Key Points
- MIT and MGH developed statistical models to predict unconscious pain (nociception) during surgery.
- The models use physiological data like heart rate and skin conductance to improve drug dosing.
- The best models included drug information and outperformed existing systems.
- Future work aims to make the models usable in real time. The study could lead to automated systems for pain control in clinical settings.
A new study from the Massachusetts Institute of Technology (MIT) and Massachusetts General Hospital (MGH) has introduced statistical models aimed at improving how pain, or “nociception,” is managed during surgery. Led by Dr. Sandya Subramanian and published in the Proceedings of the National Academy of Sciences, the research offers a way for anesthesiologists to better predict and control unconscious pain during surgery. This breakthrough could reduce post-operative pain and minimize drug side effects for patients.
The study collected data from 101 abdominal surgeries at MGH, totaling 18,582 minutes of patient monitoring. Researchers used five physiological sensors to record nearly 50,000 nociceptive stimuli, such as incisions. These data, combined with the types and doses of drugs administered, were analyzed to create models that could predict the body’s response to pain stimuli.
One of the key challenges in anesthesia is balancing the dosage of pain control drugs. Administering too much can lead to side effects like nausea or delirium, while too little can cause excessive pain post-surgery. This research aims to offer anesthesiologists more precise, data-driven tools to optimize dosing during surgery, as current methods rely heavily on subjective judgment and experience.
Subramanian and her team built models that track physiological signals such as heart rate, skin conductance, and respiration to measure nociception. By analyzing these signals, they developed indices that predict when a patient is experiencing pain, even while unconscious. The study found that the best-performing models incorporated drug information and used a “random forest” approach, outperforming existing models like ANI, which only tracks heart rate.
The research also showed promise for unsupervised models, which did not rely on predetermined data but still managed to detect nociception with reasonable accuracy. This finding suggests that nociceptive responses are objectively detectable across different patients.
The next steps in the research include refining the models for real-time use in operating rooms, enabling anesthesiologists to adjust pain management more precisely. In the future, the models could be integrated into automated systems that adjust drug doses under the supervision of healthcare professionals.
