Suppr超能文献

个体炎性标志物异常或炎性标志物评分能否用于识别因癌症接受调查的有意外体重减轻的初级保健患者?

Individual inflammatory marker abnormalities or inflammatory marker scores to identify primary care patients with unexpected weight loss for cancer investigation?

机构信息

Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.

Medical School, University of Exeter, Exeter, UK.

出版信息

Br J Cancer. 2021 Apr;124(9):1540-1542. doi: 10.1038/s41416-021-01282-4. Epub 2021 Feb 9.

Abstract

BACKGROUND

Combinations of inflammatory markers are used as prognostic scores in cancer patients with cachexia. We investigated whether they could also be used to prioritise patients attending primary care with unexpected weight loss for cancer investigation.

METHODS

We used English primary care electronic health records data linked to cancer registry data from 12,024 patients with coded unexpected weight loss. For each individual inflammatory marker and score we estimated the sensitivity, specificity, likelihood ratios, positive predictive value (PPV) and the area under the curve along with 95% confidence intervals for a cancer diagnosis within six months.

RESULTS

The risk of cancer associated with two abnormal inflammatory markers combined in a score was higher than the risk associated with individual inflammatory marker abnormalities. However, the risk of cancer in weight loss associated with individual abnormalities, notably a raised C-reactive protein, was sufficient to trigger further investigation for cancer under current NICE guidelines.

CONCLUSIONS

If scores including pairs of inflammatory marker abnormalities were to be used, in preference to individual abnormalities, fewer people would be investigated to diagnose one cancer with fewer false positives, but fewer people with cancer would be diagnosed overall.

摘要

背景

在患有恶病质的癌症患者中,炎症标志物的组合被用作预后评分。我们研究了它们是否也可用于优先安排因意外体重减轻而到初级保健就诊的患者进行癌症检查。

方法

我们使用了来自 12024 名编码意外体重减轻的患者的英国初级保健电子健康记录数据,并与癌症登记数据相关联。对于每个单独的炎症标志物和评分,我们估计了在六个月内诊断出癌症的敏感性、特异性、似然比、阳性预测值(PPV)和曲线下面积,以及 95%置信区间。

结果

与单个炎症标志物异常相比,两个异常炎症标志物联合评分与癌症相关的风险更高。然而,与个体异常相关的体重减轻的癌症风险,尤其是 C 反应蛋白升高,足以根据当前 NICE 指南触发对癌症的进一步调查。

结论

如果要使用包括两个炎症标志物异常的评分,而不是单个异常,那么诊断一种癌症的调查人数会减少,假阳性率会降低,但总体上诊断出的癌症患者会减少。

相似文献

5
Measured weight loss as a precursor to cancer diagnosis: retrospective cohort analysis of 43 302 primary care patients.
J Cachexia Sarcopenia Muscle. 2022 Oct;13(5):2492-2503. doi: 10.1002/jcsm.13051. Epub 2022 Jul 28.
10
The association between unexpected weight loss and cancer diagnosis in primary care: a matched cohort analysis of 65,000 presentations.
Br J Cancer. 2020 Jun;122(12):1848-1856. doi: 10.1038/s41416-020-0829-3. Epub 2020 Apr 15.

引用本文的文献

5
A taxonomy of early diagnosis research to guide study design and funding prioritisation.
Br J Cancer. 2023 Nov;129(10):1527-1534. doi: 10.1038/s41416-023-02450-4. Epub 2023 Oct 4.
6
Association of Weight Loss in Ambulatory Care Settings With First Diagnosis of Lung Cancer in the US.
JAMA Netw Open. 2023 May 1;6(5):e2312042. doi: 10.1001/jamanetworkopen.2023.12042.
7
Measured weight loss as a precursor to cancer diagnosis: retrospective cohort analysis of 43 302 primary care patients.
J Cachexia Sarcopenia Muscle. 2022 Oct;13(5):2492-2503. doi: 10.1002/jcsm.13051. Epub 2022 Jul 28.

本文引用的文献

2
The association between unexpected weight loss and cancer diagnosis in primary care: a matched cohort analysis of 65,000 presentations.
Br J Cancer. 2020 Jun;122(12):1848-1856. doi: 10.1038/s41416-020-0829-3. Epub 2020 Apr 15.
3
Blood markers for cancer.
BMJ. 2019 Oct 14;367:l5774. doi: 10.1136/bmj.l5774.
5
Early detection of multiple myeloma in primary care using blood tests: a case-control study in primary care.
Br J Gen Pract. 2018 Sep;68(674):e586-e593. doi: 10.3399/bjgp18X698357. Epub 2018 Aug 13.
7
Cancer-associated cachexia.
Nat Rev Dis Primers. 2018 Jan 18;4:17105. doi: 10.1038/nrdp.2017.105.
9
Data Resource Profile: Clinical Practice Research Datalink (CPRD).
Int J Epidemiol. 2015 Jun;44(3):827-36. doi: 10.1093/ije/dyv098. Epub 2015 Jun 6.

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