Pediatric Allergy and Clinical Immunology Research Unit, Division of Allergy and Immunology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand.
Front Immunol. 2022 Feb 21;13:825867. doi: 10.3389/fimmu.2022.825867. eCollection 2022.
Hypogammaglobulinemia is a condition that requires prompt diagnosis and treatment. Unfortunately, serum immunoglobulin (Ig) measurements are not widely accessible in numerous developing countries. Serum globulin is potentially the best candidate for screening of low IgG level (IgGLo) due to its high availability, low cost, and rapid turnover time. However, multiple factors may influence the probability of prediction. Our study aimed to establish a simple prediction model using serum globulin to predict the likelihood of IgGLo in children. For retrospective data of patients who were suspected of having IgGLo, both serum IgG and globulin were simultaneously collected and measured. Potential factors interfering with serum globulin and IgG levels were investigated for their impact using bivariate binary logistic regression. A multivariate binary logistic regression was used to generate a formula and score to predict IgGLo. We obtained 953 samples from 143 pediatric patients. A strong positive correlation between serum globulin and IgG levels was observed (r=0.83, p < 0.001). A screening test model using serum globulin and illness status was constructed to predict IgGLo. The formula for predicting IgGLo was generated as follows; Predicted score = (2 x globulin (g/dl)) - illness condition score (well=0, sick=1). When the score was <4, the patient has the probability of having IgGLo with a sensitivity of 0.78 (0.71, 0.84), a specificity of 0.71 (0.68, 0.74), PPV of 0.34 (0.29, 0.40) and NPV of 0.94 (0.92, 0.96). This formula will be useful as rapid and inexpensive screening tool for early IgGLo detection, particularly in countries/locations where serum IgG measurement is inaccessible.
低丙种球蛋白血症是一种需要及时诊断和治疗的疾病。不幸的是，在许多发展中国家，血清免疫球蛋白（Ig）的测量并不广泛。球蛋白由于其高可用性、低成本和快速周转时间，是筛查低 IgG 水平（IgGLo）的最佳候选者。然而，多个因素可能影响预测的可能性。我们的研究旨在建立一个使用血清球蛋白预测儿童 IgGLo 可能性的简单预测模型。对于疑似 IgGLo 患者的回顾性数据，同时收集和测量血清 IgG 和球蛋白。使用二元二项逻辑回归分析潜在的影响球蛋白和 IgG 水平的因素。使用多元二项逻辑回归生成公式和评分来预测 IgGLo。我们从 143 名儿科患者中获得了 953 个样本。观察到血清球蛋白和 IgG 水平之间存在很强的正相关（r=0.83，p <0.001）。使用血清球蛋白和疾病状态构建了一种筛选试验模型来预测 IgGLo。预测 IgGLo 的公式如下：预测评分=（2 x 球蛋白（g/dl））-疾病状况评分（健康=0，患病=1）。当评分<4 时，患者患有 IgGLo 的概率为 0.78（0.71，0.84），灵敏度为 0.71（0.68，0.74），PPV 为 0.34（0.29，0.40），NPV 为 0.94（0.92，0.96）。该公式将作为快速且廉价的 IgGLo 早期检测筛选工具非常有用，特别是在无法测量血清 IgG 的国家/地区。
