Suppr超能文献

晚期不可治愈癌症患者生存预后模型:PiPS2 观察性研究。

Prognostic models of survival in patients with advanced incurable cancer: the PiPS2 observational study.

机构信息

Marie Curie Palliative Care Research Department, Division of Psychiatry, University College London, London, UK.

School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.

出版信息

Health Technol Assess. 2021 May;25(28):1-118. doi: 10.3310/hta25280.

Abstract

BACKGROUND

The Prognosis in Palliative care Study (PiPS) prognostic survival models predict survival in patients with incurable cancer. PiPS-A (Prognosis in Palliative care Study - All), which involved clinical observations only, and PiPS-B (Prognosis in Palliative care Study - Blood), which additionally required blood test results, consist of 14- and 56-day models that combine to create survival risk categories: 'days', 'weeks' and 'months+'.

OBJECTIVES

The primary objectives were to compare PIPS-B risk categories against agreed multiprofessional estimates of survival and to validate PiPS-A and PiPS-B. The secondary objectives were to validate other prognostic models, to assess the acceptability of the models to patients, carers and health-care professionals and to identify barriers to and facilitators of clinical use.

DESIGN

This was a national, multicentre, prospective, observational, cohort study with a nested qualitative substudy using interviews with patients, carers and health-care professionals.

SETTING

Community, hospital and hospice palliative care services across England and Wales.

PARTICIPANTS

For the validation study, the participants were adults with incurable cancer, with or without capacity to consent, who had been recently referred to palliative care services and had sufficient English language. For the qualitative substudy, a subset of participants in the validation study took part, along with informal carers, patients who declined to participate in the main study and health-care professionals.

MAIN OUTCOME MEASURES

For the validation study, the primary outcomes were survival, clinical prediction of survival and PiPS-B risk category predictions. The secondary outcomes were predictions of PiPS-A and other prognostic models. For the qualitative substudy, the main outcomes were participants' views about prognostication and the use of prognostic models.

RESULTS

For the validation study, 1833 participants were recruited. PiPS-B risk categories were as accurate as agreed multiprofessional estimates of survival (61%;  = 0.851). Discrimination of the PiPS-B 14-day model (-statistic 0.837, 95% confidence interval 0.810 to 0.863) and the PiPS-B 56-day model (-statistic 0.810, 95% confidence interval 0.788 to 0.832) was excellent. The PiPS-B 14-day model showed some overfitting (calibration in the large -0.202, 95% confidence interval -0.364 to -0.039; calibration slope 0.840, 95% confidence interval 0.730 to 0.950). The PiPS-B 56-day model was well-calibrated (calibration in the large 0.152, 95% confidence interval 0.030 to 0.273; calibration slope 0.914, 95% confidence interval 0.808 to 1.02). PiPS-A risk categories were less accurate than agreed multiprofessional estimates of survival ( < 0.001). The PiPS-A 14-day model (-statistic 0.825, 95% confidence interval 0.803 to 0.848; calibration in the large -0.037, 95% confidence interval -0.168 to 0.095; calibration slope 0.981, 95% confidence interval 0.872 to 1.09) and the PiPS-A 56-day model (-statistic 0.776, 95% confidence interval 0.755 to 0.797; calibration in the large 0.109, 95% confidence interval 0.002 to 0.215; calibration slope 0.946, 95% confidence interval 0.842 to 1.05) had excellent or reasonably good discrimination and calibration. Other prognostic models were also validated. Where comparisons were possible, the other prognostic models performed less well than PiPS-B. For the qualitative substudy, 32 health-care professionals, 29 patients and 20 carers were interviewed. The majority of patients and carers expressed a desire for prognostic information and said that PiPS could be helpful. Health-care professionals said that PiPS was user friendly and may be helpful for decision-making and care-planning. The need for a blood test for PiPS-B was considered a limitation.

LIMITATIONS

The results may not be generalisable to other populations.

CONCLUSIONS

PiPS-B risk categories are as accurate as agreed multiprofessional estimates of survival. PiPS-A categories are less accurate. Patients, carers and health-care professionals regard PiPS as potentially helpful in clinical practice.

FUTURE WORK

A study to evaluate the impact of introducing PiPS into routine clinical practice is needed.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN13688211.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in ; Vol. 25, No. 28. See the NIHR Journals Library website for further project information.

摘要

背景

姑息治疗预后研究(PiPS)预后生存模型可预测无法治愈的癌症患者的生存情况。PiPS-A(姑息治疗预后研究-全部)仅进行临床观察,PiPS-B(姑息治疗预后研究-血液)还需要血液检测结果,由 14 天和 56 天模型组成,这两种模型结合起来可以创建生存风险类别:“天”、“周”和“月+”。

目的

主要目的是比较 PiPS-B 风险类别与多专业一致的生存估计,并验证 PiPS-A 和 PiPS-B。次要目的是验证其他预后模型,评估模型对患者、护理人员和医疗保健专业人员的可接受性,以及确定临床应用的障碍和促进因素。

设计

这是一项全国性、多中心、前瞻性、观察性、队列研究,嵌套了使用患者、护理人员和医疗保健专业人员访谈的定性子研究。

地点

英格兰和威尔士的社区、医院和临终关怀姑息治疗服务。

参与者

对于验证研究,参与者是最近被转诊至姑息治疗服务的患有无法治愈癌症的成年人,无论其是否有能力同意,且具有足够的英语水平。对于定性子研究,验证研究的一部分参与者以及非正式护理人员、拒绝参与主要研究的患者和医疗保健专业人员参与了研究。

