MENG Chao, FAN Zhiqiang, TONG Qing, ZHU Guijun, ZHU Hui. Value of pulmonary artery compliance in acute respiratory distress syndrome[J]. Journal of Clinical Medicine in Practice, 2021, 25(19): 62-65, 73. DOI: 10.7619/jcmp.20211055
Citation: MENG Chao, FAN Zhiqiang, TONG Qing, ZHU Guijun, ZHU Hui. Value of pulmonary artery compliance in acute respiratory distress syndrome[J]. Journal of Clinical Medicine in Practice, 2021, 25(19): 62-65, 73. DOI: 10.7619/jcmp.20211055

Value of pulmonary artery compliance in acute respiratory distress syndrome

More Information
  • Received Date: March 11, 2021
  • Available Online: October 27, 2021
  • Published Date: October 14, 2021
  •   Objective  To detect pulmonary artery compliance and right heart function in patients with acute respiratory distress syndrome (ARDS), and to explore the relationships between pulmonary artery compliance and right heart function as well as short-term prognosis of ARDS.
      Methods  A total of 135 ARDS patients (observation group) and 46 healthy subjects (control group) were selected as subjects. According to the prognosis of 30 days, they were divided into death group (n=89) and survival group (n=46). Pulmonary artery systolic pressure (PASP) was measured, atrial septal defect area index (ASD-AI) and pulmonary arterial compliance index (PACI) were calculated, and PACI was used as indicators to evaluate the compliance of pulmonary artery; stroke volume (RVSV) and other indicators of right heart function were measured; the correlation between PACI and right heart function was analyzed; the levels of PASP, ASD-AI and PACI in patients with different prognosis were compared; the value of PACI in predicting short-term prognosis ARDS of patients was analyzed.
      Results  PASP, ASD-AI, right ventricular end-diastolic volume (RVEDV) and arterial partial pressure of carbon dioxide[pa(CO2)] in the observation group were significantly higher, and PACI, right ventricular ejection fraction (RVEF), right ventricular end-systolic volume (RVESV), mean arterial pressure (MAP), arterial partial pressure of oxygen[pa(O2)] and oxygenation index[pa(O2) to FiO2] were significantly lower than those in the control group (P < 0.05). In ARDS patients, PACI was positively correlated with RVEF, RVESV and pa(O2)/FiO2 (P < 0.05), and negatively correlated with RVEDV (P < 0.05). PASP and ASD-AI levels in the death group were significantly higher, PACI and pa(O2)/FiO2 levels were significantly lower than those in the survival group (P < 0.05). Receiver operating characteristic (ROC) curve showed that the area under the curve (AUC) of PACI for the short-term prognosis of ARDS was 0.844, the sensitivity was 70.8%, and the specificity was 80.4%.
      Conclusion  In ARDS patients, pulmonary artery compliance and right heart function both decrease. PACI has a certain correlation with right heart function, and PACI can play a role in predicting the short-term prognosis of ARDS patients.
  • [1]
    何园, 张硌, 刘慧莹, 等. 巨噬细胞对急性肺损伤后炎症的消解与组织修复的研究进展[J]. 中国呼吸与危重监护杂志, 2018, 17(4): 430-434. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGHW201804033.htm
