Frailty and Intraoperative Hypoxemia Risk in Elderly ERCP Patients

Overview

Intraoperative hypoxemia is a common and serious complication during monitored anesthesia care for elective endoscopic retrograde cholangiopancreatography (ERCP), especially in older adults. Frail patients have reduced physiologic reserve and are at increased risk of life-threatening events including unplanned intubation, hemodynamic collapse, and hypoxic brain injury. However, the independent relationship between frailty and intraoperative hypoxemia during sedated ERCP remains poorly defined, and no validated risk prediction model exists for this high-risk population. This single-center, dual-cohort study will be conducted at China-Japan Friendship Hospital to evaluate whether preoperative frailty can predict intraoperative hypoxemia in patients aged 65 years and older undergoing elective ERCP. Frailty will be assessed primarily using the Frailty Index (FI), with secondary screening by the Modified Frailty Index-11 (mFI-11), Edmonton Frail Scale (EFS), Clinical Frailty Scale (CFS), FRAIL scale, and Fried phenotype. The primary outcome is intraoperative hypoxemia, defined as SpO₂ \< 90% for 5 seconds or longer. In the retrospective phase (n = 313), investigators will identify risk factors and develop a preliminary prediction model. In the prospective phase (n = 388), the model will be validated, refined, and internally tested for discrimination, calibration, and clinical utility. This study aims to establish frailty as a reliable preoperative risk marker and provide a clinically practical prediction tool to support risk stratification, personalized sedation strategies, and perioperative safety in elderly patients undergoing ERCP.

This dual-cohort study investigates the predictive value of preoperative frailty for intraoperative hypoxemia in patients aged 65 years and older undergoing elective endoscopic retrograde cholangiopancreatography (ERCP) under monitored anesthesia care at China-Japan Friendship Hospital, with a total target enrollment of 701 patients (313 retrospective and 388 prospective). Intraoperative hypoxemia, defined as pulse oxygen saturation (SpO₂) \< 90% for 5 seconds or longer from anesthesia induction to procedure completion, serves as the primary outcome, while secondary outcomes include severe hypoxemia, postoperative hypoxemia, time to oxygen discontinuation, hemodynamic instability, anesthesia- and procedure-related adverse events, respiratory depression, and apnea. Frailty will be assessed primarily using the Frailty Index (FI) as the core instrument, with additional secondary screening performed using the Modified Frailty Index-11 (mFI-11), Edmonton Frail Scale (EFS), Clinical Frailty Scale (CFS), FRAIL questionnaire, and Fried frailty phenotype. Eligible patients are aged 65 years or older with ASA class II-IV status, scheduled for elective ERCP with a planned procedure duration of less than 60 minutes and complete intraoperative SpO₂ monitoring data; those requiring emergency surgery, severe anemia, pre-existing severe respiratory or hemodynamic instability, or impaired consciousness will be excluded. The retrospective phase will use existing electronic health records to identify risk factors and construct a preliminary prediction model, with propensity score matching, univariate and multivariable logistic regression, bootstrap internal validation, and random forest for feature importance ranking. The prospective phase will prospectively collect standardized data, validate the association between frailty and hypoxemia using adjusted logistic regression, refine the prediction model, compare the performance of different frailty scales using Delong's test, and evaluate machine learning models with SHapley Additive exPlanations (SHAP) values for interpretability. The final model will be tested in an internal validation cohort for discriminative ability, calibration, and clinical utility. All statistical analyses will be performed using R and SPSS software at a two-sided significance level of α = 0.05, with multiple imputation for missing data and Holm-Bonferroni correction for multiple comparisons.