Daily Anesthesiology Research Analysis

BACKGROUND: Patients with obstructive sleep apnea (OSA) are at high risk for rapid oxygen desaturation during anesthesia induction. Apneic oxygenation with high-flow nasal oxygen (HFNO) has proven effective in prolonging safe apnea time in various patient populations. However, evidence for the efficacy of HFNO in OSA patients remains limited. This study aimed to evaluate whether the use of HFNO during anesthesia induction in OSA patients prolongs safe apnea time. METHODS: In this prospective randomized clinical trial, all participants underwent standardized pre-oxygenation and anesthesia induction. During the apneic period, oxygen was delivered either via HFNO at 60L/min (HFNO group) or with the nasal cannula left in place but disconnected from the oxygen source (control group, no supplemental oxygen). The primary outcome of this study was the time to peripheral oxygen desaturation (SpO RESULTS: The HFNO group demonstrated a significantly prolonged safe apnea time compared to the Control group (18.1 [12.1,18.8] vs 4.2 [2.5,6.3] minutes; CONCLUSION: In this randomized controlled trial, HFNO significantly prolongs safe apnea time and enhances oxygenation during anesthesia induction in OSA patients. These findings highlight the potential of HFNO to improve perioperative airway management and patient safety in this high-risk population.

BACKGROUND: Anaesthesiology and intensive care use monitoring to identify patients in danger of deterioration. Traditionally, trends and early warning scores allow clinicians to predict deterioration with moderate reliability. Reduced mean arterial blood pressure has been associated with complications, and models have been sought to predict its value. Machine learning methods with complex inputs have been used for predictive monitoring in hospital care. OBJECTIVES: This study evaluates whether machine learning can predict mean arterial pressure (MAP) from previous values. DESIGN: This is a monocentre, retrospective, exploratory, observational cohort study using the MIMIC-III-WDB, VitalDB and an internal study centre dataset, training machine learning models on adult patients with invasively measured blood pressure (IBP) as input during an observation window up to 20 min before the prediction horizon (5 to 20 min). SETTING: Kepler University Hospital, Linz, Austria. PARTICIPANTS: Two thousand three hundred and forty-six patients from the internal dataset, 4741 patients from MIMIC-III-WDB and 3357 patients from VitalDB were analysed. MAIN OUTCOME MEASURES: The primary endpoint was model performance in predicting whether MAP would fall below 65 mmHg in a given time frame. In a secondary analysis, we restricted the input set to stable patients with current MAP above 65 mmHg. RESULTS: Models using the complete training data achieved receiver operating characteristic area under the curves (ROC AUCs) of 0.963, 0.946, 0.934 and 0.923 on the internal dataset for 5, 10, 15 and 20 min of prediction horizon, respectively, and 0.856, 0.837, 0.821 and 0.804 in the secondary analysis. The maximum difference of ROC AUC to baseline measurement (ROC AUC of last measured MAP as trivial estimator) was 0.006 for the complete training data and 0.051 for stable patients. The prediction of MAP may allow clinicians to intervene in time before MAP deterioration becomes clinically relevant. CONCLUSION: Predicting MAP below 65 mmHg within 5, 10, 15 and 20 min for patients with and without a MAP above 65 mmHg is possible and requires only MAP as input for machine learning models. TRIAL REGISTRATION: ClinicalTrials.gov (NCT05471193).

INTRODUCTION: With over 300 million surgical procedures performed worldwide annually and an ageing population with increasing comorbidities, peri-operative risk assessment is more important than ever. Whilst the ASA physical status is used widely to assess surgical risk, its association with age remains underexplored in contemporary, broad surgical populations. This study examines the relationship between ASA physical status, age and postoperative mortality in an adult surgical cohort. METHODS: This nationwide cohort study analysed data from the Swedish Perioperative Register on patients aged ≥ 18 y undergoing major non-cardiac surgery from January 2019 to March 2023. Data on comorbidities, socioeconomic factors and mortality were retrieved from national health registries. The primary outcome was 30-day mortality. Secondary outcomes were 365-day mortality and days at home alive at 30 days. RESULTS: A total of 262,938 elective and 197,108 acute procedures were analysed, with median ages of 66 and 68 y, respectively. Crude mortality rates in elective surgery were 1369 (0.5%) at 30 days and 10,437 (4.0%) at 365 days. For acute surgery, mortality was 10,602 (5.4%) at 30 days and 27,912 (14.2%) at 365 days. Adjusted odds ratios (OR) for 30-day mortality indicated a 14-fold increased risk for ASA physical status 3 compared with ASA physical status 1 in both elective (OR 13.7, 95%CI 7.5-25.0) and acute (OR 14.0, 95%CI 10.2-19.3) surgeries. Correspondingly, ASA physical status ≥ 4 was associated with odds ratios of 62.2 (95%CI 33.5-115.5) for elective and 51.1 (95%CI 37.1-70.3) for acute surgery. DISCUSSION: As populations age and surgical demand increases, continuous evaluation of risk assessment tools like the ASA physical status is essential. This study shows a strong association between ASA physical status and mortality across all ages in a contemporary adult surgical cohort. These findings could enhance our understanding of peri-operative risk stratification in the context of shifting demographic trends. Every year, over 300 million people around the world have surgery. As people get older and have more health problems, it becomes more important to check how risky surgery might be for them. Doctors often use something called the ASA physical status to help decide how risky a surgery is. But we don't fully understand how this rating is connected to a person's age. This study looks at how American Society of Anesthesiology (ASA) scores, age, and the chances of dying after surgery are related. This study looked at surgery data from across Sweden. It included people 18 years and older who had big surgeries (but not heart or obstetric surgeries) between January 2019 and March 2023. The main thing the study looked at was whether people were alive 30 days after surgery. It also looked at whether they were alive after one year and how many days they spent at home in the first 30 days after surgery. The study looked at 262,938 planned surgeries and 197,108 emergency surgeries. In planned surgeries, 0.5% of people died within 30 days, and 4.0% died within a year. In emergency surgeries, 5.4% died within 30 days, and 14.2% died within a year. People with worse ASA scores (which means worse health) had a much higher chance of dying after surgery. For example, people with ASA score 3 were about 14 times more likely to die within 30 days than people with score 1. People with ASA score 4 or higher were more than 50 times more likely to die within 30 days. This study shows that people with higher ASA scores are more likely to die after surgery, no matter their age. This helps doctors better understand who might need extra care before and after surgery.