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Assessing the impact of battery charging and discharging times on the availability of mechanical ventilation service

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Abstract

This paper evaluates the impact of battery charging and discharging times on the availability of mechanical respirators in the Intensive Care Unit (ICU). The availability of these life-saving devices is crucial for ensuring optimal patient care in critical situations. This study aims to assess how the duration of battery charging and discharging cycles affects the availability of mechanical respirators and explore potential strategies to optimize their maintainability. We analyze the system’s behavior in eight scenarios that consider changes to optimize repair times, battery charge and discharge times, and power system redundancy. The results showed 98% improvements in availability and reduced system downtime. The outcomes of this research contribute to understanding the critical factors impacting the availability of mechanical respirators in the ICU. By addressing the issues related to battery charging and discharging times and maintaining these devices, healthcare facilities can enhance the availability and reliability of respiratory support systems. Ultimately, this study aims to improve patient outcomes and promote efficient resource utilization in the ICU setting.

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References

  1. Ramírez M, Navarro S, Clavería C, Molina Y, Cox A, Ramírez M, Navarro S, Clavería C, Molina Y, Cox A (2018) Parental stressors in a pediatric intensive care unit. Revista chilena de pediatria 89(2):182–189

    Article  Google Scholar 

  2. Abate SM, Ahmed Ali S, Mantfardo B, Basu B (2020) Rate of intensive care unit admission and outcomes among patients with coronavirus: A systematic review and meta-analysis. PloS one 15(7):0235653

    Article  Google Scholar 

  3. Keszler M (2017) Mechanical ventilation strategies. In: Seminars in Fetal and Neonatal Medicine, vol. 22,267–274. Elsevier

  4. Zuñiga QGP, Dreyer E, Colombrini M, Nishimura M, Pato N (2004) Ventilação mecânica básica para enfermagem. Atheneu, São Paulo

    Google Scholar 

  5. Chang R, Elhusseiny KM, Yeh Y-C, Sun W-Z (2021) Covid-19 icu and mechanical ventilation patient characteristics and outcomes-a systematic review and meta-analysis. PloS one 16(2):0246318

    Article  Google Scholar 

  6. Coffey CC, Campbell DL, Zhuang Z (1999) Simulated workplace performance of n95 respirators. Am Ind Hygiene Assoc J 60(5):618–624

    Article  Google Scholar 

  7. Savary D, Lesimple A, Beloncle F, Morin F, Templier F, Broc A, Brochard L, Richard J-C, Mercat A (2020) Reliability and limits of transport-ventilators to safely ventilate severe patients in special surge situations. Ann Intensive Care 10:1–10

    Article  Google Scholar 

  8. Blakeman TC, Robinson BR, Branson RD (2010) Battery performance of 4 intensive care ventilator models. Respiratory Care 55(3):317–321

    Google Scholar 

  9. Karaböce B (2018) Inspection and testing of respirators and anaesthesia machines. Inspection of Medical Devices: For Regulatory Purposes, 181–201

  10. Araujo MSd, et al (2020) Análise de confiabilidade de monitores multiparamétricos utilizados em unidades de terapia intensiva

  11. Sandelic M, Sangwongwanich A, Blaabjerg F (2019) Reliability evaluation of pv systems with integrated battery energy storage systems: Dc-coupled and ac-coupled configurations. Electronics 8(9):1059

    Article  Google Scholar 

  12. Nguyen TA, Min D, Choi E, Lee J-W (2021) Dependability and security quantification of an internet of medical things infrastructure based on cloud-fog-edge continuum for healthcare monitoring using hierarchical models. IEEE Internet Things J 8(21):15704–15748

    Article  Google Scholar 

  13. Maciel PRM Performance, Reliability, and Availability Evaluation of Computational Systems, Volume 1: Performance and Background. Chapman and Hall/CRC

  14. Maciel PRM Performance, Reliability, and Availability Evaluation of Computational Systems, Volume 2: Reliability, Availability Modeling, Measuring, and Data Analysis. Chapman and Hall/CRC

