Based on the frailty index and intraoperative body temperature fluctuations to predict the risk of failure to get out of bed within 24 hours after joint replacement surgery
基于衰弱指数与术中体温波动预测关节置换术后24小时内下床失败风险

摘要

目的 探讨衰弱指数和术中体温波动对关节置换术后24小时内下床失败的预测作用，为临床制定个体化康复方案提供依据。方法 回顾性选取我院100例髋膝关节置换患者，按术后24小时下床情况分组。分析临床资料、术前衰弱指数（FI）评分及术中体温指标，单因素比较组间差异，Logistic回归分析下床失败危险因素，ROC曲线评估FI联合体温波动的预测效能。结果 单因素分析显示，年龄、术前FI评分、术中最低体温、体温波动、基础疾病数、术后视觉模拟评分法（VAS）评分在两组间差异有统计学意义（P<0.05）；性别、手术类型、手术时长、出血量差异无统计学意义（P>0.05）。多因素分析表明，术前FI评分升高、术中体温波动增大、术后VAS升高是术后24小时下床失败的独立危险因素。ROC分析显示，FI联合体温波动的预测AUC为0.867，灵敏度和特异度分别为82.1%和80.6%，优于单一指标预测。结论 术前衰弱指数高和术中体温波动大是关节置换术后24小时下床失败的关键风险因素，联合评估可提高预测准确性，有助于临床干预高风险患者，改善康复效果。

Abstract

Objective To explore the predictive effect of frailty index and intraoperative body temperature fluctuations on failure to get out of bed within 24 hours after joint replacement surgery, and to provide a basis for clinical formulation of individualized rehabilitation plans.
Methods A retrospective study was conducted on 100 patients undergoing hip and knee joint replacement in our hospital. They were grouped according to their bed-falling status within 24 hours after surgery. Clinical data, preoperative Frailty Index (FI) score, and intraoperative body temperature indicators were analyzed. Univariate comparisons were made to identify differences between groups, and Logistic regression analysis was used to identify the risk factors for failure to get out of bed. ROC curve was used to evaluate the predictive efficacy of the combination of FI and body temperature fluctuations.
Results Univariate analysis showed that age, preoperative FI score, lowest intraoperative body temperature, body temperature fluctuations, number of underlying diseases, and postoperative Visual Analogue Scale (VAS) score were statistically significantly different between the two groups (P<0.05); gender, surgical type, operation duration, and blood loss showed no statistically significant differences (P>0.05). Multivariate analysis indicated that an increased preoperative FI score, increased intraoperative body temperature fluctuations, and increased postoperative VAS score were independent risk factors for failure to get out of bed within 24 hours after surgery. ROC analysis showed that the predictive AUC of the combination of FI and body temperature fluctuations was 0.867, with a sensitivity of 82.1% and a specificity of 80.6%, which was superior to that of a single indicator.
Conclusion   High preoperative frailty index and large intraoperative body temperature fluctuations are key risk factors for failure to get out of bed within 24 hours after joint replacement surgery. Combined assessment can improve the predictive accuracy and help clinical intervention for high-risk patients, thereby improving the rehabilitation effect.