Villa G, De Gaudio A R, Falsini S, Lanini I, Curtis J R
Section of Anesthesiology and Intensive Care, Department of Health Sciences, University of Florence, Florence, Italy -
Minerva Anestesiol. 2015 Dec;81(12):1318-28. Epub 2015 Jan 23.
High quality palliative care should be provided for the dying patients in the intensive care unit (ICU). The aim of this pilot study is to develop a scoring system, the "END-of-Life ScorING-System" (ENDING-S), that may help to identify ICU patients at very high risk of dying after initial response to the intensive treatments and which could be used to facilitate palliative care.
The characteristics of longer-term ICU patients (>4 days) who are at very high risk of dying were identified through an analysis of the literature and developed in a retrospective cohort of patients. ENDING-S Score was developed through a multivariate analysis. Model accuracy was tested through ROC and Hosmer-Lemeshow analysis for model discrimination and calibration respectively. Cross validation was used to provide internal model validation.
Potential predictors of death were identified and applied to 80 ICU patients. Significant variables in the multivariate analysis were the ratio of the ICU days in which the patient needs mechanical ventilation or vasoactive drugs divided by the total ICU days, the total ICU length of stay, and current sepsis. Analysis of accuracy showed a ROC-AUC equals to 0.98 (95% CI, 0.97 to 1), and agreement between the predicted probability and the observed frequency of death in the ICU was observed (P>0.05 at Hosmer-Lemeshow test). The internal validation confirms these results.
In these preliminary results, ENDING-s shows acceptable calibration and discrimination properties. ENDING-S may raise awareness among ICU physicians about the importance of integrating palliative care into ICU daily practice.
应为重症监护病房(ICU)中的临终患者提供高质量的姑息治疗。这项试点研究的目的是开发一种评分系统,即“临终评分系统”(ENDING-S),该系统可能有助于识别在对强化治疗做出初始反应后死亡风险极高的ICU患者,并可用于促进姑息治疗。
通过文献分析确定了死亡风险极高的长期ICU患者(>4天)的特征,并在一组回顾性患者队列中进行了研究。ENDING-S评分通过多变量分析得出。分别通过ROC和Hosmer-Lemeshow分析对模型的准确性进行检验,以评估模型的区分度和校准度。采用交叉验证进行内部模型验证。
确定了死亡的潜在预测因素并应用于80例ICU患者。多变量分析中的显著变量包括患者需要机械通气或血管活性药物的ICU天数与总ICU天数的比值、总ICU住院时间以及当前的脓毒症情况。准确性分析显示ROC-AUC等于0.98(95%CI,0.97至1),并且观察到ICU中预测概率与观察到的死亡频率之间具有一致性(Hosmer-Lemeshow检验P>0.05)。内部验证证实了这些结果。
在这些初步结果中,ENDING-s显示出可接受的校准和区分特性。ENDING-S可能会提高ICU医生对将姑息治疗纳入ICU日常实践重要性的认识。