Miyazaki Taiga, Fukushima Kiyoyasu, Hashiguchi Kohji, Ide Shotaro, Kobayashi Tsutomu, Sawai Toyomitsu, Yatera Kazuhiro, Kohno Yoshihisa, Fukuda Yuichi, Futsuki Yoji, Matsubara Yuichi, Koga Hironobu, Mihara Tomo, Sasaki Eisuke, Ashizawa Nobuyuki, Hirayama Tatsuro, Takazono Takahiro, Yamamoto Kazuko, Imamura Yoshifumi, Kaku Norihito, Kosai Kosuke, Morinaga Yoshitomo, Yanagihara Katsunori, Mukae Hiroshi
Division of Respirology, Rheumatology, Infectious Diseases, and Neurology, Department of Internal Medicine, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan.
Department of Respiratory Medicine, Nagasaki University Hospital, Nagasaki, Japan.
Pneumonia (Nathan). 2023 Oct 25;15(1):16. doi: 10.1186/s41479-023-00118-4.
Current microbiological tests fail to identify the causative microorganism in more than half of all pneumonia cases. We explored biomarkers that could be used for differentiating between bacterial and viral pneumonia in patients with community-acquired pneumonia (CAP).
In this prospective cohort study conducted in Japan, data obtained from adult patients with bacterial pneumonia, including bacterial and viral coinfections (bacterial pneumonia [BP] group), and purely viral pneumonia (VP group) at diagnosis were analyzed using multivariate logistic regression analysis to identify predictors of bacterial pneumonia. Furthermore, a decision tree was developed using the predictors.
A total of 210 patients were analyzed. The BP and VP groups comprised 108 and 18 patients, respectively. The other 84 patients had no identified causative microorganism. The two groups shared similar characteristics, including disease severity; however, a significant difference (p < 0.05) was observed between the two groups regarding sputum type; sputum volume score; neutrophil counts; and serum levels of interleukin (IL)-8, IL-10, and α1-antitrypsin (AAT). Sputum volume score (p < 0.001), IL-10 (p < 0.001), and AAT (p = 0.008) were ultimately identified as predictors of BP. The area under the curve for these three variables on the receiver operating characteristic (ROC) curve was 0.927 (95% confidence interval [CI]: 0.881-0.974). The ROC curve for sputum volume score and an AAT/IL-10 ratio showed a diagnostic cutoff of 1 + and 65, respectively. Logistic regression analysis using dichotomized variables at the cutoff values showed that the odds ratios for the diagnosis of BP were 10.4 (95% CI: 2.2-50.2) for sputum volume score (absence vs. presence) and 19.8 (95% CI: 4.7-83.2) for AAT/IL-10 ratio (< 65 vs. ≥ 65).
Considering that obtaining a definitive etiologic diagnosis with the current testing methods is difficult and time consuming, a decision tree with two predictors, namely sputum volume and the AAT/IL-10 ratio, can be useful in predicting BP among patients diagnosed with CAP and facilitating the appropriate use of antibiotics.
UMIN000034673 registered on November 29, 2018.
目前的微生物学检测无法在超过半数的肺炎病例中识别出致病微生物。我们探索了可用于区分社区获得性肺炎(CAP)患者细菌性肺炎和病毒性肺炎的生物标志物。
在日本进行的这项前瞻性队列研究中,对诊断时患有细菌性肺炎(包括细菌和病毒混合感染)(细菌性肺炎[BP]组)和单纯病毒性肺炎(VP组)的成年患者的数据进行多因素逻辑回归分析,以确定细菌性肺炎的预测因素。此外,利用这些预测因素构建了决策树。
共分析了210例患者。BP组和VP组分别有108例和18例患者。其他84例患者未识别出致病微生物。两组具有相似的特征,包括疾病严重程度;然而,两组在痰液类型、痰量评分、中性粒细胞计数以及白细胞介素(IL)-8、IL-10和α1-抗胰蛋白酶(AAT)的血清水平方面存在显著差异(p<0.05)。痰量评分(p<0.001)、IL-10(p<0.001)和AAT(p=0.008)最终被确定为BP的预测因素。这三个变量在受试者工作特征(ROC)曲线上的曲线下面积为0.927(95%置信区间[CI]:0.881-0.974)。痰量评分和AAT/IL-10比值的ROC曲线显示诊断临界值分别为1+和65。使用临界值处的二分变量进行逻辑回归分析表明,痰量评分(无vs有)诊断BP的比值比为10.4(95%CI:2.2-50.2),AAT/IL-10比值(<65 vs≥65)为19.8(95%CI:4.7-83.2)。
鉴于目前的检测方法难以获得明确的病因诊断且耗时较长,包含痰量和AAT/IL-10比值这两个预测因素的决策树可用于预测CAP患者中的BP,并有助于合理使用抗生素。
UMIN000034673,于2018年11月29日注册。