Cabral João V B, da Silveira Maria M B M, Vasconcelos Wilma T F, Xavier Amanda T, de Oliveira Fábio H P C, de Menezes Thaysa M G A L, Barbosa Keylla T F, Figueiredo Thaisa R, da Silva Filho Jabiael C, Silva Tamara, Torres Leuridan C, Filho Dário C Sobral, de Oliveira Dinaldo C
Postgraduate Program in Therapeutic Innovation, Federal University of Pernambuco-UFPE, Professor Moraes Rego Avenue, SN, University City, Recife 50670-420, Brazil.
Postgraduate Program in Nursing, Federal University of Paraíba-UFPB, João Pessoa 58051-900, Brazil.
Int J Mol Sci. 2025 Jul 30;26(15):7379. doi: 10.3390/ijms26157379.
Sepsis is a serious public health problem. sTREM-1 is a marker of inflammatory and infectious processes that has the potential to become a useful tool for predicting the evolution of sepsis. A prediction model for sepsis was constructed by combining sTREM-1, CRP, and a leukogram via a Bayesian network. A translational study carried out with 32 children with congenital heart disease who had undergone surgical correction at a public referral hospital in Northeast Brazil. In the postoperative period, the mean value of sTREM-1 was greater among patients diagnosed with sepsis than among those not diagnosed with sepsis (394.58 pg/mL versus 239.93 pg/mL, < 0.001). Analysis of the ROC curve for sTREM-1 and sepsis revealed that the area under the curve was 0.761, with a 95% CI (0.587-0.935) and = 0.013. With the Bayesian model, we found that a 100% probability of sepsis was related to postoperative blood concentrations of CRP above 71 mg/dL, a leukogram above 14,000 cells/μL, and sTREM-1 concentrations above the cutoff point (283.53 pg/mL). The proposed model using the Bayesian network approach with the combination of CRP, leukocyte count, and postoperative sTREM-1 showed promise for the diagnosis of sepsis.
脓毒症是一个严重的公共卫生问题。可溶性髓系细胞触发受体-1（sTREM-1）是炎症和感染过程的标志物，有潜力成为预测脓毒症病情发展的有用工具。通过贝叶斯网络将sTREM-1、C反应蛋白（CRP）和血常规相结合构建了脓毒症预测模型。在巴西东北部一家公立转诊医院对32例接受手术矫正的先天性心脏病患儿进行了一项转化研究。术后，脓毒症诊断组患者的sTREM-1平均值高于未诊断为脓毒症的患者（394.58 pg/mL对239.93 pg/mL，<0.001）。sTREM-1与脓毒症的ROC曲线分析显示，曲线下面积为0.761，95%置信区间为（0.587 - 0.935），P = 0.013。使用贝叶斯模型，我们发现脓毒症100%的概率与术后CRP血浓度高于71 mg/dL、血常规高于14,000个细胞/μL以及sTREM-1浓度高于临界值（283.53 pg/mL）有关。所提出的使用贝叶斯网络方法结合CRP、白细胞计数和术后sTREM-1的模型在脓毒症诊断方面显示出前景。
