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Medical appraisal of Chinese military aircrew with abnormal results of coronary computed tomographic angiography

World Journal of Cardiology 2024年第9期16卷 522-530页

作者： Jia Zeng Yao Zhao Di Gao Xiang Lu Jing-Jing Dong Yan-Bing Liu Bin Shen Naval Medical Center Naval Medical University of Chinese PLAShanghai 200052China No.92329 Station Health Team Chinese PLAHuludao 125000Liaoning ProvinceChina

BACKGROUND Coronary artery diseases can cause myocardial ischemia and hypoxia,angina pectoris,myocardial infarction,arrhythmia,and even sudden death led to inflight incapacitation of *** the main cause of grounding due to illness,they severe threats to the health and fighting strength of military *** warning in an early and accurate manner and early intervention of diseases possibly resulting in inflight incapacitation are key emphases of aeromedical support in *** To figure out the flight factors and clinical characteristics of military aircrew with abnormal results of coronary artery computed tomographic angiography(CTA),thereby rendering theoretical references for clinical aeromedical support of military flying *** The clinical data of 15 flying personnel who received physical examinations in a military medical center from December 2020 to June 2023 and were diagnosed with coronary artery diseases by coronary artery CTA were collected and retrospectively analyzed,and a descriptive statistical analysis was conducted on their onset age,aircraft type and clinical *** The 15 military flying personnel diagnosed with coronary artery diseases by coronary artery CTA were composed of 9 pilots,1 navigator and 5 air combat service ***-vessel disease was detected in 9 flying personnel,among which 8(88.9%)were *** personnel with multi-vessel disease had higher content of cholesterol,low-density lipoprotein cholesterol and apolipoprotein B than those with single-vessel *** Coronary artery diseases are the major heart disease for the grounding of flying personnel due to illness,which can lead to inflight *** artery CTA is conducive to early detection and early intervention treatment of such diseases in clinic.

关键词： Military flying personnel Coronary artery disease Medical appraisal

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Cardiovascular age of aviation personnel: based on the principal component analysis of heart rate and blood pressure variability

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Journal of Medical Colleges of PLA(China) 2004年第1期19卷 64-70页

作者：牛有国王守岩张玉海王兴邦张立藩 Department of Aerospace Physiology Department of Medical Statistics Fourth Military Medical University

Objective: To introduce a method to calculate cardiovascular age, a new, accurate and much simpler index for assessing cardiovascular autonomic regulatory function, based on statistical analysis of heart rate and blood pressure variability (HRV and BPV) and baroreflex sensitivity (BRS) data. Methods: Firstly, HRV and BPV of 89 healthy aviation personnel were analyzed by the conventional autoregressive (AR) spectral analysis and their spontaneous BRS was obtained by the sequence method. Secondly, principal component analysis was conducted over original and derived indices of HRV, BPV and BRS data and the relevant principal components, PCi orig and PCi deri (i=1, 2, 3,...) were obtained. Finally, the equation for calculating cardiovascular age was obtained by multiple regression with the chronological age being assigned as the dependent variable and the principal components significantly related to age as the regressors. Results: The first four principal components of original indices accounted for over 90% of total variance of the indices, so did the first three principal components of derived indices. So, these seven principal components could reflect the information of cardiovascular autonomic regulation which was embodied in the 17 indices of HRV, BPV and BRS exactly with a minimal loss of information. Of the seven principal components, PC2 orig , PC4 orig and PC2 deri were negatively correlated with the chronological age ( P personnel ( r =0.73, P personnel.

关键词： flying personnel heart rate variability blood pressure variability baroreflex sensitivity age principal components analysis multiple regression analysis

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