摘要
BACKGROUND Postoperative delirium(POD)is a prevalent complication,particularly in elderly patients with hip fractures(HFs).It significantly affects recovery,length of hospital stay,healthcare costs,and long-term outcomes.Existing studies have investigated risk factors for POD,but most are limited by single-factor analyses or small sample sizes.This study systematically determines independent risk factors using large-scale data and machine learning techniques and develops a validated nomogram model to support early prediction and management of POD.AIM To investigate POD incidence in elderly patients with HF and the independent risk factors,according to which a nomogram prediction model was developed and validated.METHODS This retrospective study included elderly patients with HF who were surgically treated in Dongying People’sHospital from April 2018 to April 2022. The endpoint event includes POD. They were categorized into themodeling and validation cohorts in a 7:3 ratio by randomization. Both cohorts were further classified into thedelirium and normal (non-delirium) groups according to the presence or absence of the endpoint event. Theincidence of POD was calculated, and logistic multivariate analysis was conducted to determine the independentrisk factors. The calibration curve and the Hosmer-Lemeshow test as well as the net benefit threshold probabilityinterval by the decision curve were utilized to statistically validate the accuracy of the nomogram predictionmodel, developed according to each factor’s influence intensity.RESULTSThis study included 532 elderly patients with HF, with an overall POD incidence of 14.85%. The comparison ofbaseline data with perioperative indicators revealed statistical differences in age (P < 0.001), number of comorbidities(P = 0.042), American Society of Anesthesiologists grading (P = 0.004), preoperative red blood cell(RBC) count (P < 0.001), preoperative albumin (P < 0.001), preoperative hemoglobin (P < 0.001), preoperativeplatelet count (P < 0.001), intraoperative blood loss (P < 0.001), RBC transfusion of ≥ 2 units (P = 0.001), andpostoperative intensive care unit care (P < 0.001) between the delirium and non-delirium groups. The participantswere randomized to a training group (n = 372) and a validation group (n = 160). A score-risk nomogram predictionmodel was developed after screening key POD features using Lasso regression, support vector machine, and therandom forest method. The nomogram showed excellent discriminatory capacity with area under the curve of0.833 [95% confidence interval (CI) interval: 0.774-0.888] in the training group and 0.850 (95%CI: 0.718-0.982) in thevalidation group. Calibration curves demonstrated good agreement between predicted and actual probabilities,and decision curve analysis confirmed clinical net benefits within risk thresholds of 0%-30% and 0%-36%, respectively.The model has strong accuracy and clinical utility for predicting the risk of POD.CONCLUSIONThis study reveals cognitive impairment history, American Society of Anesthesiologists grade of > 2, RBCtransfusion of ≥ 2 units, postoperative intensive care unit care, and preoperative hemoglobin level as independentrisk factors for POD in elderly patients with HF. The developed nomogram model demonstrates excellent accuracyand stability in predicting the risk of POD, which is recommended to be applied in clinical practice to optimizepostoperative management and reduce delirium incidence.
基金
Supported by Wang Zhengguo Foundation for Traumatic Medicine“Sequential Medical Research Special Foundation”,No 2024-XGM05.
