Multivariate predictive model for asymptomatic spontaneous bacterial peritonitis in patients with liver cirrhosis

BACKGROUNDSpontaneous bacterial peritonitis (SBP) is a detrimental infection of the asciticfluid in liver cirrhosis patients, with high mortality and morbidity. Earlydiagnosis and timely antibiotic administration have successfully decreased themortality rate to 20%-25%. However, many patients cannot be diagnosed in theearly stages due to the absence of classical SBP symptoms. Early diagnosis ofasymptomatic SBP remains a great challenge in the clinic.AIMTo establish a multivariate predictive model for early diagnosis of asymptomaticSBP using positive microbial cultures from liver cirrhosis patients with ascites.METHODSA total of 98 asymptomatic SBP patients and 98 ascites liver cirrhosis patients withnegative microbial cultures were included in the case and control groups,respectively. Multiple linear stepwise regression analysis was performed toidentify potential indicators for asymptomatic SBP diagnosis. The diagnosticperformance of the model was estimated using the receiver operatingcharacteristic curve.RESULTSPatients in the case group were more likely to have advanced disease stages,cirrhosis related-complications, worsened hematology and ascites, and higher mortality. Based on multivariate analysis, the predictive model was as follows: y (P) = 0.018 + 0.312 × MELD (model of end-stage liver disease) + 0.263 × PMN(ascites polymorphonuclear) + 0.184 × N (blood neutrophil percentage) + 0.233 ×HCC (hepatocellular carcinoma) + 0.189 × renal dysfunction. The area under thecurve value of the established model was 0.872, revealing its high diagnosticpotential. The diagnostic sensitivity was 73.5% (72/98), the specificity was 86.7%(85/98), and the diagnostic efficacy was 80.1%.CONCLUSIONOur predictive model is based on the MELD score, polymorphonuclear cells,blood N, hepatocellular carcinoma, and renal dysfunction. This model mayimprove the early diagnosis of asymptomatic SBP.

Fractional derivative multivariable grey model for nonstationary sequence and its application

Most of the existing multivariable grey models are based on the 1-order derivative and 1-order accumulation, which makes the parameters unable to be adjusted according to the data characteristics of the actual problems. The results about fractional derivative multivariable grey models are very few at present. In this paper, a multivariable Caputo fractional derivative grey model with convolution integral CFGMC(q, N) is proposed. First, the Caputo fractional difference is used to discretize the model, and the least square method is used to solve the parameters. The orders of accumulations and differential equations are determined by using particle swarm optimization(PSO). Then, the analytical solution of the model is obtained by using the Laplace transform, and the convergence and divergence of series in analytical solutions are also discussed. Finally, the CFGMC(q, N) model is used to predict the municipal solid waste(MSW). Compared with other competition models, the model has the best prediction effect. This study enriches the model form of the multivariable grey model, expands the scope of application, and provides a new idea for the development of fractional derivative grey model.

克罗恩病患者C1orf106、IL1RN和IL10基因多态性与英夫利昔单抗诱导治疗后药物谷浓度的关系

背景:英夫利昔单抗(IFX)诱导治疗克罗恩病后的药物谷浓度会影响近、远期疗效,但药物谷浓度在个体间有很大差异。本研究旨在探究FCGR3A、ATG16L1、C1orf106、OSM、OSMR、NF-jB1、IL1RN及IL10等基因多态性是否与这种个体差异相关,并通过建立多因素回归模型来预测诱导治疗后IFX血药浓度。方法:回顾性纳入189例接受IFX治疗的克罗恩病患者。检测诱导治疗后IFX谷浓度和8个基因中41个标签单核甘酸多态性(tag SNP),分析二者之间的相关性。建立多因素逻辑回归模型来预测诱导治疗后的IFX浓度是否达到治疗剂量(3μg/mL)。结果:rs7587051(P=0.015)、rs143063741(P<0.001)、rs442905(P=0.046)、rs59457695(P=0.022)、rs3213448(P=0.011)和rs3021094(P=0.013)等6个SNP与诱导治疗后IFX谷浓度显著相关。IFX谷浓度的多因素预测模型纳入的变量包括:血浆白蛋白(P=0.002)、rs442905(P=0.025)、rs59457695(P=0.049)、rs3213448(P=0.056)和rs3021094(P=0.047)。该模型在训练队列中的受试者操作特征曲线下面积(AUROC)为0.758,在验证队列中的AUROC为0.733。结论:诱导治疗后IFX血药浓度的个体差异很大程度上是由C1orf106、IL1RN和IL10基因多态性所致,基于这些基因多态性的多因素预测模型可有效预测IFX血药浓度。