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一种预测慢性阻塞性肺疾病严重程度模型的开发。

Development of a model for predicting the severity of chronic obstructive pulmonary disease.

作者信息

Gu Yu-Feng, Chen Long, Qiu Rong, Wang Shu-Hong, Chen Ping

机构信息

Department of Information, Suining Central Hospital, Suining, China.

Department of Research Management, Suining Central Hospital, Suining, China.

出版信息

Front Med (Lausanne). 2022 Dec 16;9:1073536. doi: 10.3389/fmed.2022.1073536. eCollection 2022.

Abstract

BACKGROUND

Several models have been developed to predict the severity and prognosis of chronic obstructive pulmonary disease (COPD). This study aimed to identify potential predictors and construct a prediction model for COPD severity using biochemical and immunological parameters.

METHODS

A total of 6,274 patients with COPD were recruited between July 2010 and July 2018. COPD severity was classified into mild, moderate, severe, and very severe based on the Global Initiative for Chronic Obstructive Lung Disease guidelines. A multivariate logistic regression model was constructed to identify predictors of COPD severity. The predictive ability of the model was assessed by measuring sensitivity, specificity, accuracy, and concordance.

RESULTS

Of 6,274 COPD patients, 2,644, 2,600, and 1,030 had mild/moderate, severe, and very severe disease, respectively. The factors that could distinguish between mild/moderate and severe cases were vascular disorders (OR: 1.44; < 0.001), high-density lipoprotein (HDL) (OR: 1.83; < 0.001), plasma fibrinogen (OR: 1.08; = 0.002), fructosamine (OR: 1.12; = 0.002), standard bicarbonate concentration (OR: 1.09; < 0.001), partial pressure of carbon dioxide (OR: 1.09; < 0.001), age (OR: 0.97; < 0.001), eosinophil count (OR: 0.66; = 0.042), lymphocyte ratio (OR: 0.97; < 0.001), and apolipoprotein A1 (OR: 0.56; = 0.003). The factors that could distinguish between mild/moderate and very severe cases were vascular disorders (OR: 1.59; < 0.001), HDL (OR: 2.54; < 0.001), plasma fibrinogen (OR: 1.10; = 0.012), fructosamine (OR: 1.18; = 0.001), partial pressure of oxygen (OR: 1.00; = 0.007), plasma carbon dioxide concentration (OR: 1.01; < 0.001), standard bicarbonate concentration (OR: 1.13; < 0.001), partial pressure of carbon dioxide (OR: 1.16; < 0.001), age (OR: 0.91; < 0.001), sex (OR: 0.71; = 0.010), allergic diseases (OR: 0.51; = 0.009), eosinophil count (OR: 0.42; = 0.014), lymphocyte ratio (OR: 0.93; < 0.001), and apolipoprotein A1 (OR: 0.45; = 0.005). The prediction model correctly predicted disease severity in 60.17% of patients, and kappa coefficient was 0.35 (95% CI: 0.33-0.37).

CONCLUSION

This study developed a prediction model for COPD severity based on biochemical and immunological parameters, which should be validated in additional cohorts.

摘要

背景

已经开发了几种模型来预测慢性阻塞性肺疾病(COPD)的严重程度和预后。本研究旨在使用生化和免疫参数识别潜在的预测指标并构建COPD严重程度的预测模型。

方法

2010年7月至2018年7月期间共招募了6274例COPD患者。根据慢性阻塞性肺疾病全球倡议指南,将COPD严重程度分为轻度、中度、重度和极重度。构建多因素逻辑回归模型以识别COPD严重程度的预测指标。通过测量敏感性、特异性、准确性和一致性来评估模型的预测能力。

结果

在6274例COPD患者中,分别有2644例、2600例和1030例患有轻度/中度、重度和极重度疾病。能够区分轻度/中度和重度病例的因素有血管疾病(比值比:1.44;P<0.001)、高密度脂蛋白(HDL)(比值比:1.83;P<0.001)、血浆纤维蛋白原(比值比:1.08;P=0.002)、果糖胺(比值比:1.12;P=0.002)、标准碳酸氢盐浓度(比值比:1.09;P<0.001)、二氧化碳分压(比值比:1.09;P<0.001)、年龄(比值比:0.97;P<0.001)、嗜酸性粒细胞计数(比值比:0.66;P=0.042)、淋巴细胞比例(比值比:0.97;P<0.001)和载脂蛋白A1(比值比:0.56;P=0.003)。能够区分轻度/中度和极重度病例的因素有血管疾病(比值比:1.59;P<0.001)、HDL(比值比:2.54;P<0.001)、血浆纤维蛋白原(比值比:1.10;P=0.012)、果糖胺(比值比:1.18;P=0.001)、氧分压(比值比:1.00;P=0.007)、血浆二氧化碳浓度(比值比:1.01;P<0.001)、标准碳酸氢盐浓度(比值比:1.13;P<0.001)、二氧化碳分压(比值比:1.16;P<0.001)、年龄(比值比:0.91;P<0.001)、性别(比值比:0.71;P=0.010)、过敏性疾病(比值比:0.51;P=0.009)、嗜酸性粒细胞计数(比值比:0.42;P=0.014)、淋巴细胞比例(比值比:0.93;P<0.001)和载脂蛋白A1(比值比:0.45;P=0.005)。该预测模型正确预测了60.17%患者的疾病严重程度,kappa系数为0.35(95%置信区间:0.33-0.37)。

结论

本研究基于生化和免疫参数开发了一种COPD严重程度的预测模型,该模型应在其他队列中进行验证。

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