Abstract

Background and Aims: In intensive care units (ICUs), clinicians use various mathematical models to assess and monitor respiratory illness. However, evidence suggests that some pulmonary internal and external physiolog¬ical factors are not integrated in the calculation of these models. This research aimed to demonstrate the variation in these models within a short period in critical care settings. Methods: A retrospective cohort study was conducted from 2017 to 2019 with adults admitted to an ICU and mechanically ventilated for more than 24 hours. Two different arterial blood gas samples were selected within a mean of 120 minutes. The coefficient of variation percent [(CV%)≤SD/Mean] was calculated. Results: In total, 211 intubated ICU patients were included. The mean age was 57 (SD +/- 19.62) years, and 33% were female. The degree of variation of the average CV% for all the mathematical models was 14%-30%. The mean CV% for the arterial oxygen tension to inspired oxygen fraction (PaO2/FiO2) and arterial/alveolar oxygen tension ratio (PaO2/ PAO2) was 14%, which is a low variation compared to the respiratory index (RI) for which the mean CV% was 30%. Conclusions: The findings of this study indicate that most of the existing oxygen mathematical models used in clinical settings lack physiological realism, necessitating a new mathematical model that integrate both assess illness severity and enhance other models’ accuracy.

Keywords: Mathematical model - Pulmonary oxygen indices - Respiratory index - Oxygen index - Oxygen sat¬uration index

References

  1. 1.Armstrong J, Guleria A, Girling K. Evaluation of gas exchange deficit in thecriticallyill.ContinEducAnaesthe- 38 Majmaah Journal of Health Sciences ,Vol. 12, Issue 2 , April - 2024, Shawwal - 1445 Assessment of the variation of the pulmonary gas exchange model in the critical care setting sia, Crit Care Pain. 2007;7(4):131–4.
  2. 2.Kathirgamanathan A, McCahon RA, Hardman J. G. Indices of pulmonary oxygenation in pathological lung states: an investigation using high-fidelity, computational modelling. BJA Br J Anaesth. 2009;103(2):291–7.
  3. 3.Zetterström H. Assessment of the efficiency of pulmonary oxygenation. The choice of oxygenation index. Acta Anaesthesiol Scand. 1988;32(7):579–84.
  4. 4.Nirmalan M, Willard T, Columb MO, Nightingale P. Effect of changes in arterial-mixed venous oxygen content difference (C(a-v)O2) on indices of pulmonary oxygen transfer in a model ARDS lung. Br JAnaesth. 2001/09/28. 2001;86(4):477–85.
  5. 5.Mistry S, Brook BS, Saffaran S, Chikhani M, Hannon DM, Laffey JG, et al. A computational cardiopulmonary physiology simulator accurately predicts individual patient responses to changes in mechanical ventilator settings. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE; 2022. p. 3261–4.
  6. 6.Hannon DM, Mistry S, DasA, Saffaran S, Laffey JG, Brook BS, et al. Modeling MechanicalVentilation In Silico— Potential and Pitfalls. In: Seminars in Respiratory and Critical Care Medicine. Thieme Medical Publishers, Inc. 333 Seventh Avenue, 18th Floor, New York, NY …; 2022. p. 335–45.
  7. 7.Sharafutdinov K, Fritsch SJ, Iravani M, Ghalati PF, Saffaran S, Bates DG, et al. Computational simulation of virtual patients reduces dataset bias and improves machine learning-based detection of ARDS from noisy heterogeneous ICU datasets. IEEE Open J Eng Med Biol. 2023;
  8. 8.Barakat CS, Sharafutdinov K, Busch J, Saffaran S, Bates DG, Hardman JG, et al. Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome. Diagnostics. 2023;13(12):2098.
  9. 9.Ferkol T, Schraufnagel D. The Global Burden of Respiratory Disease. http://dx.doi.org/101513/AnnalsATS201311-405PS. 2014;11(3):404–

Peer review

Peer reviewed by 1 reviewer, who chose to remain anonymous.

Majmaah Journal of Health Sciences · eISSN 1658-8223 · Majmaah University · open access