    [2]
    LOPES-PACHECO M, ROBBA C, ROCCO P R M, et al. Current understanding of the therapeutic benefits of mesenchymal stem cells in acute respiratory distress syndrome[J]. Cell Biol Toxicol, 2020, 36(1): 83-102. doi: 10.1007/s10565-019-09493-5
    [3]
    FU S, THANGAVEL S, IVANOVA V. Cardiac dysfunction in acute respiratory distress syndrome[J]. Crit Care Nurs Q, 2019, 42(4): 448-458. doi: 10.1097/CNQ.0000000000000284
    [4]
    颜卫峰, 陈喆, 史淑静, 等. 急性呼吸窘迫综合征应用有创-无创序贯性机械通气治疗的效果及时机分析[J]. 山西医药杂志, 2019, 48(24): 3018-3020. doi: 10.3969/j.issn.0253-9926.2019.24.002
    [5]
    林锦乐, 傅萱, 曾世永, 等. 急性呼吸窘迫综合征患者血清Clara细胞蛋白16表达及与肺顺应性关系[J]. 中华实用诊断与治疗杂志, 2018, 32(3): 265-268. https://www.cnki.com.cn/Article/CJFDTOTAL-HNZD201803017.htm
    [6]
    REPESSÉX, VIEILLARD-BARON A. Right heart function during acute respiratory distress syndrome[J]. Ann Transl Med, 2017, 5(14): 295. doi: 10.21037/atm.2017.06.66
    [7]
    中华医学会呼吸病学分会. 急性肺损伤/急性呼吸窘迫综合征的诊断标准(草案)[J]. 中华结核和呼吸杂志, 2000, 23(4): 203. https://www.cnki.com.cn/Article/CJFDTOTAL-ZHJH200004005.htm
    [8]
    刘鸿飞, 崔颖. 急性呼吸窘迫综合征患者血管外肺水指数和肺血管通透性指数变化对其预后的影响[J]. 内科急危重症杂志, 2018, 24(3): 261-262. https://www.cnki.com.cn/Article/CJFDTOTAL-NKJW201803028.htm
    [9]
    李佳清, 袁彩云, 缪红军. 急性呼吸窘迫综合征患儿的右心保护通气策略[J]. 中国小儿急救医学, 2019, 26(6): 412-414. doi: 10.3760/cma.j.issn.1673-4912.2019.06.003
    [10]
    陈利红, 张蕾, 刘红娟, 等. 急性呼吸窘迫综合征患者肺死腔分数与患者死亡风险关系探讨[J]. 临床肺科杂志, 2018, 23(9): 1642-1645. doi: 10.3969/j.issn.1009-6663.2018.09.022
    [11]
    朱勇, 李吉明, 高冉冉, 等. 前列地尔对感染性休克并急性呼吸窘迫综合征患者呼吸功能、炎性反应、免疫调节及近期预后的影响[J]. 实用心脑肺血管病杂志, 2018, 26(10): 41-46. doi: 10.3969/j.issn.1008-5971.2018.10.010
    [12]
    贾子毅, 刘晓伟, 刘志. 机械通气氧合指数对ARDS患者预后评估的价值: 附228例回顾性分析[J]. 中华危重病急救医学, 2017, 29(1): 45-50. doi: 10.3760/cma.j.issn.2095-4352.2017.01.010
    [13]
    李欢, 吉训恋. 前列地尔对慢性阻塞性肺疾病合并肺动脉高压患者肺血管顺应性和血管内皮功能的影响[J]. 中国医药, 2020, 15(12): 1849-1853. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGYG202012008.htm
    [14]
    张臣, 陈辉, 赵蕾, 等. 磁共振相位对比法评价老年慢性阻塞性肺疾病患者肺动脉血流及右心功能[J]. 中华老年医学杂志, 2019, 38(5): 542-546. doi: 10.3760/cma.j.issn.0254-9026.2019.05.017
    [15]
    PRICE L C, WORT S J. Pulmonary hypertension in ARDS: inflammation matters![J]. Thorax, 2017, 72(5): 396-397. doi: 10.1136/thoraxjnl-2016-209199
    [16]
    张小花, 姜志荣, 孙安华, 等. 实时三维超声心动图和二维斑点追踪技术对冠心病患者左心室收缩同步性的评价[J]. 中国超声医学杂志, 2016, 32(10): 901-904. doi: 10.3969/j.issn.1002-0101.2016.10.014
    [17]
    夏炳杰, 施善阳. PiCCO监测对SAP合并ARDS患者心功能心脏前后负荷及血管外肺水的观察作用[J]. 河北医学, 2016, 22(5): 766-768. doi: 10.3969/j.issn.1006-6233.2016.05.024
    [18]
    PATERNOT A, REPESSÉX, VIEILLARD-BARON A. Rationale and description of right ventricle-protective ventilation in ARDS[J]. Respir Care, 2016, 61(10): 1391-1396. doi: 10.4187/respcare.04943
    [19]
    BEITLER J R, SANDS S A, LORING S H, et al. Quantifying unintended exposure to high tidal volumes from breath stacking dyssynchrony in ARDS: the BREATHE criteria[J]. Intensive Care Med, 2016, 42(9): 1427-1436. doi: 10.1007/s00134-016-4423-3
    [20]
    黄莉, 黎明, 陈娟, 等. 肺保护通气策略对小儿急性呼吸窘迫综合征心肺功能的影响及存活者随访的研究[J]. 贵州医药, 2018, 42(8): 950-952. doi: 10.3969/j.issn.1000-744X.2018.08.014
  • Related Articles

