Conduit choice in open repair of iliac artery injuries: a comparative analysis of in-hospital outcomes from the National Trauma Data Bank

Journal of Vascular Surgery · 2026-04-01

Determining potential immunomodulatory drug efficacy in sepsis using ELISpot

Scientific Reports · 2025-04-18 · 6 citations

This study evaluated the ability of ELISpot to identify potential immuno-modulatory drug therapies in sepsis. ELISpot was performed ex vivo on whole blood from septic patients and healthy controls. Innate and adaptive immunity were evaluated by production of TNF-α and IFN-γ, respectively. Drug efficacy was determined by their effects to modulate the both the number of cytokine-producing cells and amount of cytokine produced per cell. The corticosteroid dexamethasone was evaluated for its ability to down modulate TNF-α and IFN-γ production. The TLR7/8 agonist resiquimod (R848) and T cell stimulants IL-7 and anti-PD-1 mAb were tested for their ability to enhance immunity. LPS and resiquimod increased total TNF-α production in septic patients by 1,549% and 1,829%, respectively. Conversely, dexamethasone diminished the responses to LPS or resiquimod by 75% and 61%, respectively. IL-7, but not anti-PD-1 mAb markedly increased IFN-γ production in both healthy subjects (121%) and septic patients (82%). Dexamethasone also reduced anti-CD3/CD28 mAb stimulated IFN-γ production by 69%; while IL-7 ameliorated dexamethasone-induced suppression. IL-7 significantly enhanced lymphocyte function in over 90% of septic patients. ELISpot can reveal host immune response patterns and the effects of drugs to selectively down- or up-regulate patient immunity. Furthermore, the ability of ELISpot to detect the effect of specific immuno-modulatory drugs to independently regulate the innate and adaptive host response could enable precision-based immune drug therapies in sepsis.

Quantifying Nuance Within Sepsis-Associated Immune Suppression Toward Diagnostic Certainty

Shock · 2025-10-21

Plain Language Summary Summary: Impact of sepsis on human T-cell function and outcome was assessed. In sepsis survivors, IFNγ-production in response to T-cell stimulation remains intact, while sepsis non-survivors display exaggerated IFNγ responses to TCR-independent stimuli. Plain Language SummaryThis study explored how sepsis affects T-cell function and patient outcomes. Researchers found that sepsis survivors maintain normal IFNγ production when their T-cells are stimulated, indicating intact immune function. However, those who did not survive sepsis showed excessive IFNγ responses to stimuli that do not involve T-cell receptor (TCR) activation, suggesting a dysregulated immune response. These findings highlight differences in immune function between sepsis survivors and non-survivors, which could inform future treatments and interventions for sepsis patients. Text is machine generated and may contain inaccuracies. FAQ

Language Models for Multilabel Document Classification of Surgical Concepts in Exploratory Laparotomy Operative Notes: Algorithm Development Study

JMIR Medical Informatics · 2025-07-09 · 4 citations

Background: Operative notes are frequently mined for surgical concepts in clinical care, research, quality improvement, and billing, often requiring hours of manual extraction. These notes are typically analyzed at the document level to determine the presence or absence of specific procedures or findings (eg, whether a hand-sewn anastomosis was performed or contamination occurred). Extracting several binary classification labels simultaneously is a multilabel classification problem. Traditional natural language processing approaches-bag-of-words (BoW) and term frequency-inverse document frequency (tf-idf) with linear classifiers-have been used previously for this task but are now being augmented or replaced by large language models (LLMs). However, few studies have examined their utility in surgery. Objective: We developed and evaluated LLMs for the purpose of expediting data extraction from surgical notes. Methods: A total of 388 exploratory laparotomy notes from a single institution were annotated for 21 concepts related to intraoperative findings, intraoperative techniques, and closure techniques. Annotation consistency was measured using the Cohen κ statistic. Data were preprocessed to include only the description of the procedure. We compared the evolution of document classification technologies from BoW and tf-idf to encoder-only (Clinical-Longformer) and decoder-only (Llama 3) transformer models. Multilabel classification performance was evaluated with 5-fold cross-validation with F1-score and hamming loss (HL). We experimented with and without context. Errors were assessed by manual review. Code and implementation instructions may be found on GitHub. Results: The prevalence of labels ranged from 0.05 (colostomy, ileostomy, active bleed from named vessel) to 0.50 (running fascial closure). Llama 3.3 was the overall best-performing model (micro F1-score 0.88, 5-fold range: 0.88-0.89; HL 0.11, 5-fold range: 0.11-0.12). The BoW model (micro F1-score 0.68, 5-fold range: 0.64-0.71; HL 0.14, 5-fold range: 0.13-0.16) and Clinical-Longformer (micro F1-score 0.73, 5-fold range: 0.70-0.74; HL 0.11, 5-fold range: 0.10-0.12) had overall similar performance, with tf-idf models trailing (micro F1-score 0.57, 5-fold range: 0.55-0.59; HL 0.27, 5-fold range: 0.25-0.29). F1-scores varied across concepts in the Llama model, ranging from 0.30 (5-fold range: 0.23-0.39) for class III contamination to 0.92 (5-fold range: 0.98-0.84) for bowel resection. Context enhanced Llama's performance, adding an average of 0.16 improvement to the F1-scores. Error analysis demonstrated semantic nuances and edge cases within operative notes, particularly when patients had references to prior operations in their operative notes or simultaneous operations with other surgical services. Conclusions: Off-the-shelf autoregressive LLMs outperformed fined-tuned, encoder-only transformers and traditional natural language processing techniques in classifying operative notes. Multilabel classification with LLMs may streamline retrospective reviews in surgery, though further refinements are required prior to reliable use in research and quality improvement.

