Cincinnati Children's Hospital Medical Center

Senthilkumar Sadhasivam

Vidya Chidambaran

Tsuyoshi Fukuda

Kejian Zhang

John McAuliffe

Cynthia A Prows

Jaroslaw Meller

Hong Ji

Lisa J Martin

Lisa Martin

Jarolsaw Meller

The disclosure relates to methods for pain management in the perioperative context, particularly through the use of the DNA methylation status of the human OPRM1 gene as a biomarker for increased susceptibility to perioperative pain, and related methods and compositions.

Methods and compositions relating to OPRM1 DNA methylation for personalized pain management

Methods and compositions disclosed herein generally relate to methods of improving clinical and economic outcomes to address adverse effects related to anesthesia, analgesics, opioids, and inadequate pain relief. Embodiments of the invention relate to the association between genes, specific polymorphisms of genes, and non-genetic factors with inadequate pain relief and anesthesia-, analgesic, and/or opioid-related adverse effects. Embodiments of the invention can be used to determine and manage patient risk factors for development of adverse perioperative effects and can allow for personalized anesthesia and pain management for improvement of pain control and reduction of anesthesia-, analgesic-, and opioid-related adverse outcomes. These methods and compositions apply to non-surgical pain management with opioids. Therefore, patients who are genetically predisposed to risk of inadequate pain relief and/or serious side effects from anesthesia, analgesics, and/or opioids can be identified and individualized treatment plans developed for implementation by the clinician to improve clinical and economic outcomes.

Personalized pain management and anesthesia: preemptive risk identification and therapeutic decision support

The invention provides methods, kits and systems related to personalized pain management.

Methods and compositions for personalized pain management

A non-invasive bedside tool for monitoring central nervous system effects of opioids. The tool illustratively comprises a quantitative pupillometry system for predicting a probability of at least one opioid-related central effect based on detected pupillary effects.

Quantitative pupillometry as a bedside predictor of postoperative respiratory depression

Growing community of inventors

Mason, OH, United States of America

Senthilkumar Sadhasivam