Department of Respiratory Diseases, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China.
Co-Construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of P.R. China, Henan University of Chinese Medicine, Zhengzhou, China.
Clin Respir J. 2023 Aug;17(8):707-718. doi: 10.1111/crj.13606. Epub 2023 Mar 21.
The prognosis for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is not optimistic, and severe AECOPD leads to an increased risk of mortality. Prediction models help distinguish between high- and low-risk groups. At present, many prediction models have been established and validated, which need to be systematically reviewed to screen out more suitable models that can be used in the clinic and provide evidence for future research.
We searched PubMed, EMBASE, Cochrane Library and Web of Science databases for studies on risk models for AECOPD mortality from their inception to 10 April 2022. The risk of bias was assessed using the prediction model risk of bias assessment tool (PROBAST). Stata software (version 16) was used to synthesize the C-statistics for each model.
A total of 37 studies were included. The development of risk prediction models for mortality in patients with AECOPD was described in 26 articles, in which the most common predictors were age (n = 17), dyspnea grade (n = 11), altered mental status (n = 8), pneumonia (n = 6) and blood urea nitrogen (BUN, n = 6). The remaining 11 articles only externally validated existing models. All 37 studies were evaluated at a high risk of bias using PROBAST. We performed a meta-analysis of five models included in 15 studies. DECAF (dyspnoea, eosinopenia, consolidation, acidemia and atrial fibrillation) performed well in predicting in-hospital death [C-statistic = 0.91, 95% confidence interval (CI): 0.83, 0.98] and 90-day death [C-statistic = 0.76, 95% CI: 0.69, 0.82] and CURB-65 (confusion, urea, respiratory rate, blood pressure and age) performed well in predicting 30-day death [C-statistic = 0.74, 95% CI: 0.70, 0.77].
This study provides information on the characteristics, performance and risk of bias of a risk model for AECOPD mortality. This pooled analysis of the present study suggests that the DECAF performs well in predicting in-hospital and 90-day deaths. Yet, external validation in different populations is still needed to prove this performance.
慢性阻塞性肺疾病急性加重(AECOPD)的预后不容乐观,严重的 AECOPD 导致死亡率增加。预测模型有助于区分高风险和低风险人群。目前,已经建立和验证了许多预测模型,需要进行系统评价,以筛选出更适合临床应用的模型,并为未来的研究提供证据。
我们从建库至 2022 年 4 月 10 日,在 PubMed、EMBASE、Cochrane 图书馆和 Web of Science 数据库中检索关于 AECOPD 死亡率风险模型的研究。使用预测模型风险偏倚评估工具(PROBAST)评估风险偏倚的风险。使用 Stata 软件(版本 16)对每个模型的 C 统计量进行综合分析。
共纳入 37 项研究。26 项研究描述了 AECOPD 患者死亡率风险预测模型的开发,其中最常见的预测因素是年龄(n=17)、呼吸困难等级(n=11)、意识状态改变(n=8)、肺炎(n=6)和血尿素氮(BUN,n=6)。其余 11 项研究仅对现有模型进行了外部验证。所有 37 项研究均使用 PROBAST 评估为高偏倚风险。我们对纳入的 15 项研究中的 5 个模型进行了荟萃分析。DECAF(呼吸困难、嗜酸性粒细胞减少、实变、酸中毒和房颤)在预测住院内死亡[C 统计量=0.91,95%置信区间(CI):0.83,0.98]和 90 天死亡[C 统计量=0.76,95%CI:0.69,0.82]方面表现良好,而 CURB-65(意识障碍、尿素、呼吸频率、血压和年龄)在预测 30 天死亡[C 统计量=0.74,95%CI:0.70,0.77]方面表现良好。
本研究提供了 AECOPD 死亡率风险模型的特征、性能和偏倚风险信息。本研究的汇总分析表明,DECAF 可较好地预测住院内和 90 天死亡。然而,仍需要在不同人群中进行外部验证以证实其性能。