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MDoloris Research and its vision for the future of pain monitoring

MDoloris Technology's ANS monitoring has the potential to revolutionise pain management, patient comfort and personalised care in surgical, intensive care and palliative care settings. As research continues and technology evolves, we can expect more sophisticated devices. That is why we are constantly working to improve the accuracy of our algorithms, as well as on new versions of our devices and seamless integration with existing medical systems.

MDoloris is a French MedTech company founded in 2010 thanks to a public-private partnership based on the work of Pr. Regis Logier, Mathieu Jeanne and Julien De Jonckheere and Michel Delecroix (Unit 807 INSERM - CIC-IT of the CHRU of Lille. Since the first spin-off, the research has enabled to file numerous patents.

The innovation, the scientific and medical breakthrough of MDoloris consist in finding, evaluating and validating a new biological measurement, a new physiological data representing the parasympathetic tone of the human body. This new reference mark is obviously of great importance in certain specific circumstances, in particular in the objective assessment of pain and the well-being / comfort of the patient, whether conscious or unconscious.

Beyond anaesthesiology (human or animal), many applications remain possible. Let us take the example of intensive care monitoring. 2 Studies carried out during COVID show that the MDoloris technology, by objectifying and facilitating the maintenance of parasympathetic tone within "normal" values, allows better pharmacological titration and reduces risks:

  • respiratory depression (overdose)
  • inflammatory cascade (underdose)

whatever the pharmacological nature of the analgesic effect.

Suffice it to say that this patented and proprietary technology is based on solid scientific and clinical foundations and offers many prospects for the future:

The first development is no less than one of the future of anaesthesia and represent a potential turning point in the evolution of MDoloris

MDoloris, in partnership with the University Hospital of Lille, is the first company to have succeeded in integrating an autonomous loop (close-loop system) with its technology. The MDoloris technology will now benefit from physiological and "automatic" feedback for the control/optimisation of the tone of the neuro-vegetative system. In other words, the realisation of this loop, already clinically validated with the University Hospital of Lille and soon to be published, represents a new step in anaesthesiology, offering greater safety, control and comfort to the patient, while freeing up the anaesthetist's time to monitor other vital parameters.

Patented and registered e, these biomechanical innovations have demonstrated their full clinical potential in a clinical study, to be published shortly.

But many other fields are of interest:  Let's explore how MDoloris technology and autonomic nervous system (ANS) monitoring could develop in the coming years in different medical contexts.

1. Anaesthesia during surgery:

Closed-loop control of nociception:

MDoloris has already made significant progress in this area. Our pioneering approach, known as "closed-loop perioperative nociception control", uses real-time ANS monitoring to adjust analgesia levels based on the patient's comfort state (publication submitted) and is the cornerstone for further developments such as:

  1. Additional closed-loop vasopressor systems
  2. Predictive algorithms: In the coming years, we can expect further advances in predictive algorithms by collecting registered data and using artificial intelligence to further improve the clinical impact of autonomous nervous system monitoring. These algorithms will continuously analyse ANS responses, allowing anaesthesiologists to anticipate pain levels (today they are able to monitor pain in a continuous mode with MDoloris technology) and tailor anaesthetic doses even more precisely.

Integration with Surgical Robots:

As surgical robots become more commonplace, integrating MDoloris technology and artificial intelligence directly into these systems will enhance patient safety. Real-time feedback of ANS parameters combined with robotic instruments will ensure optimal anaesthesia levels throughout the procedure. 1

2. Intensive care patient comfort:

Early detection of discomfort:

ANS monitoring can play a critical role in intensive care units (ICUs). By continuously assessing comfort levels, it can detect subtle signs of discomfort or distress, even in non-communicative patients. MDoloris' existing ANI (Analgesia Nociception Index) technology could be further developed to provide a more comprehensive view of a patient's overall wellbeing. Data collection coupled with artificial intelligence could further improve understanding of ICU patients and help predict clinical outcomes. 2

Personalised sedation management:

As ANS monitoring can guide personalised sedation protocols, we can expect further development towards a more individualised approach, adjusting sedative doses based on individual ANS responses. Sedation is a necessary part of treatment for ICU patients to reduce stress and anxiety and improve long-term prognosis, particularly in sepsis and severe burn patients. This approach would minimise oversedation, reduce the risk of complications and improve patient outcomes. 3

Prediction of mortality risk:

As demonstrated by 2 peer-reviewed publications during the Covid crisis, the Mdoloris technology can predict the survival potential of patients suffering from severe infections (ARDS, sepsis, Covid) thanks to a precise analysis of the patients' ANS. Based on these results, we are developing new algorithms to improve the accuracy of such a useful new index for intensivists.

3. Monitoring comfort in palliative and Alzheimer's care:

Objective assessment of comfort:

Palliative care and Alzheimer's patients often struggle with communication. ANS monitoring provides an objective way to assess their comfort level. By tracking ANI or other relevant parameters, caregivers can tailor interventions to improve patient comfort.

Behavioural insights:

ANS monitoring could complement behavioural assessments. In Alzheimer's patients, changes in ANS responses may correlate with pain, anxiety or other discomfort. Integrating this data with behavioural observations could lead to more effective care plans.

Wearable devices:

In the next few years, we may see specific wearable ANS monitoring devices designed for palliative and/or Alzheimer's care. These devices could continuously track comfort levels, alert caregivers to distress and prompt timely intervention. 4

Integration with telemedicine:

As telemedicine becomes more prevalent, ANS monitoring devices could be remotely connected to monitoring systems. This would allow healthcare providers to assess patient comfort in real time, even from a distance, and make timely adjustments. 5,6

  1. Agosto, A. (2024). Surgical robotics: Leveraging hardware and AI to improve patient care. Global X.
  2. Knudsen, J. E., Ghaffar, U., Ma, R., & Hung, A. J. (2024). Clinical applications of artificial intelligence in robotic surgery. *Springer*.
  3. Kobayashi, N., Shiga, T., Ikumi, S., Watanabe, K., Murakami, H., & Yamauchi, M. (2021). Semi-automated pain tracking in intensive care patients using artificial intelligence: A retrospective observational study. Scientific Reports.
  4. Marcos-Vidal, J. M., González, R., Merino, M., Higuera, E., & García, C. (2023). Sedation in patients with sepsis: towards a personalised approach. *Journal of Personalised Medicine*.
  5. Bauschert, L., & Prod'homme, C. (2021). Assessing comfort at the end of life, is the clinic sufficient? A retrospective cohort study of combined comfort assessment with analgesia/nociception index and clinic in non-communicative patients. Journal of Palliative Care, 39(2).
  6. Turcu, A.-M., Ilie, A. C., Ștefăniu, R., Țăranu, S. M., Sandu, I. A., Alexa-Stratulat, T., Pîslaru, A. I., & Alexa, I. D. (2023). The impact of heart rate variability monitoring on the prevention of major cardiovascular events. Diagnostics.
  7. Sharma, S., Rawal, R., & Shah, D. (2023). Overcoming the challenges of AI-based telemedicine: Best practices and lessons learned. *Journal of Education and Health Promotion.