School of Public Health, Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China.
School of Public Health, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, Jiangsu, China.
PeerJ. 2024 Sep 26;12:e18084. doi: 10.7717/peerj.18084. eCollection 2024.
The fatal risk of high-altitude pulmonary edema (HAPE) is attributed to the inaccurate diagnosis and delayed treatment. This study aimed to identify the clinical characteristics and to establish an effective diagnostic nomogram for HAPE in habitual low altitude dwellers.
A total of 1,255 individuals of Han Chinese were included in the study on the Qinghai-Tibet Plateau at altitudes exceeding 3,000 m. LASSO algorithms were utilized to identify significant predictors based on Akaike's information criterion (AIC), and a diagnostic nomogram was developed through multivariable logistic regression analysis. Internal validation was conducted through bootstrap resampling. Model performance was evaluated using ROC curves and the Hosmer-Lemeshow test.
The nomogram included eleven predictive factors and demonstrated high discrimination with an AUC of 0.787 (95% CI [0.757-0.817]) and 0.833 (95% CI [0.793-0.874]) in the training and validation cohorts, respectively. Calibration curves were assessed in both the training ( = 0.793) and validation datasets ( = 0.629). Confusion matrices revealed accuracies of 70.95% and 74.17% for the training and validation groups. Furthermore, decision curve analysis supported the use of the nomogram for patients with HAPE.
We propose clinical features and column charts based on hematological parameters and demographic variables, which can be conveniently used for the diagnosis of HAPE. In high-altitude areas with limited emergency environments, a diagnostic model can provide fast and reliable diagnostic support for medical staff, helping them make better treatment decisions.
高原肺水肿（HAPE）的致命风险归因于诊断不准确和治疗延误。本研究旨在确定习惯性低海拔居住者 HAPE 的临床特征，并建立有效的诊断列线图。
共有 1255 名汉族人参与了在海拔 3000 米以上的青藏高原上的研究。基于赤池信息量准则（AIC），LASSO 算法用于识别显著预测因子，通过多变量逻辑回归分析建立诊断列线图。通过 bootstrap 重采样进行内部验证。通过 ROC 曲线和 Hosmer-Lemeshow 检验评估模型性能。
该列线图包含 11 个预测因素，在训练和验证队列中具有较高的区分度，AUC 分别为 0.787（95%CI [0.757-0.817]）和 0.833（95%CI [0.793-0.874]）。校准曲线在训练集（ = 0.793）和验证数据集（ = 0.629）中进行了评估。混淆矩阵显示训练组和验证组的准确率分别为 70.95%和 74.17%。此外，决策曲线分析支持使用该列线图对 HAPE 患者进行诊断。
我们提出了基于血液学参数和人口统计学变量的临床特征和列线图，可以方便地用于 HAPE 的诊断。在紧急环境有限的高海拔地区，诊断模型可以为医务人员提供快速可靠的诊断支持，帮助他们做出更好的治疗决策。
Zotero 插件