主要结局指标

对于验证研究,主要结局指标是生存、临床预测生存和 PiPS-B 风险类别预测。次要结局指标是 PiPS-A 和其他预后模型的预测。对于定性子研究,主要结局指标是参与者对预后的看法以及对预后模型的使用。

结果

对于验证研究,共招募了 1833 名参与者。PiPS-B 风险类别与多专业一致的生存估计(61%;=0.851)一样准确。PiPS-B 14 天模型(-统计量 0.837,95%置信区间 0.810-0.863)和 PiPS-B 56 天模型(-统计量 0.810,95%置信区间 0.788-0.832)的区分度很好。PiPS-B 14 天模型显示出一定的过度拟合(校准大值-0.202,95%置信区间-0.364 至-0.039;校准斜率 0.840,95%置信区间 0.730 至 0.950)。PiPS-B 56 天模型校准良好(校准大值 0.152,95%置信区间 0.030 至 0.273;校准斜率 0.914,95%置信区间 0.808 至 1.02)。PiPS-A 风险类别不如多专业一致的生存估计准确(<0.001)。PiPS-A 14 天模型(-统计量 0.825,95%置信区间 0.803-0.848;校准大值-0.037,95%置信区间-0.168 至 0.095;校准斜率 0.981,95%置信区间 0.872 至 1.09)和 PiPS-A 56 天模型(-统计量 0.776,95%置信区间 0.755-0.797;校准大值 0.109,95%置信区间 0.002 至 0.215;校准斜率 0.946,95%置信区间 0.842 至 1.05)的区分度和校准都很好或相当好。其他预后模型也得到了验证。在可比较的情况下,其他预后模型的表现不如 PiPS-B。对于定性子研究,采访了 32 名医疗保健专业人员、29 名患者和 20 名护理人员。大多数患者和护理人员都表示希望获得预后信息,并表示 PiPS 可能会有所帮助。医疗保健专业人员表示,PiPS 使用方便,可能有助于决策和护理计划。PiPS-B 需要进行血液检查被认为是一个限制。

局限性

结果可能不适用于其他人群。

结论

PiPS-B 风险类别与多专业一致的生存估计一样准确。PiPS-A 类别不太准确。患者、护理人员和医疗保健专业人员认为 PiPS 在临床实践中可能有帮助。

未来工作

需要进行一项研究来评估将 PiPS 引入常规临床实践的影响。

试验注册

当前对照试验 ISRCTN85465203。

资金

本项目由英国国家卫生研究院(NIHR)卫生技术评估计划资助,将在;第 25 卷,第 28 期全文发表。欲了解更多项目信息,请访问 NIHR 期刊库网站。

相似文献

7
Eye donation from palliative and hospice care contexts: the EDiPPPP mixed-methods study.
Health Soc Care Deliv Res. 2023 Nov;11(20):1-159. doi: 10.3310/KJWA6741.
9
10

引用本文的文献

2
Urinary metabolite model to predict the dying process in lung cancer patients.
Commun Med (Lond). 2025 Feb 27;5(1):49. doi: 10.1038/s43856-025-00764-3.
5
Prediction of Survival in Patients with Advanced Cancer: A Narrative Review and Future Research Priorities.
J Hosp Palliat Care. 2023 Mar 1;26(1):1-6. doi: 10.14475/jhpc.2023.26.1.1.
7
Prognostic evaluation in patients with advanced cancer in the last months of life: ESMO Clinical Practice Guideline.
ESMO Open. 2023 Apr;8(2):101195. doi: 10.1016/j.esmoop.2023.101195. Epub 2023 Apr 11.
8
GC-MS Techniques Investigating Potential Biomarkers of Dying in the Last Weeks with Lung Cancer.
Int J Mol Sci. 2023 Jan 13;24(2):1591. doi: 10.3390/ijms24021591.
9
Prognostication in palliative radiotherapy-ProPaRT: Accuracy of prognostic scores.
Front Oncol. 2022 Aug 16;12:918414. doi: 10.3389/fonc.2022.918414. eCollection 2022.
10
Development and Validation of the PaP Score Nomogram for Terminally Ill Cancer Patients.
Cancers (Basel). 2022 May 19;14(10):2510. doi: 10.3390/cancers14102510.

本文引用的文献

1
Prognostic tools or clinical predictions: Which are better in palliative care?
PLoS One. 2021 Apr 28;16(4):e0249763. doi: 10.1371/journal.pone.0249763. eCollection 2021.
4
Prognostic evaluation in palliative care: final results from a prospective cohort study.
Support Care Cancer. 2019 Jun;27(6):2095-2102. doi: 10.1007/s00520-018-4463-z. Epub 2018 Sep 18.
6
Access to palliative care by disease trajectory: a population-based cohort of Ontario decedents.
BMJ Open. 2018 Apr 5;8(4):e021147. doi: 10.1136/bmjopen-2017-021147.
7
A Comparison of the Accuracy of Clinician Prediction of Survival Versus the Palliative Prognostic Index.
J Pain Symptom Manage. 2018 Mar;55(3):792-797. doi: 10.1016/j.jpainsymman.2017.11.028. Epub 2017 Dec 6.
9
Predicting prognosis in patients with advanced cancer: A prospective study.
Palliat Med. 2018 Feb;32(2):413-416. doi: 10.1177/0269216317705788. Epub 2017 May 10.
10
Clinician prediction of survival versus the Palliative Prognostic Score: Which approach is more accurate?
Eur J Cancer. 2016 Sep;64:89-95. doi: 10.1016/j.ejca.2016.05.009. Epub 2016 Jun 30.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验