    [1]MA Jiajia, LIU Xiaoxin, XUE Bei, FENG Jing, ZHANG Zhengmin, YAO Liping, JU Xinxing, LIU Tingting. Risk prediction model construction of postoperative pulmonary infection in lung cancer patients undergoing four-level thoracoscopic surgery based on machine learning algorithms[J]. Journal of Clinical Medicine in Practice, 2025, 29(6): 111-117. DOI: 10.7619/jcmp.20245679
    [2]DU Fengming, CAO Xiaohua, LIU Ruichen, HU Chaoyang, SUN Yan. Establishment of artificial neural network model based on mitochondria-associated genes in Crohn's disease[J]. Journal of Clinical Medicine in Practice, 2024, 28(23): 8-15. DOI: 10.7619/jcmp.20242822
    [3]DING Jiali, LIU Xiaoguang, SHI Tian, MA Qiang, QI Yajie, LI Yuping, YU Hailong, LU Guangyu. Construction of a risk prediction model for enteral nutrition feeding intolerance in patients with severe cerebral hemorrhage based on machine learning algorithms[J]. Journal of Clinical Medicine in Practice, 2024, 28(12): 1-6. DOI: 10.7619/jcmp.20240467
    [4]SHI Haomin, YAN Su, QIAO Mengmeng, YANG Huilian. Research on gastric cancer lymph node metastasis prediction model based on machine learning algorithms[J]. Journal of Clinical Medicine in Practice, 2024, 28(1): 41-47, 61. DOI: 10.7619/jcmp.20233076
    [5]ZHANG Jiangnan, LI Ronghua, ZHOU Hongmei, XU Minyi, CAI Liangyu. Establishment of a predictive model for the risk of deep vein thrombosis after orthopedic surgery in the lower extremities and its verification[J]. Journal of Clinical Medicine in Practice, 2023, 27(23): 73-78. DOI: 10.7619/jcmp.20231971
    [6]ZHAN Xianfa, YU Xiaoya, WANG Hongjun, XIONG Kunlin. Efficacy of three machine learning algorithms in evaluating stability of carotid plaque in patients with cerebral infarction[J]. Journal of Clinical Medicine in Practice, 2023, 27(22): 6-12. DOI: 10.7619/jcmp.20232657
    [7]WANG Yu, CHU Jiadong, SUN Na, HAN Qiang, SHEN Yueping, ZHOU Lei, ZHU Xinping, ZHANG Xiaobin, YANG Yong. Construction of a predictive model for auxiliary diagnosis of perinatal depression and screening of machine learning algorithm[J]. Journal of Clinical Medicine in Practice, 2023, 27(18): 93-99. DOI: 10.7619/jcmp.20232044
    [8]WANG Yiren, LIU Aiai, ZHAN Xiang, LUO Yan, ZHOU Ping. Screening of genetic markers for diagnosis of nasopharyngeal carcinoma based on machine learning algorithm[J]. Journal of Clinical Medicine in Practice, 2023, 27(7): 6-11. DOI: 10.7619/jcmp.20230091
    [9]WANG Xiaoli, QU Hang, CHENG Weiyan, ZHAO Yi, CAI Yujian, WANG Wei. Application value of X-ray radiomics in distinguishing benign and malignant breast lesions and the efficacy comparison of three predictive models[J]. Journal of Clinical Medicine in Practice, 2021, 25(8): 21-24. DOI: 10.7619/jcmp.20210555
    [10]Influence of electro acupuncture on learning and memory capability and hippocampal neuronal apoptosis in AD model SD rats[J]. Journal of Clinical Medicine in Practice, 2015, (1): 1-6. DOI: 10.7619/jcmp.201501001

Catalog

    Article views (281) PDF downloads (10) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return