Balancing Act: Opioid Prescribing and Addiction Risk

Opioid Risk: Can a New Algorithm Predict Addiction?

Mira Elwood in Health & Wellness December 2025 • 4 min read.

"A groundbreaking study explores how a predictive algorithm could revolutionize opioid prescribing and addiction prevention in primary care."

Chronic pain is a major public health issue, with many patients relying on opioid prescriptions to manage their pain. However, the use of opioids carries a significant risk of developing opioid use disorder (OUD), making it crucial for healthcare providers to carefully assess and mitigate this risk.

Primary care physicians are often the first point of contact for patients seeking pain relief, placing them at the forefront of opioid prescribing. Balancing effective pain management with the need to prevent OUD presents a significant challenge, especially when resources and time are limited.

A recent study published in Health Services Research and Managerial Epidemiology investigates the effectiveness of a predictive algorithm in identifying patients at high risk of OUD. This algorithm incorporates both phenotypic (observable traits) and genotypic (genetic) risk factors to provide a more comprehensive risk assessment.

How Does the Predictive Algorithm Work?

Balancing Act: Opioid Prescribing and Addiction Risk

The study, led by Maneesh Sharma and colleagues, sought to validate an algorithm that predicts aberrant behavior related to opioid use. This "profile" algorithm uniquely combines phenotypic and genotypic risk factors, offering a comprehensive scoring system to stratify patients according to their risk of developing OUD.

Researchers conducted a validation study involving 452 participants diagnosed with OUD and 1237 controls. The algorithm's ability to correctly categorize patients was then assessed, with a particular focus on sensitivity (the ability to correctly identify those with OUD) and specificity (the ability to correctly identify those without OUD).

Phenotypic Factors: These include observable traits and risk factors such as a personal history of alcoholism, illegal drug use, prescription drug misuse, mental health disorders, and age between 16 and 45.
Genotypic Factors: The algorithm incorporates 11 different single nucleotide polymorphisms (SNPs) that have been linked to opioid abuse, misuse, dependence, or addiction. These SNPs are involved in various neurobiological pathways, including serotonergic, endorphinergic, GABAergic, and dopaminergic pathways.

The algorithm assigns a risk score to each patient based on the combined assessment of phenotypic and genotypic factors, categorizing them into low, moderate, and high-risk groups for OUD. A low-risk score (1-11) suggests that the clinician may proceed with opioid therapy, while moderate-risk (12-23) indicates caution and consideration for urine drug testing. A high-risk score (≥24) suggests extreme caution and the implementation of more intensive monitoring and alternative treatment strategies.

Implications for Primary Care and Beyond

The study's findings suggest that the algorithm can effectively stratify patients in primary care settings according to their risk of OUD. This can help physicians make more informed decisions about opioid prescribing, monitoring, and treatment strategies.

By identifying high-risk patients early, clinicians can implement preventative measures such as frequent urine drug testing, alternative pain management therapies, and closer monitoring to reduce the likelihood of OUD development.

While the algorithm shows promise, the researchers emphasize the need for further studies to address limitations such as the wide age range of participants and reliance on ICD codes for OUD diagnosis. Future research should also explore additional objective measures of drug use and address potential barriers to technology adoption, including financial and practice-related challenges.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1177/2333392817717411, Alternate LINK

Title: Validation Study Of A Predictive Algorithm To Evaluate Opioid Use Disorder In A Primary Care Setting

Subject: Health Policy

Journal: Health Services Research and Managerial Epidemiology

Publisher: SAGE Publications

Authors: Maneesh Sharma, Chee Lee, Svetlana Kantorovich, Maria Tedtaotao, Gregory A. Smith, Ashley Brenton

Published: 2017-01-01

Everything You Need To Know

How does the new algorithm predict the risk of developing an Opioid Use Disorder (OUD)?

The predictive algorithm assesses the risk of Opioid Use Disorder (OUD) by uniquely combining phenotypic and genotypic risk factors. Phenotypic factors include observable traits like a history of alcoholism, illegal drug use, mental health disorders, and age (16-45). Genotypic factors involve 11 single nucleotide polymorphisms (SNPs) linked to opioid abuse affecting neurobiological pathways like serotonergic, endorphinergic, GABAergic, and dopaminergic pathways. The algorithm assigns a risk score, categorizing patients into low, moderate, and high-risk groups for OUD.

How do the different risk scores generated by the algorithm influence a physician's decisions regarding opioid prescriptions and patient care?

The algorithm's risk scores guide clinical decisions. A low-risk score (1-11) suggests opioid therapy is appropriate. A moderate-risk score (12-23) indicates caution, potentially requiring urine drug testing. A high-risk score (≥24) suggests extreme caution, intensive monitoring, and exploring alternative treatments. This stratification enables primary care physicians to make more informed prescribing choices and tailor treatment strategies.

What are phenotypic risk factors, and what specific factors are considered by the predictive algorithm?

Phenotypic risk factors, as used in the algorithm, are observable characteristics and risk factors. Examples include a personal history of alcoholism, illegal drug use, prescription drug misuse, mental health disorders, and the patient's age, specifically if they are between 16 and 45 years old. These factors are readily assessed during a patient evaluation and contribute to the overall risk assessment for developing Opioid Use Disorder (OUD).

What is the role of genotypic factors, specifically single nucleotide polymorphisms (SNPs), in the algorithm's prediction of opioid use disorder risk?

The 11 single nucleotide polymorphisms (SNPs) included in the algorithm are genotypic risk factors related to opioid abuse, misuse, dependence, or addiction. These SNPs impact various neurobiological pathways such as serotonergic, endorphinergic, GABAergic, and dopaminergic pathways. By incorporating these genetic markers, the algorithm provides a deeper, more personalized risk assessment beyond what can be determined by phenotypic factors alone, improving the accuracy of OUD risk prediction.

Are there other risk factors, besides the genotypic and phenotypic, that could be impacting the risk of developing OUD?

While the study focuses on phenotypic and genotypic factors, other elements also influence opioid addiction risk. Socioeconomic factors, access to mental health services, the specific type and dosage of opioid prescribed, and the duration of opioid use all play a significant role. A truly comprehensive approach to preventing OUD would integrate these additional considerations alongside the algorithm's risk assessment to provide holistic patient care and support.