Background: Laparoscopic surgery in the lateral decubitus position can alter pulmonary mechanics and oxygenation. Although positive end-expiratory pressure (PEEP) may alleviate these effects, the optimal level remains unclear. This study evaluated whether electrical impedance tomography (EIT)–guided PEEP titration improves oxygenation compared to a fixed PEEP of 5 cmH2O. Methods: In this randomized controlled trial, 74 adult patients undergoing robot-assisted or laparoscopic urologic surgery in the lateral decubitus position were assigned to either the EIT-guided or standard care group. The EIT-guided group underwent decremental PEEP titration to determine and maintain optimal PEEP throughout surgery. The standard care group received a fixed PEEP of 5 cmH2O. The primary outcome was ratio of partial pressure of arterial oxygen to fraction of inspired oxygen (PaO2/FiO2) at the end of surgery. Secondary outcomes included intraoperative respiratory mechanics and postoperative pulmonary complications (PPCs) until discharge. Results: Seventy-one patients completed the study (EIT-guided: 35, standard care: 36). The PaO2/FiO2 ratio at the end of surgery was higher in the EIT-guided group than in the standard care group (523.8 ± 82.4 vs. 414.6 ± 96.7 mmHg, P < 0.001). Driving pressure was lower in the EIT-guided group at 30 min after pneumoperitoneum initiation (15.8 [12.5, 17.4] vs. 19.9 [17.2, 22.5] cmH2O, P < 0.001) and at the end of surgery (9.1 [8.0, 10.4] vs. 10.0 [8.8, 12.6] cmH2O, P = 0.033). PPCs did not differ between groups. Conclusions EIT-guided PEEP titration improved intraoperative oxygenation. Further studies are needed to assess clinical outcomes.

Background: Conventional machine learning (ML) models for predicting surgical outcomes have limitations in generalizability. We explored large language models (LLMs) as scalable alternatives to conventional ML models in predicting postoperative outcomes, including in-hospital 30-day mortality, intensive care unit (ICU) admission, and acute kidney injury (AKI). Methods: This study utilized the Informative Surgical Patient for Innovative Research Environment (INSPIRE) dataset (n = 80 985) from South Korea for model development and internal validation, and the Medical Informatics Operating Room Vitals and Events Repository (MOVER) dataset (n = 6265) from the United States for external validation. The study compared three different LLMs—Generative Pre-trained Transformer [GPT]-4o, Llama-3-70B, and OpenBioLLM-70B—against MLs using various prompt engineering approaches. LLMs were evaluated with different model parameter quantizations (4-bit normalized floating point vs. 16-bit brain floating point). Results: OpenBioLLM-70B was comparable to eXtreme Gradient Boosting (XGBoost) across all tasks (in-hospital 30-day mortality: area under receiver operating characteristic curve [AUROC] 0.782 [95% CI, 0.748–0.813] vs. 0.791 [95% CI, 0.753–0.825]; ICU admission: AUROC 0.595 [95% CI, 0.581–0.609] vs. 0.594 [95% CI, 0.580–0.608]; AKI: AUROC 0.830 [95% CI, 0.802–0.855] vs. 0.823 [95% CI, 0.792–0.851]) during external validation. Open-source LLMs maintained performance with 4-bit quantization, reducing computational requirements by 75%. Conclusions The findings support the versatility and efficiency of LLMs for clinical decision support through on-premises compatibility, addressing data privacy. Further validation with diverse datasets is needed to ensure their reliability and applicability across different perioperative settings.

Background: Use of endotracheal tubes (ETTs) with appropriate size and depth can help minimize intubation-related complications in pediatric patients. Existing age-based formulae for selecting the optimal ETT size present several inaccuracies. We developed a machine learning model that predicts the optimal size and depth of ETTs in pediatric patients using demographic data, enabling clinical applications. Methods: Data from 37,057 patients younger than 12 years who underwent general anesthesia with endotracheal intubation were retrospectively analyzed. Gradient boosted regression tree (GBRT) model was developed and compared with traditional age-based formulae. Results: The GBRT model demonstrated the highest macro-averaged F1 scores of 0.502 (95% CI 0.486, 0.568) and 0.669 (95% CI 0.640, 0.694) for predicting the uncuffed and cuffed ETT size (internal diameter [ID]), outperforming the age-based formulae that yielded 0.163 (95% CI 0.140, 0.196, P < 0.001) and 0.392 (95% CI 0.378, 0.406, P < 0.001), respectively. In predicting the ETT depth (distance from tip to lip corner), the GBRT model showed the lowest mean absolute error (MAE) of 0.71 cm (95% CI 0.69, 0.72) and 0.72 cm (95% CI 0.70, 0.74) compared to the age-based formulae that showed an error of 1.18 cm (95% CI 1.16, 1.20, P < 0.001) and 1.34 cm (95% CI 1.31, 1.38, P < 0.001) for uncuffed and cuffed ETT, respectively. Conclusions The GBRT model using only demographic data accurately predicted the ETT size and depth. If these results are validated, the model may be practical for predicting optimal ETT size and depth for pediatric patients.