Department of Neonatology, The First Affiliated Hospital of Xinjiang Medical University, 830054, Urumqi, Xinjiang, China.
BMC Pediatr. 2023 Jan 28;23(1):47. doi: 10.1186/s12887-023-03853-1.
Intraventricular hemorrhage (IVH) is the most common type of brain injury in newborns, especially in newborns with Neonatal acute respiratory distress syndrome (ARDS). IVH can cause brain parenchyma damage and long-term neurological sequelae in children. Early identification and prevention of sequelae are essential. This study aims to establish a predictive nomogram for the early prediction of IVH in newborns with ARDS.
From 2019 to 2021, we collected data from 222 infants diagnosed with ARDS in the Department of Neonatology, First Affiliated Hospital of Xinjiang Medical University. Infants have been randomly assigned to the training set (n = 161) or the validation set (n = 61) at a ratio of 7:3. Variables were screened using the Least Absolute Contract and Selection Operator (LASSO) regression to create a risk model for IVH in infants with ARDS. The variables chosen in the LASSO regression model were used to establish the prediction model using multivariate logistic regression analysis.
We recognized 4 variables as independent risk factors for IVH in newborns with ARDS via LASSO analysis, consisting of premature rupture of membranes (PROM), pulmonary surfactant (PS) dosage, PH and Arterial partial pressure of oxygen (PaO). The C-Index for this dataset is 0.868 (95% CI: 0.837-0.940) and the C index in bootstrap verification is 0.852 respectively. The analysis of the decision curve shows that the model can significantly improve clinical efficiency in predicting IVH. We also provide a website based on the model and open it to users for free, so that the model can be better applied to clinical practice.
In conclusion, the nomogram based on 4 factors shows good identification, calibration and clinical practicability. Our nomographs can help clinicians make clinical decisions, screen high-risk ARDS newborns, and facilitate early identification and management of IVH patients.
脑室内出血（IVH）是新生儿中最常见的脑损伤类型，尤其是患有新生儿急性呼吸窘迫综合征（ARDS）的新生儿。IVH 可导致儿童脑实质损伤和长期神经后遗症。早期识别和预防后遗症至关重要。本研究旨在建立预测 ARDS 新生儿 IVH 的预测列线图。
2019 年至 2021 年，我们从新疆医科大学第一附属医院新生儿科收集了 222 例诊断为 ARDS 的婴儿的数据。婴儿按 7:3 的比例随机分配到训练集（n=161）或验证集（n=61）。使用最小绝对收缩和选择算子（LASSO）回归筛选变量，为 ARDS 婴儿的 IVH 创建风险模型。使用多变量逻辑回归分析，使用 LASSO 回归模型中选择的变量建立预测模型。
通过 LASSO 分析，我们识别出 4 个变量是 ARDS 新生儿 IVH 的独立危险因素，包括胎膜早破（PROM）、肺表面活性剂（PS）剂量、pH 值和动脉氧分压（PaO）。该数据集的 C 指数为 0.868（95%CI：0.837-0.940），bootstrap 验证的 C 指数为 0.852。决策曲线分析表明，该模型可显著提高 IVH 预测的临床效率。我们还基于该模型建立了一个网站，并免费向用户开放，以便更好地将模型应用于临床实践。
综上所述，基于 4 个因素的列线图显示出良好的识别、校准和临床实用性。我们的列线图可以帮助临床医生做出临床决策，筛选高危 ARDS 新生儿，并有助于早期识别和管理 IVH 患者。
