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澳大利亚和爱尔兰急诊老年患者短期死亡预测清单的前瞻性验证。

Prospective Validation of a Checklist to Predict Short-term Death in Older Patients After Emergency Department Admission in Australia and Ireland.

机构信息

Centre for Research in Evidence-Based Practice, Faculty of Health Sciences and Medicine, Bond University, Robina, QLD, Australia.

School of Public Health and Community Medicine, The University of New South Wales, Sydney, NSW, Australia.

出版信息

Acad Emerg Med. 2019 Jun;26(6):610-620. doi: 10.1111/acem.13664. Epub 2018 Dec 14.

DOI:10.1111/acem.13664
PMID:30428145
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6619350/
Abstract

BACKGROUND

Emergency departments (EDs) are pressured environment where patients with supportive and palliative care needs may not be identified. We aimed to test the predictive ability of the CriSTAL (Criteria for Screening and Triaging to Appropriate aLternative care) checklist to flag patients at risk of death within 3 months who may benefit from timely end-of-life discussions.

METHODS

Prospective cohorts of >65-year-old patients admitted for at least one night via EDs in five Australian hospitals and one Irish hospital. Purpose-trained nurses and medical students screened for frailty using two instruments concurrently and completed the other risk factors on the CriSTAL tool at admission. Postdischarge telephone follow-up was used to determine survival status. Logistic regression and bootstrapping techniques were used to test the predictive accuracy of CriSTAL for death within 90 days of admission as primary outcome. Predictability of in-hospital death was the secondary outcome.

RESULTS

A total of 1,182 patients, with median age 76 to 80 years (IRE-AUS), were included. The deceased had significantly higher mean CriSTAL with Australian mean of 8.1 (95% confidence interval [CI] = 7.7-8.6) versus 5.7 (95% CI = 5.1-6.2) and Irish mean of 7.7 (95% CI = 6.9-8.5) versus 5.7 (95% CI = 5.1-6.2). The model with Fried frailty score was optimal for the derivation (Australian) cohort but prediction with the Clinical Frailty Scale (CFS) was also good (areas under the receiver-operating characteristic [AUROC] = 0.825 and 0.81, respectively). Values for the validation (Irish) cohort were AUROC = 0.70 with Fried and 0.77 using CFS. A minimum of five of 29 variables were sufficient for accurate prediction, and a cut point of 7+ or 6+ depending on the cohort was strongly indicative of risk of death. The most significant independent predictor of short-term death in both cohorts was frailty, carrying a twofold risk of death. CriSTAL's accuracy for in-hospital death prediction was also good (AUROC = 0.795 and 0.81 in Australia and Ireland, respectively), with high specificity and negative predictive values.

CONCLUSIONS

The modified CriSTAL tool (with CFS instead of Fried's frailty instrument) had good discriminant power to improve certainty of short-term mortality prediction in both health systems. The predictive ability of models is anticipated to help clinicians gain confidence in initiating earlier end-of-life discussions. The practicalities of embedding screening for risk of death in routine practice warrant further investigation.

摘要

背景

急诊科(ED)是一个充满压力的环境,可能无法识别有支持和姑息治疗需求的患者。我们旨在测试 CriSTAL(用于筛选和分诊以提供替代护理的标准)检查表的预测能力,以标记出可能在 3 个月内死亡并可能受益于及时进行临终讨论的高危患者。

方法

在澳大利亚的五家医院和一家爱尔兰医院,对>65 岁的至少住院一晚的患者进行前瞻性队列研究。经过专门培训的护士和医学生同时使用两种工具筛查衰弱情况,并在入院时完成 CriSTAL 工具的其他风险因素。出院后通过电话随访确定生存状况。使用逻辑回归和自举技术测试 CriSTAL 在入院后 90 天内死亡的预测准确性(主要结果)。院内死亡的预测能力是次要结果。

结果

共纳入 1182 例患者,中位年龄为 76-80 岁(IRE-AUS)。死亡患者的 CriSTAL 平均值明显更高,澳大利亚平均值为 8.1(95%置信区间[CI]7.7-8.6),而爱尔兰平均值为 5.7(95% CI 5.1-6.2)。模型中包含 Fried 衰弱评分时,用于推导(澳大利亚)队列的效果最佳,但使用临床虚弱量表(CFS)进行预测的效果也很好(受试者工作特征曲线下面积[AUROC]分别为 0.825 和 0.81)。验证(爱尔兰)队列的 AUROC 值分别为 Fried 0.70 和 CFS 0.77。29 个变量中至少有 5 个变量即可进行准确预测,根据队列的不同,7+或 6+的切点值强烈提示死亡风险。两个队列中短期死亡的最显著独立预测因素均为衰弱,其死亡风险增加两倍。 CriSTAL 对院内死亡预测的准确性也很好(澳大利亚和爱尔兰的 AUROC 分别为 0.795 和 0.81),具有高特异性和阴性预测值。

结论

改良 CriSTAL 工具(使用 CFS 替代 Fried 衰弱工具)在这两个医疗体系中均具有良好的鉴别能力,可提高短期死亡率预测的确定性。模型的预测能力有望帮助临床医生增强对启动临终讨论的信心。在常规实践中嵌入死亡风险筛查的实用性需要进一步研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c8a/6619350/d0dcabf4f09e/ACEM-26-610-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c8a/6619350/479ea0d3cbc1/ACEM-26-610-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c8a/6619350/6f0b5fdd8b3a/ACEM-26-610-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c8a/6619350/d0dcabf4f09e/ACEM-26-610-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c8a/6619350/479ea0d3cbc1/ACEM-26-610-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c8a/6619350/6f0b5fdd8b3a/ACEM-26-610-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c8a/6619350/d0dcabf4f09e/ACEM-26-610-g003.jpg

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