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资源有限环境下成人肺结核估计的预测因素:诊断预测的系统评价

Predictors contributing to the estimation of pulmonary tuberculosis among adults in a resource-limited setting: A systematic review of diagnostic predictions.

作者信息

Gebregergs Gebremedhin Berhe, Berhe Gebretsadik, Gebrehiwot Kibrom Gebreslasie, Mulugeta Afework

机构信息

School of Public Health, College of Health Sciences, Mekelle University, Mekelle, Ethiopia.

Department of Internal Medicine, College of Health Sciences, Mekelle University, Mekelle, Ethiopia.

出版信息

SAGE Open Med. 2024 May 16;12:20503121241243238. doi: 10.1177/20503121241243238. eCollection 2024.

DOI:10.1177/20503121241243238
PMID:38764538
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11100385/
Abstract

BACKGROUND

Although tuberculosis is highly prevalent in low- and middle-income countries, millions of cases remain undetected using current diagnostic methods. To address this problem, researchers have proposed prediction rules.

OBJECTIVE

We analyzed existing prediction rules for the diagnosis of pulmonary tuberculosis and identified factors with a moderate to high strength of association with the disease.

METHODS

We conducted a comprehensive search of relevant databases (MEDLINE/PubMed, Cochrane Library, Science Direct, Global Health for Reports, and Google Scholar) up to 14 November 2022. Studies that developed diagnostic algorithms for pulmonary tuberculosis in adults from low and middle-income countries were included. Two reviewers performed study screening, data extraction, and quality assessment. The study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2. We performed a narrative synthesis.

RESULTS

Of the 26 articles selected, only half included human immune deficiency virus-positive patients. In symptomatic human immune deficiency virus patients, radiographic findings and body mass index were strong predictors of pulmonary tuberculosis, with an odds ratio of >4. However, in human immune deficiency virus-negative individuals, the biomarkers showed a moderate association with the disease. In symptomatic human immune deficiency virus patients, a C-reactive protein level ⩾10 mg/L had a sensitivity and specificity of 93% and 40%, respectively, whereas a trial of antibiotics had a specificity of 86% and a sensitivity of 43%. In smear-negative patients, anti-tuberculosis treatment showed a sensitivity of 52% and a specificity of 63%.

CONCLUSIONS

The performance of predictors and diagnostic algorithms differs among patient subgroups, such as in human immune deficiency virus-positive patients, radiographic findings, and body mass index were strong predictors of pulmonary tuberculosis. However, in human immune deficiency virus-negative individuals, the biomarkers showed a moderate association with the disease. A few models have reached the World Health Organization's recommendation. Therefore, more work should be done to strengthen the predictive models for tuberculosis screening in the future, and they should be developed rigorously, considering the heterogeneity of the population in clinical work.

摘要

背景

尽管结核病在低收入和中等收入国家高度流行,但使用当前诊断方法仍有数百万病例未被发现。为解决这一问题,研究人员提出了预测规则。

目的

我们分析了现有的肺结核诊断预测规则,并确定了与该疾病有中度至高度关联强度的因素。

方法

我们对截至2022年11月14日的相关数据库(MEDLINE/PubMed、Cochrane图书馆、Science Direct、全球健康报告和谷歌学术)进行了全面检索。纳入了为低收入和中等收入国家成年人开发肺结核诊断算法的研究。两名评审员进行了研究筛选、数据提取和质量评估。使用诊断准确性研究质量评估-2对研究质量进行评估。我们进行了叙述性综合分析。

结果

在所选的26篇文章中,只有一半纳入了人类免疫缺陷病毒阳性患者。在有症状的人类免疫缺陷病毒患者中,影像学表现和体重指数是肺结核的强预测指标,比值比>4。然而,在人类免疫缺陷病毒阴性个体中,生物标志物与该疾病呈中度关联。在有症状的人类免疫缺陷病毒患者中,C反应蛋白水平⩾10mg/L的敏感性和特异性分别为93%和40%,而抗生素试验的特异性为86%,敏感性为43%。在涂片阴性患者中,抗结核治疗的敏感性为52%,特异性为63%。

结论

预测指标和诊断算法在不同患者亚组中的表现不同,例如在人类免疫缺陷病毒阳性患者中,影像学表现和体重指数是肺结核的强预测指标。然而,在人类免疫缺陷病毒阴性个体中,生物标志物与该疾病呈中度关联。少数模型达到了世界卫生组织的推荐标准。因此,未来应开展更多工作以加强结核病筛查的预测模型,并且应考虑临床工作中人群的异质性进行严格开发。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1134/11100385/6dfc0101fe00/10.1177_20503121241243238-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1134/11100385/8432fb19e936/10.1177_20503121241243238-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1134/11100385/6dfc0101fe00/10.1177_20503121241243238-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1134/11100385/8432fb19e936/10.1177_20503121241243238-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1134/11100385/6dfc0101fe00/10.1177_20503121241243238-fig2.jpg

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