  15. Avizienis A, Laprie J-C (1986) Dependable computing: From concepts to design diversity. Proc IEEE 74(5):629–638

    Article  Google Scholar 

  16. Gray J, Siewiorek DP (1991) High-availability computer systems. Computer 24(9):39–48

    Article  Google Scholar 

  17. Wang D, Trivedi KS (2005) Computing steady-state mean time to failure for non-coherent repairable systems. IEEE Trans Reliab 54(3):506–516

    Article  Google Scholar 

  18. Smith R, Trivedi KS, Ramesh A (1988) Performability analysis: measures, an algorithm, and a case study. IEEE Trans Comput 37(4):406–417

    Article  Google Scholar 

  19. Trivedi KS, Malhotra M (1993) Reliability and performability techniques and tools: A survey. In: Messung, Modellierung und Bewertung Von Rechen-und Kommunikationssystemen: 7. ITG/GI-Fachtagung, Aachen, 21.–23. September 1993, 27–48. Springer

  20. Knight J (2012) Fundamentals of Dependable Computing for Software Engineers. CRC Press

  21. Koren I, Krishna CM (2020) Fault-tolerant Systems. Morgan Kaufmann

  22. Dodson B, Nolan D (1999) Reliability Engineering Handbook. Marcel Dekker New York

  23. Symons FJW (1989) Modelling and analysis of communication protocols using numerical petri nets

  24. Natkin S (1980) Les reseaux de petri stochastiques et leur application a l’evaluation des systém informatiques

  25. Molloy MK (1981) On the integration of delay and throughput measures in distributed processing models. AAI8201138

  26. Ajmone Marsan M, Conte G, Balbo G (1984) A class of generalized stochastic petri nets for the performance evaluation of multiprocessor systems. ACM Trans Comput Syst 2(2):93–122. https://doi.org/10.1145/190.191

    Article  Google Scholar 

  27. Marsan MA, Balbo G, Conte G, Donatelli S, Franceschinis G (1994) Modelling with generalized stochastic petri nets, 1st edn. John Wiley & Sons Inc, USA

    MATH  Google Scholar 

  28. Marsan MA, Chiola G (1986) On petri nets with deterministic and exponentially distributed firing times. Advances in Petri Nets 1987. Covers the 7th European Workshop on Applications and Theory of Petri Nets. Springer, Berlin, Heidelberg, pp 132–145

  29. Lindemann C (1998) Performance Modelling with Deterministic and Stochastic Petri Nets. John Wiley & Sons, Inc.

  30. German R (2000) Performance Analysis of Communication Systems with Non-Markovian Stochastic Petri Nets. John Wiley & Sons, Inc., Inc. New York, NY, USA

  31. Muppala J, Ciardo G, Trivedi KS (1994) Stochastic reward nets for reliability prediction. Commun Reliab Maintain Serviceability 1(2):9–20

    Google Scholar 

  32. Hamby DM (1994) A review of techniques for parameter sensitivity analysis of environmental models. Environ Monit Assess 32:135–154

    Article  Google Scholar 

  33. Barrett M, Smith M, Elixhauser A, Honigman L, Pines J (2011) Utilization of intensive care services. Healthcare Cost and Utilization Project

  34. Papadakos PJ, Lachmann B (2007) The open lung concept of mechanical ventilation: the role of recruitment and stabilization. Critical Care Clin 23(2):241–250

    Article  Google Scholar 

  35. Brochard L, Slutsky A, Pesenti A (2017) Mechanical ventilation to minimize progression of lung injury in acute respiratory failure. Am J Respiratory Crit Care Med 195(4):438–442

    Article  Google Scholar 

  36. Pham T, Brochard LJ, Slutsky AS (2017) Mechanical ventilation: state of the art. In: Mayo Clinic Proceedings, v92,1382–1400. Elsevier

  37. Azoulay É, Kouatchet A, Jaber S, Lambert J, Meziani F, Schmidt M, Schnell D, Mortaza S, Conseil M, Tchenio X (2013) Noninvasive mechanical ventilation in patients having declined tracheal intubation. Intensive Care Med 39:292–301

