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Pascual-Saldaña, H.; Masip-Bruin, X.; Asensio, A.; Alonso, A.; Blanco, I. Personalized Oxygen Dosing System. Encyclopedia. Available online: https://encyclopedia.pub/entry/55066 (accessed on 17 April 2024).
Pascual-Saldaña H, Masip-Bruin X, Asensio A, Alonso A, Blanco I. Personalized Oxygen Dosing System. Encyclopedia. Available at: https://encyclopedia.pub/entry/55066. Accessed April 17, 2024.
Pascual-Saldaña, Heribert, Xavi Masip-Bruin, Adrián Asensio, Albert Alonso, Isabel Blanco. "Personalized Oxygen Dosing System" Encyclopedia, https://encyclopedia.pub/entry/55066 (accessed April 17, 2024).
Pascual-Saldaña, H., Masip-Bruin, X., Asensio, A., Alonso, A., & Blanco, I. (2024, February 15). Personalized Oxygen Dosing System. In Encyclopedia. https://encyclopedia.pub/entry/55066
Pascual-Saldaña, Heribert, et al. "Personalized Oxygen Dosing System." Encyclopedia. Web. 15 February, 2024.
Personalized Oxygen Dosing System
Edit

Considering the prevalence of chronic obstructive pulmonary disease (COPD) and the limitations of traditional long-term oxygen therapy (LTOT) in meeting individual patient needs, a proactive and personalized oxygen dosing system is introduced. This system harnesses AI and edge-to-cloud technologies, distributed across the continuum and Its primary objective is to develop accurate, reliable, and efficient predictive SpO2 AI models for each enrolled patient.

chronic obstructive pulmonary disease COPD artificial intelligence machine learning

1. Introduction

Focusing on chronic respiratory diseases, chronic obstructive pulmonary disease (COPD) is the major contributor to the global burden, with over 3.23 million deaths recorded in 2019, with 80% of these deaths occurring in low- and middle-income countries [1]. The average prevalence of COPD, as reported by European countries to the World Health Organization (WHO) from 2017 to the present, is between 1 and 2% and is expected to increase significantly by 2050 [2]. For example, it is forecasted that the prevalence of it will reach up to 13.8%, with a total of 4.8 million patients in Spain and up to 13.1% in United Kingdom. These predictions indicate a concerning trend of COPD becoming a major public health issue in the coming decades, highlighting the importance of continued research and development of effective interventions and treatments for this condition.
Long-term oxygen therapy (LTOT) is a common and well accepted treatment for patients with COPD and, in general, for patients with any other respiratory disease [3][4][5] tha strongly impacts patients’ capacity to breath [6][7]. Consequently, the main rationale behind LTOT is to improve this patients’ capacity to breath, thereby reducing breathing difficulties. To this end, patients are demanded to carry an oxygen concentrator, which is responsible for providing patients with the theoretically needed supplementary amount of oxygen. In order to measure these breathing difficulties, clinicians consider blood capillary peripheral oxygen saturation (SpO2), which is the indicator used to assess the need for oxygen and conceptually stands for the fraction of oxygen-saturated hemoglobin relative to the total hemoglobin [7]. The supplementary oxygen flow rate prescribed to patients by clinicians is fixed and static and therefore, with a high level of probability, will not always match real patients’ oxygen needs [8]. This static dose is decided by clinicians applying a rule-based approach [7] that is based on the SpO2 values obtained from patients running some well-known exercise tests (the most usual being the 6-min walking test, 6MWT). These tests are conducted periodically (e.g., twice per year) by patients in hospitals to adjust or modify their oxygen prescription based on the updated state of the disease [7].
However, the mismatch between the real oxygen needs and the oxygen provided often leads to either hypoxemia during exertion or hyperoxemia when resting. Put briefly, hypoxemia can cause adverse health effects such as dyspnea, muscle fatigue, and decreased exercise tolerance [9]. On the other hand, hyperoxemia, or an excess of oxygen in the blood, can cause health problems such as oxygen toxicity, which can damage the lungs and other organs [10]. Therefore, it would be extremely important to monitor in real time the SpO2 levels in patients receiving LTOT and to dynamically adapt the oxygen flow rate to the real needs of patients in order to ensure that they always receive the appropriate amount of oxygen to avoid the problems mentioned above (see [11] for a more detailed analysis).
To address these challenges, the first approach might aim at designing an intelligent oxygen dosing system to dynamically and reactively deliver the appropriate dose of oxygen at the right time. Researchers go far beyond this first approach and propose to to consider a reactive approach but rather to consider a proactive one that intends to personalize the treatment for each individual patient based on their specific physical conditions and the physical activity to be undertaken [12].
The reactive approach envisioned above is not new. Indeed, recent hospital tests have employed closed-loop oxygen dosing systems with good results [13]. These systems use control strategies based on proportional–integral–derivative, proportional–integral, or rule-based approaches to regulate the oxygen supply to a predefined set point. The oxygen dose is adjusted as a direct function of SpO2, with satisfactory short-term results [14]. However, as expected, changes in oxygen saturation levels are not instantaneous and it takes several seconds for the delivered oxygen dose to affect the SpO2 level [15], thus resulting in patients continuing to experience shortness of breath. Additionally, several tests that researchers have also conducted emphasized some fast and unexpected changes in the observed SpO2 curve, which may turn into errors in the closed-loop decision process, thereby strongly affecting patients’ wellbeing. Aligned to the view on the need for a personalized dosing strategy, Kofod et al. in [16] conducted a study that demonstrated the benefits of dynamic oxygen dosing. The authors concluded that “individualized automated oxygen titration decreases dyspnea during walking and increases walking endurance in patients with COPD on LTOT”.
The proactive approach may efficiently address current limitations in the oxygen dosing system. To this end, by leveraging current tools and techniques available (e.g., edge–cloud continuum and artificial intelligence, AI) the strategy will take advantage of the data generated by patients to create a personalized oxygen dosing system that will proactively adjust the dose to each patient’s specific situation. The system can predict future changes in SpO2 levels based on past experiences and properly adjust the oxygen dose instantaneously to prevent heavy drops or increases in SpO2. This adaptive approach can notably revolutionize the management of oxygen therapy for patients with chronic respiratory diseases, significantly improving patient wellbeing and their quality of life while reducing the burden of disease, extending the life of concentrators and their components, and ultimately benefiting the healthcare system. It is also critical to mention that the proposed proactive approach may be deployed on hospital premises but is specifically designed for ambulatory use, thus clearly impacting on real daily patient activity.

