Additional file 1: of The effects of ipsilateral tilt position on right subclavian venous catheterization: study protocol for a prospective randomized trial

Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) Checklist. (DOC 125 kb

Calibration plot of the prediction in the holdout validation cohort.

GBM, Gradient-boosting model; LSTM, Long short-term memory.</p

Ability of models in predicting intraoperative hypoxemia in pediatric patients.

Ability of models in predicting intraoperative hypoxemia in pediatric patients.</p

Additional file 1 of Real-time machine learning model to predict short-term mortality in critically ill patients: development and international validation

Additional file 1. Table E1-7 and Figure E1-10

Characteristics of remifentanil infusion rate groups.

Characteristics of remifentanil infusion rate groups.</p

Baseline characteristics in the dataset.

BackgroundReducing the duration of intraoperative hypoxemia in pediatric patients by means of rapid detection and early intervention is considered crucial by clinicians. We aimed to develop and validate a machine learning model that can predict intraoperative hypoxemia events 1 min ahead in children undergoing general anesthesia.MethodsThis retrospective study used prospectively collected intraoperative vital signs and parameters from the anesthesia ventilator machine extracted every 2 s in pediatric patients undergoing surgery under general anesthesia between January 2019 and October 2020 in a tertiary academic hospital. Intraoperative hypoxemia was defined as oxygen saturation ResultsIn total, 1,540 (11.73%) patients with intraoperative hypoxemia out of 13,130 patients’ records with 2,367 episodes were included for developing the model dataset. After model development, 200 (13.25%) of the 1,510 patients’ records with 289 episodes were used for holdout validation. Among the models developed, the GBM had the highest AUROC of 0.904 (95% confidence interval [CI] 0.902 to 0.906), which was significantly higher than that of the LSTM (0.843, 95% CI 0.840 to 0.846 P P P P ConclusionsMachine learning models can be used to predict upcoming intraoperative hypoxemia in real-time based on the biosignals acquired by patient monitors, which can be useful for clinicians for prediction and proactive treatment of hypoxemia in an intraoperative setting.</div

Changes in pain scores and analgesic consumption for 48 hours after robotic thyroidectomy.

A: Postoperative pain is quantified by 11-point (0–11) numeric rating scale. Data are expressed as mean ± SD (symbol and error bar). B: The number of patients requiring analgesic for each hour is divided by the total number of patients. Data are expressed as incidence (%).</p

Basic characteristics (n = 635).

Basic characteristics (n = 635).</p

Flow chart presenting patient selection and data analysis.

PIP, Peak Inspiratory Pressure; EtCO2 End tidal CO2; FiO2, Fraction of inspired oxygen.</p

Changes in pain scores, analgesic consumption, and incidence of treatment-requiring pain in two remifentanil groups for 48 hours after robotic thyroidectomy.

Postoperative pain was quantified by 11-point (0–11) numeric rating scale. Treatment-requiring pain was defined when numeric rating scale of the pain is greater than 4. A, B and C: Two remifentanil categories are not easily distinguishable in terms of pain scores, analgesic use and treatment-requiring pain incidence. D: Time dependent Cox proportional hazards regression analysis identified that the risk of treatment-requiring pain was 1.3 times higher in the high-dose remifentanil group than in the low-dose group after adjusting for analgesic consumption and its interaction with time.</p