利用 NECPAL CCOMS-ICO 工具识别的三级医院中晚期慢性病患者的院内死亡率和生存率。

Intrahospital Mortality and Survival of Patients with Advanced Chronic Illnesses in a Tertiary Hospital Identified with the NECPAL CCOMS-ICO Tool.

机构信息

1 Comprehensive Support Unit, Catalan Institute of Oncology , Badalona, Spain .

2 Department of Palliative Care, University of Vic-Central University of Catalonia , Barcelona, Spain .

出版信息

J Palliat Med. 2018 May;21(5):665-673. doi: 10.1089/jpm.2017.0339. Epub 2018 Mar 19.

DOI:10.1089/jpm.2017.0339

PMID:29649401

Abstract

BACKGROUND

Between 69% and 82% of patients with advanced chronic illness require palliative care (PC). The NECPAL CCOMS-ICO tool can identify these individuals. Tools to estimate survival are available, but have limited predictive ability, and therefore we sought to assess if NECPAL could improve survival prediction.

OBJECTIVE

To describe hospital mortality, survival rates, and related variables in a sample of inpatients identified with the NECPAL tool.

DESIGN

Cross-sectional study with longitudinal cohort follow-up. Sociodemographic and clinical data were analyzed. A predictive model (Cox regression analysis) was performed to assess survival.

SETTING/SUBJECTS: Patients admitted to a tertiary hospital. Included patients were considered to be especially affected by their chronic condition and NECPAL+ patients (surprise question [SQ]+ plus ≥1 of the tool's other three criteria). Patients were classified into three subgroups: non-NECPAL (either SQ- or not meeting any additional NECPAL criteria); NECPAL I-II (SQ+ with one to two additional criteria); and NECPAL III (SQ+ with all three additional criteria).

RESULTS

Of the 602 inpatients, 236 (39.2%) were included. Of these, 49 (20.3%) died during hospitalization: 14 (13.3%) were NECPAL I-II; 34 (35.1%) were NECPAL III; and none were non-NECPAL (p < 0.001). At two years, 146 deaths (61.9%) were observed: 9 (26.5%) non-NECPAL; 57 (54.3%) NECPAL I-II; and 80 (82.5%) NECPAL III (p < 0.001). Median survival was 9.1 months. Variables associated with higher mortality were NECPAL III classification (hazard ratio [HR]: 1.75 [1.19-2.57]); in need of PC (HR: 2 [1.27-3.13]); dysphagia (HR: 1.7 [1.12-2.58] 6); cancer (HR: 3.21 [2.19-4.71]); and age >85 years (HR: 2.52 [1.46-4.35]). At six months, the NECPAL had an area under the curve (AUC) of 0.7 (95% confidence interval [CI]: 0.632-0.765), and at 24 months, the NECPAL AUC was 0.717 (95% CI: 0.650-0.785).

CONCLUSIONS

The NECPAL CCOMS-ICO tool can improve the prediction of mortality. The presence of all three NECPAL criteria (NECPAL III) increases the tool's predictive ability.

摘要

背景

69%至 82%的晚期慢性病患者需要姑息治疗（PC）。NECPAL CCOMS-ICO 工具可以识别这些患者。目前已有用于评估生存的工具，但预测能力有限，因此我们试图评估 NECPAL 是否能改善生存预测。

目的

描述使用 NECPAL 工具识别的住院患者的住院死亡率、生存率及相关变量。

设计

具有纵向队列随访的横断面研究。分析了社会人口统计学和临床数据。使用预测模型（Cox 回归分析）评估生存情况。

地点/受试者：入住三级医院的患者。纳入的患者被认为受到其慢性疾病的严重影响，并且为 NECPAL+患者（惊讶问题[SQ]+和/或满足工具的其他三个标准中的一个）。患者被分为三组：非 NECPAL（SQ-或不符合任何其他 NECPAL 标准）；NECPAL I-II（SQ+且有一个或两个其他标准）；和 NECPAL III（SQ+且有所有三个其他标准）。

结果

在 602 名住院患者中，有 236 名（39.2%）患者被纳入。其中，49 名（20.3%）患者在住院期间死亡：14 名（13.3%）为 NECPAL I-II；34 名（35.1%）为 NECPAL III；无一例非 NECPAL 患者死亡（p<0.001）。两年时，共观察到 146 例死亡（61.9%）：9 例（26.5%）非 NECPAL；57 例（54.3%）NECPAL I-II；和 80 例（82.5%）NECPAL III（p<0.001）。中位生存期为 9.1 个月。与更高死亡率相关的变量包括 NECPAL III 分类（危险比[HR]：1.75[1.19-2.57]）；需要姑息治疗（HR：2[1.27-3.13]）；吞咽困难（HR：1.7[1.12-2.58]）；癌症（HR：3.21[2.19-4.71]）；和年龄>85 岁（HR：2.52[1.46-4.35]）。在六个月时，NECPAL 的曲线下面积（AUC）为 0.7（95%置信区间[CI]：0.632-0.765），在 24 个月时，NECPAL 的 AUC 为 0.717（95% CI：0.650-0.785）。