    Article  Google Scholar 

  38. Bates JH, Smith BJ (2018) Ventilator-induced lung injury and lung mechanics. Ann Trans Med 6(19)

  39. Glenski TA, Diehl C, Clopton RG, Friesen RH (2017) Breathing circuit compliance and accuracy of displayed tidal volume during pressure-controlled ventilation of infants: a quality improvement project. Pediatric Anesthesia 27(9):935–941

    Article  Google Scholar 

  40. Ahmed RA, Boyer TJ (2019) Endotracheal tube

  41. Lee JJ, Choi GJ, Lee WJ, Choi SB, Kang H (2022) Effect of active airway warming with a heated-humidified breathing circuit on core body temperature in patients under general anesthesia: a systematic review and meta-analysis with trial sequential analysis. Korean J Anesthesiol

  42. Bertoni M, Spadaro S, Goligher EC (2020) Monitoring patient respiratory effort during mechanical ventilation: lung and diaphragm-protective ventilation. Ann Update Intensive Care Emergency Med 2020:21–35

    Article  Google Scholar 

  43. Srinivasan S, Ramadi KB, Vicario F, Gwynne D, Hayward A, Langer R, Frassica JJ, Baron RM, Traverso G (2020) Individualized system for augmenting ventilator efficacy (isave): a rapidly deployable system to expand ventilator capacity. BioRxiv, 2020–03

  44. Ng QA, Chiew YS, Wang X, Tan CP, Nor MBM, Damanhuri NS, Chase JG (2021) Network data acquisition and monitoring system for intensive care mechanical ventilation treatment. IEEE Access 9:91859–91873

    Article  Google Scholar 

  45. Lee AS (1989) A scientific methodology for mis case studies. MIS quarterly, 33–50

  46. Melo C, Dantas J, Pereira P, Maciel P (2021) Distributed application provisioning over ethereum-based private and permissioned blockchain: availability modeling, capacity, and costs planning. J Supercomput 77(9):9615–9641

    Article  Google Scholar 

  47. Azaron A, Katagiri H, Kato K, Sakawa M (2006) Reliability evaluation of multi-component cold-standby redundant systems. Appl Math Comput 173(1):137–149

    MathSciNet  MATH  Google Scholar 

  48. Jasper A (2022) Servos Users manual. Available in: https://www.academia.edu/41870952/Manual_Operaç~ao_Servo_S?email_work_card=view-paper. Accessed Oct 10 2022

  49. Maciel P, Matos R, Silva B, Figueiredo J, Oliveira D, Fé I, Maciel R, Dantas J (2017) Mercury: Performance and dependability evaluation of systems with exponential, expolynomial, and general distributions. In: 2017 IEEE 22nd Pacific Rim International Symposium on Dependable Computing (PRDC), 50–57. IEEE

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Authors and Affiliations

  1. Centro de Informática, Universidade Federal de Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Recife, 50.740-560, Pernamuco, Brazil

    Aline do Monte, Pablo Pessoa, Daliton Silva, Luan Lins & Paulo Maciel

  2. Universidade Federal do Agreste de Pernambuco, Av. Bom Pastor, s/n, Garanhuns, 55292-270, Pernambuco, Brazil

    Dimas Cassimiro Nascimento

Authors
  1. Aline do Monte
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  2. Pablo Pessoa
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  5. Dimas Cassimiro Nascimento
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  6. Paulo Maciel
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Contributions

AM wrote the main text of the manuscript. DS contributed to the study design. AM and PM built the SPN model. PP participated in the construction and development of figures and the construction of the text. LL participated in the construction and development of the tables and model working. DCN participated in the construction of the text and translation into English. Both authors performed the revision of the manuscript.

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Correspondence to Aline do Monte.

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The authors have no relevant financial or non-financial interests to disclose. The authors have no conflicts of interest to declare relevant to this article’s content. All the authors certify that they have no affiliations with or involvement in any organization or entity with any financial or non-financial interest in the subject or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.

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Monte, A.d., Pessoa, P., Silva, D. et al. Assessing the impact of battery charging and discharging times on the availability of mechanical ventilation service. J Reliable Intell Environ (2023). https://doi.org/10.1007/s40860-023-00213-9

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