2. Personalized Oxygen Dosing System

In recent years, predictive models have gained significant attention in the respiratory field, with numerous studies conducted to improve the accuracy of predictions. Early contributions in this field date back to 2013 and were written by H. Elmoaqet et al. in [17], and considerable progress has been made to enhance the predictive capabilities of these systems since then.
H. Elmoaqet et al. worked on predictive modeling for many years by applying mathematical methods even though no significant progress was obtained [18][19]. Their last contribution in the area [20] introduced a k-step predictive model utilizing AI for the first time. The authors proposed a framework for multi-step-ahead predictions of critical levels in physiological signals and developed a new performance metric for validation purposes (e.g., prediction accuracy). Results demonstrated a remarkable improvement compared with standard autoregressive models.
Another recent study (Sam Ghazal et al. [21]) proposed the use of different machine learning (ML) techniques to predict five SpO2 levels toward a proactive mechanical ventilator setting adjustment in an intensive care unit (ICU). The authors used an artificial neural network (NN) based on the back-propagation method along with a Bootstrap aggregation of complex decision tree implementation as the classifier. While the average results appeared promising, precision for medium and high severity events, which are the most important ones, was low, thereby highlighting the need for having better data quality and quantity to achieve acceptable results from predictive approaches.
It is also worth mentioning that whatever predictive technology may be applied, the IT infrastructure should be able to properly accommodate the specific set of computation and storage requirements. The fog-to-cloud paradigm [22] (today also referred to as edge-to-cloud or more generically as cloud continuum) was introduced as a baseline technology for predictive systems. Xavi Masip et al. applied this paradigm to the health field and more particularly to the LTOT arena by utilizing patients’ contexts, historical data, and biological signals to proactively predict their oxygen dose needs in an optimal infrastructure utilization paradigm, which was centered on computing the results through edge devices [23]. This approach alleviates network load and distributes computing resources in a more efficient manner, demonstrating the feasibility of running AI models in edge devices using currently available technologies.
More recently, H. Pascual et al. [24] provided an initial analysis aimed at predicting blood oxygen saturation by utilizing patients’ vital signs. To this end, a small size test with four patients was considered. The main objective was to assess the applicability of the same proposed AI architecture, which was trained with the same algorithm across diverse individuals, and analyze the obtained results. After analyzing the collected data, the authors concluded that, as expected for the sake of optimality, the same AI model architecture is not universally applicable to all patients.
In the commercial arena, some products in the oxygen flow rate regulation field may be found, such as the O2 Flow Regulator by Dima Italia™ [25] and Sanso Via™ by Sanso Health [26]. However, although these solutions focus on regulating patients’ O2 flows, both of them are completely reactive (closed-loop) and only available on hospital premises. Another device on the market is iGo2 [27], an oxygen concentrator designed to slightly adjust the oxygen dose to the patient’s activity through a rule-based system monitoring the respiratory rate. There is limited research available specifically addressing the challenges of personalized modeling and edge-computed SpO2 predictions in the respiratory disorders area.

