Xu Jixiang, Han Xiaoxiao, Qi Yinliang, Zhou Xiaomei
Department of Hyperbaric Oxygen, The Second People's Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei, 230011, Anhui, China.
Department of Critical Medicine, The Second People's Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei, 230011, Anhui, China.
Biomed Eng Online. 2025 Jul 12;24(1):88. doi: 10.1186/s12938-025-01425-1.
This study aimed to develop and validate a clinical prediction model for assessing the risk of concurrent pulmonary infection (PI) in patients recovering from intracerebral hemorrhage (ICH).
In this retrospective study, we analyzed clinical data from 761 patients in the subacute recovery phase of ICH, of whom 504 developed PI and 257 did not. Univariate logistic regression was initially used to identify potential risk factors, followed by variable selection through the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Predictors selected by LASSO were entered into a multivariate logistic regression to establish a final model. A nomogram was constructed based on the significant variables. The model's discrimination was evaluated using the area under the receiver operating characteristic curve (AUC), and its calibration was assessed using calibration plots and the Hosmer-Lemeshow goodness-of-fit test. Clinical utility was evaluated via decision curve analysis (DCA). Positive predictive value (PPV) and negative predictive value (NPV) were also calculated at the optimal threshold.
Eight independent predictors were identified: age, prophylactic antibiotic use, disturbance of consciousness, tracheotomy, dysphagia, duration of bed rest, nasal feeding, and procalcitonin level. The model demonstrated excellent discriminative ability with an AUC of 0.901(95%CI 0.878-0.924) and good calibration (Hosmer-Lemeshow test, P = 0.982). At the optimal cut-off point, the PPV was 92.6% and the NPV was 68.0%. DCA indicated favorable clinical benefit across a wide range of threshold probabilities.
We developed a nomogram-based prediction model that accurately identifies the risk of pulmonary infection in patients recovering from ICH. This model offers valuable support for early clinical decision-making and targeted preventive strategies.
本研究旨在开发并验证一种用于评估脑出血（ICH）康复患者并发肺部感染（PI）风险的临床预测模型。
在这项回顾性研究中，我们分析了761例ICH亚急性恢复期患者的临床数据，其中504例发生了PI，257例未发生。最初采用单因素逻辑回归来识别潜在风险因素，随后通过最小绝对收缩和选择算子（LASSO）回归进行变量选择。将LASSO选择的预测因子纳入多因素逻辑回归以建立最终模型。基于显著变量构建列线图。使用受试者操作特征曲线（ROC）下面积评估模型的辨别力，并使用校准图和Hosmer-Lemeshow拟合优度检验评估其校准情况。通过决策曲线分析（DCA）评估临床实用性。在最佳阈值处还计算了阳性预测值（PPV）和阴性预测值（NPV）。
确定了8个独立预测因子：年龄、预防性使用抗生素、意识障碍、气管切开术、吞咽困难、卧床时间、鼻饲和降钙素原水平。该模型表现出出色的辨别能力，AUC为0.901（95%CI 0.878 - 0.924），校准良好（Hosmer-Lemeshow检验，P = 0.982）。在最佳临界点，PPV为92.6%，NPV为68.0%。DCA表明在广泛的阈值概率范围内具有良好的临床益处。
我们开发了一种基于列线图的预测模型，可准确识别ICH康复患者的肺部感染风险。该模型为早期临床决策和针对性预防策略提供了有价值的支持。
