Advertisement

Cluster Computing

, Volume 22, Supplement 2, pp 4703–4709 | Cite as

The mechanism of active respiratory circulation in patients with chronic respiratory failure COPD

  • Yingchao ZhangEmail author
  • Yun Mao
Article
  • 41 Downloads

Abstract

One early intervention analysis method for ICU mechanical ventilation based on multiple factors logistic regression analysis has been proposed to ensure the safety of early exercise of ICU mechanical ventilation patient and reduce the happening of adverse event related to exercise. Firstly, evaluate and screen patient, make suitable early exercise program, make safety management hierarchically, guard the adverse reaction during exercise process and make 1105 times of different hierarchical exercises in total for 158 patients. There are 16 cases that stop exercise halfway. The happened adverse reaction of exercise as well as adverse events related to exercise account for 1.4% of the total exercise events. Experimental results have shown that early exercise of ICU mechanical ventilation patient is safe and feasible through optimizing management process.

Keywords

Multiple factors Logistic regression ICU guardianship Mechanical ventilation Early intervention 

References

  1. 1.
    Estenssoro, E., González, F., Laffaire, E., et al.: Shock on admission day is the best predictor of prolonged mechanical ventilation in the ICU. Chest 127(2), 598–603 (2005)CrossRefGoogle Scholar
  2. 2.
    Yamashita, A., Yamasaki, M., Matsuyama, H., et al.: Risk factors and prognosis of pain events during mechanical ventilation: a retrospective study. J. Intensive Care 5(1), 17 (2017)CrossRefGoogle Scholar
  3. 3.
    Rimantas, B., Edmundas, Širvinskas, Birute, K., et al.: A case–control study of readmission to the intensive care unit after cardiac surgery. Med. Sci. Monit. Int. Med. J. Exp. Clin. Res. 19(1), 148–152 (2013)Google Scholar
  4. 4.
    Hung, T.C., Lai, Y.F., Tseng, C.W., et al.: Trend analysis of hospital resource utilization for prolonged mechanical ventilation patients in Taiwan: a population-based study. Respir. Care 58(4), 669–675 (2013)CrossRefGoogle Scholar
  5. 5.
    Kollef, M.H., Levy, N.T., Ahrens, T.S., et al.: The use of continuous IV sedation is associated with prolongation of mechanical ventilation. Chest 114(2), 541–548 (1998)CrossRefGoogle Scholar
  6. 6.
    Toft, P., Olsen, H.T., Jørgensen, H.K., et al.: Non-sedation versus sedation with a daily wake-up trial in critically ill patients receiving mechanical ventilation (NONSEDA Trial): study protocol for a randomised controlled trial. Trials 15(1), 1–11 (2014)CrossRefGoogle Scholar
  7. 7.
    Kollef, M.H., Shapiro, S.D., Harz, B.V., et al.: Patient transport from intensive care increases the risk of developing ventilator-associated pneumonia. Chest 112(3), 765–773 (1997)CrossRefGoogle Scholar
  8. 8.
    Fukuda, S., Miyauchi, T., Fujita, M., et al.: Risk factors for late defecation and its association with the outcomes of critically ill patients: a retrospective observational study. J. Intensive Care 4(1), 1–8 (2016)CrossRefGoogle Scholar
  9. 9.
    Egbe, A.C., Uppu, S.C., Mittnacht, A.J., et al.: Primary tetralogy of Fallot repair: predictors of intensive care unit morbidity. Asian Cardiovasc. Thorac. Ann. 22(7), 794 (2014)CrossRefGoogle Scholar
  10. 10.
    Jubran, A., Lawm, G., Kelly, J., et al.: Depressive disorders during weaning from prolonged mechanical ventilation. Intensive Care Med. 36(5), 828–835 (2010)CrossRefGoogle Scholar
  11. 11.
    Hori, D., Hogue, C., Adachi, H., et al.: Perioperative optimal blood pressure as determined by ultrasound tagged near infrared spectroscopy and its association with postoperative acute kidney injury in cardiac surgery patients. Interact. Cardiovasc. Thorac. Surg. 22(4), 445 (2016)CrossRefGoogle Scholar
  12. 12.
    Kim, M.J., Park, Y.H., Park, Y.S., et al.: Associations between prolonged intubation and developing post-extubation dysphagia and aspiration pneumonia in non-neurologic critically ill patients. Ann. Rehabil Med 39(5), 763–771 (2015)CrossRefGoogle Scholar
  13. 13.
    Sabaté, S., Mazo, V., Canet, J.: Predicting postoperative pulmonary complications: implications for outcomes and costs. Curr. Opin. Anaesthesiol. 27(2), 201–209 (2014)CrossRefGoogle Scholar
  14. 14.
    Horster, S., Stemmler, H.J., Mandel, P.C., et al.: Mortality of patients with hematological malignancy after admission to the intensive care unit. Onkologie 35(10), 556–561 (2012)CrossRefGoogle Scholar
  15. 15.
    Rahmanian, P.B., Kröner, A., Langebartels, G., et al.: Impact of major non-cardiac complications on outcome following cardiac surgery procedures: logistic regression analysis in a very recent patient cohort. Interact. Cardiovasc. Thorac. Surg. 17(2), 326–327 (2013)CrossRefGoogle Scholar
  16. 16.
    Malarkodi, M.P., Arunkumar, N., Venkataraman, V.: Gabor wavelet based approach for face recognition. Int. J. Appl. Eng. Res. 8(15), 1831–1840 (2013)Google Scholar
  17. 17.
    Arunkumar, N., Venkataraman, V., Thivyashree, V., Lavanya, : A moving window approximate entropy based neural network for detecting the onset of epileptic seizures. Int. J. Appl. Eng. Res. 8(15), 1841–1847 (2013)Google Scholar
  18. 18.
    Stephygraph, L.R., Arunkumar, N.: Brain-actuated wireless mobile robot control through an adaptive human–machine interface. Adv. Intell. Syst. Comput. 397, 537–549 (2016)Google Scholar
  19. 19.
    Arunkumar, N., Mohamed Sirajudeen, K.M.: Approximate entropy based ayurvedic pulse diagnosis for diabetics—a case study. In: TISC 2011—Proceedings of the 3rd International Conference on Trendz in Information Sciences and Computing, Art. No. 6169099, pp. 133–135 (2011)Google Scholar
  20. 20.
    Arunkumar, N., Jayalalitha, S., Dinesh, S., Venugopal, A., Sekar, D.: Sample entropy based ayurvedic pulse diagnosis for diabetics. In: Proceedings of the IEEE-International Conference on Advances in Engineering, Science and Management, ICAESM-2012, Art. No. 6215973, pp. 61–62 (2012)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of RespirationTianjin Baodi HospitalTianjinChina
  2. 2.Department of General MedicineJinhua Municipal Central HospitalJinhuaChina

Personalised recommendations