References

  1. Chronic Obstructive Pulmonary Disease (COPD). Available online: https://www.who.int/news-room/fact-sheets/detail/chronic-obstructive-pulmonary-disease-(copd) (accessed on 16 June 2023).
  2. Benjafield, A.; Tellez, D.; Barrett, M.; Gondalia, R.; Nunez, C.; Wedzicha, J.; Malhotra, A. An estimate of the European prevalence of COPD in 2050. Eur. Respir. J. 2021, 58 (Suppl. S65), OA2866.
  3. Stewart, A.G.; Howard, P. Indications for Long-Term Oxygen Therapy. Respiration 1992, 59 (Suppl. S2), 8–13.
  4. Bellone, A.; Monari, A.; Cortellaro, F.; Vettorello, M.; Arlati, S.; Coen, D. Myocardial infarction rate in acute pulmonary edema: Noninvasive pressure support ventilation versus continuous positive airway pressure. Crit. Care Med. 2004, 32, 1860–1865.
  5. Capsoni, N.; Privitera, D.; Airoldi, C.; Gheda, S.; Mazzone, A.; Terranova, G.; Galbiati, F.; Bellone, A. Evaluation of PaCO2 trend in COVID-19 patients undergoing helmet CPAP in the emergency department. Emerg. Care J. 2023, 19.
  6. Sami, R.; Savari, M.A.; Mansourian, M.; Ghazavi, R.; Meamar, R. Effect of Long-Term Oxygen Therapy on Reducing Rehospitalization of Patients with Chronic Obstructive Pulmonary Disease: A Systematic Review and Meta-Analysis. Pulm. Ther. 2023, 9, 255–270.
  7. O’driscoll, B.R.; Howard, L.S.; Earis, J.; Mak, V. BTS guideline for oxygen use in adults in healthcare and emergency settings. Thorax 2017, 72 (Suppl. S1), ii1–ii90.
  8. Galera, R.; Casitas, R.; Martínez, E.; Lores, V.; Rojo, B.; Carpio, C.; Llontop, C.; García-Río, F. Exercise oxygen flow titration methods in COPD patients with respiratory failure. Respir. Med. 2012, 106, 1544–1550.
  9. Bhutta, B.S.; Alghoula, F.; Berim, I. Hypoxia; StatPearls: Treasure Island, FL, USA, 2022. Available online: https://www.ncbi.nlm.nih.gov/books/NBK482316/ (accessed on 19 January 2024).
  10. Singer, M.; Young, P.J.; Laffey, J.G.; Asfar, P.; Taccone, F.S.; Skrifvars, M.B.; Meyhoff, C.S.; Radermacher, P. Dangers of hyperoxia. Crit. Care 2021, 25, 440.
  11. Branson, R.D.; Robinson, B.R.H.; Branson, R.D.; Robinson, B.R.H. Oxygen: When is more the enemy of good? Intensive Care Med. 2010, 37, 1–3.
  12. Mayoralas-Alises, S.; Carratalá, J.M.; Díaz-Lobato, S. New Perspectives in Oxygen Therapy Titration: Is Automatic Titration the Future? Arch. Bronconeumol. 2019, 55, 319–327.
  13. L’Her, E.; Dias, P.; Gouillou, M.; Riou, A.; Souquiere, L.; Paleiron, N.; Archambault, P.; Bouchard, P.A.; Lellouche, F. Automatic versus manual oxygen administration in the emergency department. Eur. Respir. J. 2017, 50, 1602552.
  14. Branson, R.D. Oxygen therapy in copd. Respir. Care 2018, 63, 734–748.
  15. Gruber, P.; Kwiatkowski, T.; Silverrnan, R.; Flaster, E.; Auerbach, C. Time to equilibration of oxygen saturation using pulse oximetry. Acad. Emerg. Med. 1995, 2, 810–815.
