分析虚弱是否能提高消化外科后ICU患者的死亡率预测。
预期,观察,2018年6月1日至2019年6月1日期间入住ICU的患者队列的6个月随访研究。
三级医院外科ICU。
一系列70岁以上的连续患者,在对消化系统进行手术干预后立即进入ICU。92例患者被纳入,2例因6个月随访中断而被排除。
入住ICU后,严重程度和预后通过APACHEII评估,通过临床脆弱量表和改良的脆弱指数。
ICU,住院和6个月死亡率。
最能预测ICU死亡率的模型是APACHEII,ROC曲线下面积(AUC)为0.89,校准良好。结合APACHEII和临床虚弱量表的模型是最能预测住院死亡率的模型(AUC:0.82),显着提高了分离的APACHEII的预测(AUC:0.78;综合歧视指数:0.04)。虚弱是6个月死亡率的预测指标,作为结合临床衰弱量表和衰弱指数的模型,显示出最大的辨别力(AUC:0.84)。
Failty可以通过改善APACHEII对医院死亡率的预测来补充APACHEII。此外,它可以很好地预测手术后6个月的死亡率。对于ICU的死亡率,脆弱失去了预测能力,而孤立的APACHEII显示出优异的预测能力。
To analyze whether frailty can improve the prediction of mortality in patients admitted to the ICU after digestive surgery.
Prospective, observational, 6-month follow-up study of a cohort of patients admitted to the ICU between June 1, 2018, and June 1, 2019.
Surgical ICU of a third level hospital.
Series of successive patients older than 70 years who were admitted to the ICU immediately after a surgical intervention on the digestive system. 92 patients were included and 2 were excluded due to loss of follow-up at 6 months.
Upon admission to the ICU, severity and prognosis were assessed by APACHE II, and fragility by the Clinical Frailty Scale and the modified Frailty Index.
ICU, in-hospital and 6-month mortality.
The model that best predicts mortality in the ICU is the APACHE II, with an area under the ROC curve (AUC) of 0.89 and a good calibration. The model that combines APACHE II and Clinical Frailty Scale is the one that best predicts in-hospital mortality (AUC: 0.82), significantly improving the prediction of isolated APACHE II (AUC: 0.78; Integrated Discrimination Index: 0.04). Frailty is a predictor of mortality at 6 months, being the model that combines Clinical Frailty Scale and Frailty Index the one that has shown the greatest discrimination (AUC: 0.84).
Frailty can complement APACHE II by improving its prediction of hospital mortality. Furthermore, it offers a good prediction of mortality 6 months after surgery. For mortality in ICU, frailty loses its predictive power, whereas isolated APACHE II shows excellent predictive capacity.