High-altitude Illnesses and Air Travel

Villca, Nelson; Asturizaga, Adriana; Heath-Freudenthal, Alexandra

doi:10.1016/j.pcl.2020.09.015

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Previous studies17–19 have confirmed that rapid altitude gain significantly correlates with the development of HAPE symptoms. For example, studies suggested that the speed and time to reach a certain altitude by plane are significant risk factors for HAPE 20. However, this study found that train travel was riskier than air travel.…”

Section: Discussionmentioning

confidence: 58%

“…For example, studies suggested that the speed and time to reach a certain altitude by plane are significant risk factors for HAPE. 20 However, this study found that train travel was riskier than air travel. This is mainly because, with increasing altitude, the oxygen content in the air becomes lower for passengers entering Tibet by train, which increases the passengers' Open access morbidity.…”

Section: Analysis Of Risk Factors Influencing the Occurrence Of Hapementioning

confidence: 73%

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Predictive model for estimating the risk of high-altitude pulmonary edema: a single-centre retrospective outcome-reporting study

Suona,

Gesang,

Danzeng

et al. 2023

BMJ Open

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ObjectiveTo develop the first prediction model based on the common clinical symptoms of high-altitude pulmonary edema (HAPE), enabling early identification and an easy-to-execute self-risk prediction tool.MethodsA total of 614 patients who consulted People’s Hospital of Tibet Autonomous Region between January 2014 and April 2022 were enrolled. Out of those, 508 patients (416 males and 92 females) were diagnosed with HAPE and 106 were patients without HAPE (33 females and 72 males). They were randomly distributed into training (n=431) and validation (n=182) groups. Univariate and multivariate analysis were used to screen predictors of HAPE selected from the 36 predictors; nomograms were established based on the results of multivariate analysis. The receiver operating characteristic curve (ROC) was developed to obtain the area under the ROC curve (AUC) of the predictive model, and its predictive power was further evaluated by calibrating the curve, while the Decision Curve Analysis (DCA) was developed to evaluate the clinical applicability of the model, which was visualised by nomogram.ResultsAll six predictors were significantly associated with the incidence of HAPE, and two models were classified according to whether the value of SpO2(percentage of oxygen in the blood) was available in the target population. Both could accurately predict the risk of HAPE. In the validation cohort, the AUC of model 1 was 0.934 with 95% CI (0.848 to 1.000), and model 2 had an AUC of 0.889, 95% CI (0.779 to 0.999). Calibration plots showed that the predicted and actual HAPE probabilities fitted well with internal validation, and the clinical decision curve shows intervention in the risk range of 0.01–0.98, resulting in a net benefit of nearly 99%.ConclusionThe recommended prediction model (nomogram) could estimate the risk of HAPE with good precision, high discrimination and possible clinical applications for patients with HAPE. More importantly, it is an easy-to-execute scoring tool for individuals without medical professionals’ support.

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Section: Discussionmentioning

confidence: 58%

Section: Analysis Of Risk Factors Influencing the Occurrence Of Hapementioning

confidence: 73%