Predicting Pain Patient Response


Rosemary Frei, MSc
Clinical Pain Medicine
ISSUE: JUNE 2014 | VOLUME: 12(6)

Predicting Pain Patient Response to Opioid Detoxification

Las Vegas—A series of analyses suggests it is possible to accurately predict which chronic pain patients will respond to a medical detoxification program.

The investigators administered the Nutrition, Emotional, Social and Physical (NESP) detoxification treatment program to 148 individuals who had been diagnosed with a workplace injury. Of these individuals, 122 were successfully weaned off opioids. The investigators also genotyped the patients with the Proove Narcotic Risk Genetic Profile test and—using an algorithm they also developed—found the genotyping had a 97.2% positive predictive value in pinpointing which patients would be successfully detoxified. The negative predictive value was 56.1%.

“In addition, we detected a potential economic benefit from genotyping the patients, on top of showing a benefit to both patients and physicians by being able to show which patients are the best candidates for detoxification,” said Brian Meshkin, lead investigator and founder and CEO of Proove BioSciences, Irvine, Calif.

Pain Medicine News asked another of the very small number of experts in this area to comment on the study. Sanford Silverman, MD, president of the Florida Society of Interventional Pain Physicians, is on the Proove Medical Advisory Board.

“It’s got tremendous potential to add to the accuracy of predictive testing we use on pain patients,” Dr. Silverman said. “But it’s a small study and we need to know more about the predictors of failure; for example, did the patients who failed detoxification have a high rate of psychiatric comorbidities?”

The patients were being treated at pain medicine clinics operated by Comprehensive Pain Relief Group in Los Angeles and Fresno, Calif., between May 2009 and May 2011. The patients were all workers’ compensation claimants, were on chronic opioid pain medication therapy and had been diagnosed with comorbid chronic pain syndrome.

The patients were genotyped before participating in the NESP program for 90 days. Treatment included daily doses of 4 to 8 mg buprenorphine/naloxone (Suboxone, Reckitt Benckiser) and 10 mg prochlorperazine, as well as supportive therapy, followed by a 24-month maintenance program.

The average age of participants was 39 years for female patients (n=58, 39%) and 44 years for male patients (n=90, 61%). The average minimum number of pain and psychiatric diagnoses in the entire group was three; the most common diagnoses were chronic pain syndrome and low back pain with lumbar radiculopathy.

There was a 98.5% success rate (146 of 148) at 90 days, with success defined as a 50% reduction in pain scores on a visual analog scale, 50% reduction in the pain of activities of daily living and complete weaning from prescription pain opioids. The two-year success rate was 81.8% (122 of 148). Most of the relapses to narcotics (23 of 26) were the result of development of another medical condition during the program.

The mean cost of the genetic testing plus the mean annual cost of the NESP intervention was $16,628 per patient.

Mr. Meshkin and his colleagues estimated cost savings of this approach for the 148 patients in the study at $2.04 million in the first year and $4.08 million in year 2. This was based on the cost of the interventions in the study and on an earlier publication on the cost of opioid abusers versus non-abusers (J Manag Care Pharm 2005;11:469-479).

The investigators also developed a system that confers a dependence risk score on each patient based on his or her genotyping results, as well as a cutoff score that determines whether the NESP program is likely to suceed or fail for that patient. They determined that this prediction model has a positive predictive value of 97.2%, a negative predictive value of 56.1%, a sensitivity of 85.2% and a specificity of 78.8%.

—Rosemary Frei, MSc


Mr. Meshkin is the founder and CEO of Proove BioSciences. Dr. Silverman is on Proove’s Medical Advisory Board.