  16. Kofod, L.M.; Westerdahl, E.; Kristensen, M.T.; Brocki, B.C.; Ringbaek, T.; Hansen, F. Clinical Medicine Effect of Automated Oxygen Titration during Walking on Dyspnea and Endurance in Chronic Hypoxemic Patients with COPD: A Randomized Crossover Trial. J. Clin. Med. 2021, 10, 4820.
  17. Elmoaqet, H.; Tilbury, D.M.; Ramachandran, S.K. Predicting oxygen saturation levels in blood using autoregressive models: A threshold metric for evaluating predictive models. In Proceedings of the American Control Conference, Washington, DC, USA, 17–19 June 2013; pp. 734–739.
  18. Elmoaqet, H.; Tilbury, D.M.; Ramachandran, S.K. A novel dynamic model to predict abnormal oxygen desaturations in blood. In Proceedings of the IEEE MeMeA 2014—IEEE International Symposium on Medical Measurements and Applications, Lisboa, Portugal, 11–12 June 2014.
  19. Elmoaqet, H.; Tilbury, D.M.; Ramachandran, S.K. Evaluating predictions of critical oxygen desaturation events. Physiol. Meas. 2014, 35, 639–655.
  20. ElMoaqet, H.; Tilbury, D.M.; Ramachandran, S.K. Multi-Step Ahead Predictions for Critical Levels in Physiological Time Series. IEEE Trans. Cybern. 2016, 46, 1704–1714.
  21. Ghazal, S.; Sauthier, M.; Brossier, D.; Bouachir, W.; Jouvet, P.A.; Noumeir, R. Using machine learning models to predict oxygen saturation following ventilator support adjustment in critically ill children: A single center pilot study. PLoS ONE 2019, 14, e0198921.
  22. Masip-Bruin, X.; Marin-Tordera, E.; Gomez, A.; Barbosa, V.; Alonso, A. Will it be cloud or will it be fog? F2C, A novel flagship computing paradigm for highly demanding services. In Proceedings of the FTC 2016—Proceedings of Future Technologies Conference, San Francisco, CA, USA, 6–7 December 2016; pp. 1129–1136.
  23. Masip-Bruin, X.; Marin-Tordera, E.; Alonso, A.; Garcia, J. Fog-to-cloud Computing (F2C): The key technology enabler for dependable e-health services deployment. In Proceedings of the 2016 Mediterranean Ad Hoc Networking Workshop, Med-Hoc-Net 2016—15th IFIP MEDHOCNET 2016, Vilanova i la Geltru, Spain, 20–22 June 2016.
  24. Pascual, H.; Masip-Bruin, X.; Alonso, A.; Blanco, I. Analyzing Distinct Neural Network Models for Oxygen Saturation Prediction Towards a Personalized COPD Management. In Proceedings of the Proceedings 2023 IEEE 19th International Conference on e-Science, e-Science 2023, Limassol, Cyprus, 9–13 October 2023.
  25. Cirio, F.T.S.; Nava, M.D.S. Pilot study of a new device to titrate oxygen flow in hypoxic patients on long-term oxygen therapy. Respir. Care 2011, 56, 429–434.
  26. Sanso Via System. Available online: https://www.sansohealth.com/our-solution.html (accessed on 13 February 2023).
  27. iGo2|Oxygen Therapy|Respiratory|Products|Drive Devilbiss International. Available online: https://www.drivedevilbiss-int.com/products/respiratory/oxygen-therapy/212/igo2 (accessed on 17 December 2023).
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