluid responsiveness.

OBJECTIVES:To determine the impact of short-acting beta-blocker therapy on outcomes in adult patients with septic shock. DATA SOURCES:We searched MEDLINE, Embase, and unpublished sources from inception to April 19, 2024. STUDY SELECTION:We included randomized controlled trials (RCTs) that evaluated short-acting beta-blockers compared with usual care in patients with septic shock. DATA EXTRACTION:We collected data regarding study and patient characteristics, beta-blocker administration, and clinical, hemodynamic, and biomarker outcomes. DATA SYNTHESIS:Twelve RCTs proved eligible ( n = 1170 patients). Short-acting beta-blockers may reduce 28-day mortality (relative risk [RR], 0.76; 95% CI, 0.62-0.93; low certainty) and probably reduce new-onset tachyarrhythmias (RR, 0.37; 95% CI, 0.18-0.78; moderate certainty) but may increase the duration of vasopressors (mean difference [MD], 1.04 d; 95% CI, 0.37-1.72; low certainty). Furthermore, there is an uncertain effect as to whether short-acting beta blockers impact 90-day mortality (RR, 0.98; 95% CI, 0.73-1.31), ICU length of stay (MD, -0.75 d; 95% CI, -3.43 to 1.93 d), hospital length of stay (MD, 1.03 d; 95% CI, -1.92 to 3.98 d), duration of mechanical ventilation (MD, -0.10 d; 95% CI, -1.25 to 1.05 d) (all very low certainty), bradycardia episodes (RR, 3.14; 95% CI, 0.91-14.01), and hypotension episodes (RR, 4.74; 95% CI, 1.62-14.01) (all very low certainty). CONCLUSIONS:In patients with septic shock, short-acting beta-blockers may improve survival and reduce new-onset tachyarrhythmias. However, these findings were based on low certainty evidence and given ongoing concerns regarding adverse effects and the increase duration of vasopressor use, we need larger and more rigorous RCTs to evaluate this intervention.

We sought to conduct a systematic review to determine the diagnostic test accuracy of point-of-care ultrasound (POCUS) for the specific etiologies and subtypes of shock. We searched MEDLINE, Embase, and the grey literature for prospective studies in adult populations with shock. We collected data on study design, patient characteristics, operator characteristics, POCUS protocol, and true and false positives and negatives, and assessed the risk of bias. We found 18 eligible studies with a total of N = 2,088 patients. The pooled sensitivity and specificity of POCUS for determining shock subtype were 90 CRD42020160001 ); first submitted 1 December 2019.

BACKGROUND:Persistently increasing healthcare spending, paired with growing healthcare demand, highlights the need to identify mechanisms for cost savings. Chest radiography (CXR) is commonly performed following intrathoracic procedures to rule out pneumothorax (PTX) even if the clinical pretest probability is low. However, lung ultrasound (LUS) is known to have superior sensitivity, possibly representing a promising cost-saving tool. In response, we conducted an economic analysis comparing LUS and CXR to exclude PTX after invasive intrathoracic procedures. METHODS:A retrospective review of the radiology case-costing center was performed at an academic cardiothoracic surgical institution to identify the activity and cost of CXRs performed to rule out PTX following intrathoracic procedures. This cost was then compared to the theoretical cost of LUS. RESULTS:CXRs performed to rule out iatrogenic PTX were common with 22,274 radiographs completed and were economically burdensome, with an associated cost of $1.4 million. Portable CXR cost $75.46 per test, while CXR posteroanterior/lateral costs $41.64. Comparatively, LUS cost $38.38. Implementation would lead to cost savings of $559,537.10 or $41.58, on average, per patient. CONCLUSION:Given the superiority of LUS in terms of sensitivity and accuracy for PTX diagnosis, these findings underscore the compelling rationale for its broader integration into clinical practice.

The Venous Excess Ultrasound Score (VExUS) has produced great interest in venous Doppler ultrasound as a noninvasive means to evaluate right heart hemodynamics. While this score includes Doppler morphologies from sub-diaphragmatic veins, the physiology of transcutaneous venous Doppler velocimetry and its change with cardiac pathology was first studied and described in the internal jugular vein (IJV). Over 50 years ago, the systolic and diastolic velocity waves of the IJV were found to describe the x'- and y-descents of the jugular venous pulse (JVP) in sickness and in health. Therefore, it is established that abnormalities in right heart filling and function are reflected in the jugular venous flow velocity (JVFV) profile. In this narrative review, we highlight the physiology of the JVP, its relationship to right heart performance, and, accordingly, its connection with JVFV. Grounded upon decades-old, pioneering investigations, we briefly highlight JVFV in patients with post-cardiopulmonary bypass physiology, atrial fibrillation, pericardial tamponade, and pulmonary hypertension. We then describe a novel, wireless, and wearable Doppler ultrasound that continuously displays JVFV and consider how this device informs diagnosis and therapy of acute circulatory dysfunction. We touch on gaps in knowledge and suggest future avenues of inquiry with special attention paid to synchronous acquisition and interpretation of venous and arterial Doppler measures. We emphasize the clinical relevance of this technology and physiological framework, including acute, inpatient shock resuscitation, volume removal (eg, "de-resuscitation"), and the possibility for chronic, outpatient monitoring.

Background Large language models (LLMs) are capable of processing extensive textual data and synthesizing evidence to answer complex clinical questions. The labor-intensive nature of systematic reviews with meta-analyses (SRMAs) present a unique opportunity to evaluate the utility of LLMs as a novel method for evidence synthesis. Objective This study assessed the ability of OpenAI's o3 DeepResearch model to approximate the direction of effect, magnitude of effect and certainty of evidence for clinical questions addressed by published meta-analyses in top critical care medicine journals. Methods We constructed standardized prompts based on the PICO (Population, Intervention, Comparator, Outcome) from a convenience sample of 23 systematic reviews with meta-analyses published in high-impact critical care journals. The LLM's estimates of effect size and certainty of evidence ratings were compared to those reported in the original SRMAs. Results The LLM demonstrated a concordance rate of 83 % (19 of 23 studies) for the magnitude of effect size and 91 % (21 of 23 studies) for the direction of effect. Concordance for certainty of evidence was also 91 %. Discrepancies were due to differences in study selection between the LLM and SRMAs, rather than model hallucination or misinterpretation. Conclusions LLMs show promise as a new tool for rapid evidence synthesis in critical care, with outputs comparable to traditional meta-analyses in many cases. While not a replacement for systematic reviews, LLMs may enhance clinical decision-making, perform rapid evidence synthesis, and streamline future research workflows.