PeerJ · 2024
Clinical characteristics and a diagnostic model for high-altitude pulmonary edema in habitual low altitude dwellers.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Li Qiong, Xu Zhichao, Gong Qianhui, Shen Xiaobing
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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 (P = 0.793) and validation datasets (P = 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.
CONCLUSION: 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.
METHODS: 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.
RESULTS: 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 (P = 0.793) and validation datasets (P = 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.
CONCLUSION: 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.