Daily Anesthesiology Research Analysis

OBJECTIVE: The Pao2/Fio2 (PF) ratio is widely used as an assessment of respiratory failure in guiding ventilation strategies and prognostication in critically ill patients. However, given that it mandates invasive arterial access, the Spo2/Fio2 (SF) ratio has been suggested as a noninvasive and readily accessible alternative. What are the best ways to convert SF and PF ratios in critically ill patients, in terms of their diagnostic/prognostic accuracy and clinical utility? DATA SOURCES: We comprehensively searched databases (MEDLINE, Embase, Web of Science, Cochrane library) to identify relevant studies. STUDY SELECTION: Any observational studies that compared the SF to PF ratio in critically ill patients. We assessed individual study risk of bias (ROB) using the revised QUADAS II tool. DATA EXTRACTION: We included 45 observational studies, ranging from 61 to 141,000 measurements. DATA SYNTHESIS: SF to PF imputation was less accurate when the Spo2 was equal to or greater than 97%. Otherwise, all studies were able to establish strong correlational relationships between SF and PF ratios, but there was no clear best equation. Based on ease of use, size, generalizability and methodology, we were able to prioritize four equations (one linear, one logarithmic linear, and two nonlinear). All four equations showed strong correlation between SF and PF ratios, with the linear equation being easiest to apply. The SF ratio also correlated well with clinical outcomes when compared with the PF ratio, both as an individual value and as part of a comprehensive score, with more discriminating performance in some cases. CONCLUSIONS: SF and PF ratios demonstrate good correlation, and may have similar prognostic value. Although there is no clear optimal method to convert SF to PF ratios, linear equations show acceptable correlation and are most easily applied at the bedside.

IMPORTANCE: Hospitals are increasingly experiencing challenges with variable and unpredictable inpatient loads, including days with excessively high and excessively low capacity for surgical patients. Artificial intelligence has the potential to facilitate postoperative hospital bed management and stabilize capacity. OBJECTIVES: To predict hospital length of stay (LOS) following elective surgical procedures using machine learning methods, and to implement the LOS prediction model in a perioperative clinical setting and evaluate its ability to optimize elective surgical scheduling and hospital bed capacity. DESIGN, SETTING, AND PARTICIPANTS: This preimplementation and postimplementation cohort study was conducted at a tertiary, freestanding, US children's hospital among patients of any age undergoing an elective surgical procedure requiring inpatient recovery. For LOS prediction, a retrospective analysis was performed on elective surgical cases from January 1, 2018, to March 31, 2022, using Extreme Gradient Boosting (XGBoost) to predict postoperative LOS based on in-training and holdout datasets, with hyperparameter tuning using 5-fold cross-validation. For implementation and evaluation of the LOS prediction model, a preimplementation and postimplementation analysis was performed from July 1, 2022, to April 30, 2024. Data analysis was conducted from June 1 to October 31, 2024. EXPOSURES: Patients' type of surgery, chronic conditions, and demographic characteristics. MAIN OUTCOMES AND MEASURES: Postoperative LOS, day-to-day variance in bedded days for elective surgical procedures, and days with excessively high capacity (>75th percentile of historical elective surgical census) or excessively low capacity (<25th percentile of historical elective surgical census). RESULTS: There were 21 352 elective surgical cases (mean [SD] age, 10.2 [7.4] years; 10 804 [50.6%] female) for patients included in the retrospective analysis of postoperative LOS prediction and 12 522 elective surgical cases in the pretest and posttest analysis of the prediction model (premodel implementation, n = 5867; postmodel implementation, n = 6655). The postoperative LOS model had 85.6% accuracy with a 1-night leniency. The model's mean absolute error was 0.6 days. After implementation of the LOS model in elective surgery scheduling and hospital bed capacity management, the median number of elective surgical procedures increased by 5 (IQR, 4.5-5) for each weekday. Variation in postoperative bedded days across days of the week decreased significantly. The magnitude of the IQR of bedded days decreased the most during midweek: 43% and 44% reductions in the IQR occurred on Wednesdays and Thursdays, respectively. The percentage of weekdays with underused capacity (<84 patients) decreased from 33% to 10% (P < .001), without a significant increase in days with excessive capacity. CONCLUSIONS AND RELEVANCE: In this cohort study, use of a machine-learning, postoperative LOS model helped to reduce day-to-day variation in the number of elective surgical procedures performed, increase the total number of elective surgical procedures, and decrease underuse of hospital beds.

BACKGROUND: This randomized trial aimed to determine whether achievement of individualized blood pressure targets through norepinephrine administration can mitigate acute kidney injury (AKI) and reduce urine concentration, which serves as a surrogate marker for fluid retention, in older patients undergoing major abdominal surgery. METHODS: This study included 166 patients aged 55-80 years who were scheduled to undergo gastrectomy or colorectal cancer resection. They were randomly assigned to the individualized care group, in which systolic blood pressure or mean arterial pressure was maintained within ± 10% and ± 20% of baseline, respectively, using norepinephrine; or to the usual care group, in which mean arterial pressure was maintained at ≥ 65 mmHg without individualized titration. Individuals in both groups underwent continuous arterial monitoring and goal-directed fluid therapy. AKI was diagnosed based on the Kidney Disease: Improving Global Outcomes criteria. Further, renal injury was assessed based on serum neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1) levels. Urine concentration scores were calculated from the urine parameters. RESULTS: AKI occurred in 13.5% and 14.3% of patients in the individualized and usual care groups, respectively. Patients in the usual care group experienced significant intraoperative fluctuations in mean arterial pressure. Norepinephrine was administered to 80.9% and 17.1% of patients in the individualized and usual care groups, respectively. Compared with the usual care group, the individualized care group demonstrated reduced NGAL levels and KIM-1 levels at the end of surgery and on postoperative day 2, and lower urine concentration scores at the end of surgery and on postoperative day 1. CONCLUSION: Individualized blood pressure management using norepinephrine mitigated kidney damage, as indicated by elevated biomarkers, and decreased urine concentration, a surrogate indicator of fluid retention. However, it did not significantly reduce the incidence of AKI. TRIAL REGISTRATION: This trial was registered in the Chinese Clinical Trial Registry (ChiCTR2100049843) on August 10, 2021 (https://www.chictr.org.cn/).