Comparative Outcomes of Extremity Vascular Trauma Managed in Trauma Versus Vascular Hybrid Operating Rooms

Journal of Vascular Surgery · 2025-05-23

The Predictive Value of Basic Laparoscopic Evaluations on Complex Cases Using the Society for Improving Medical Procedural Learning Database

Journal of Surgical Research · 2025-05-27

Federated Learning for Predicting Major Postoperative Complications

Annals of Surgery Open · 2025-05-02 · 3 citations

Objective: To develop a robust model to accurately predict the risk of postoperative complications using clinical data from multiple institutions while ensuring data privacy. Background: Building accurate, artificial intelligence models to predict postoperative complications relies on accessibility of large-scale and diverse datasets, often restricted by privacy concerns. Methods: This retrospective cohort study includes adult patients admitted to University of Florida Health (UFH) hospitals in Gainesville (GNV) (n = 79,850) and Jacksonville (JAX) (n = 28,636) for all inpatient major surgical procedures. We developed federated learning models to predict 9 major postoperative complications and compared them with both local models trained on a single site and central models trained on a pooled dataset from 2 hospitals. Results: Our best-federated learning models using preoperative features achieved the area under the receiver operating characteristics curve values with 95% confidence interval (CI) ranging from 0.80 (95% CI, 0.79-0.80) for wound complications to 0.90 (95% CI, 0.90-0.91) for prolonged intensive care unit (ICU) stay at UFH GNV. At UFH JAX, these values ranged from 0.71 (95% CI, 0.70-0.72) for wound complications to 0.90 (95% CI, 0.88-0.92) for in-hospital mortality. Federated learning models achieved comparable discrimination to central models for all outcomes, except prolonged ICU stay, where the performance of the federated learning model was slightly better at UFH GNV and slightly worse at UFH JAX. Our federated learning models obtained comparable performance to the best local models. Conclusions: We show federated learning to be a useful tool to train robust postoperative outcome prediction models from large-scale data across 2 hospitals.

Age- and Sex- Driven Transcriptional and Metabolic Diversity in Myeloid-Derived Suppressor Cells After Mouse Sepsis

bioRxiv (Cold Spring Harbor Laboratory) · 2025-10-07

Sepsis induces profound immune dysregulation, often resulting in chronic critical illness characterized by persistent immunosuppression and poor outcomes. Myeloid-derived suppressor cells (MDSCs) are central mediators of this immunosuppressive phenotype, yet the influence of age and sex on their transcriptional and metabolic states remain poorly understood. Here, we employed single-cell RNA sequencing of splenic leukocytes from young (3-4 months) and older (18-24 months) adult male and female mice subjected to a clinically relevant murine sepsis model to define age- and sex-specific MDSC phenotypes. We identified significant differences regarding age and sex in MDSC expansion, transcriptome, canonical pathway activation, RNA velocity, mitochondrial metabolism, and predicted cell-cell communication after sepsis. Using drug2cell analysis of total leukocytes we also identified cohort-specific drug target profiles. These findings underscore the importance of age and sex in shaping sepsis-induced MDSC biology and suggest that personalized immunomodulatory strategies targeting MDSCs could improve sepsis outcomes.

Validation of the uChicago Health Inequity Classification System (CHI-CS): A multi-institution pilot study

Surgery · 2025-06-24

Learning optimal treatment strategies for intraoperative hypotension using deep reinforcement learning.

PubMed · 2025-05-27 · 1 citations

Importance: Traditional methods of surgical decision making heavily rely on human experience and prompt actions, which are variable. A data-driven system that generates treatment recommendations based on patient states can be a substantial asset in perioperative decision-making, as in cases of intraoperative hypotension, for which suboptimal management is associated with acute kidney injury (AKI), a common and morbid postoperative complication. Objective: To develop a Reinforcement Learning (RL) model to recommend optimum dose of intravenous (IV) fluid and vasopressors during surgery to avoid intraoperative hypotension and postoperative AKI. Design setting participants: We retrospectively analyzed 50,021 surgeries from 42,547 adult patients who underwent major surgery at a quaternary care hospital between June 2014 and September 2020. Of these, 34,186 surgeries were used for model training and internal validation while 15,835 surgeries were reserved for testing. We developed an RL model based on Deep Q-Networks to provide optimal treatment suggestions. Exposures: Demographic and baseline clinical characteristics, intraoperative physiologic time series, and total dose of IV fluid and vasopressors were extracted every 15-minutes during the surgery. Main outcomes: In the RL model, intraoperative hypotension (MAP<65 mmHg) and AKI in the first three days following the surgery were considered. Results: The developed model replicated 69% of physician's decisions for the dosage of vasopressors and proposed higher or lower dosage of vasopressors than received in 10% and 21% of the treatments, respectively. In terms of intravenous fluids, the model's recommendations were within 0.05 ml/kg/15 min of the actual dose in 41% of the cases, with higher or lower doses recommended for 27% and 32% of the treatments, respectively. The RL policy resulted in a higher estimated policy value compared to the physicians' actual treatments, as well as random policies and zero-drug policies. The prevalence of AKI was lowest in the patients who received medication dosages that aligned with our agent model's decisions. Conclusions and Relevance: Our findings suggest that implementation of the model's policy has the potential to reduce postoperative AKI and improve other outcomes driven by intraoperative hypotension.