Predictive Modelling of Susceptibility to Substance Abuse, Mortality and Drug-Drug Interactions in Opioid Patients

Objective: Opioids are a class of drugs that are known for their use as pain relievers. They bind to opioid receptors on nerve cells in the brain and the nervous system to mitigate pain. Addiction is one of the chronic and primary adverse events of prolonged usage of opioids. They may also cause psychological disorders, muscle pain, depression, anxiety attacks etc. In this study, we present a collection of predictive models to identify patients at risk of opioid abuse and mortality by using their prescription histories. Also, we discover particularly threatening drug-drug interactions in the context of opioid usage.

Methods and Materials: Using a publicly available dataset from MIMIC-III, two models were trained, Logistic Regression with L2 regularization (baseline) and Extreme Gradient Boosting (enhanced model), to classify the patients of interest into two categories based on their susceptibility to opioid abuse. We’ve also used K-Means clustering, an unsupervised algorithm, to explore drug-drug interactions that might be of concern.

Results: The baseline model for classifying patients susceptible to opioid abuse has an F1 score of 76.64% (accuracy 77.16%) while the enhanced model has an F1 score of 94.45% (accuracy 94.35%). These models can be used as a preliminary step towards inferring the causal effect of opioid usage and can help monitor the prescription practices to minimize the opioid abuse.

Discussion and Conclusion: Results suggest that the enhanced model provides a promising approach in preemptive identification of patients at risk for opioid abuse. By discovering and correlating the patterns contributing to opioid overdose or abuse among a variety of patients, machine learning models can be used as an efficient tool to help uncover the existing gaps and/or fraudulent practices in prescription writing. To quote an example of one such incidental finding, our study discovered that insulin might possibly be interacting with opioids in an unfavourable way leading to complications in diabetic patients. This indicates that diabetic patients under long term opioid usage might need to take increased amounts of insulin to make it more effective. This observation backs up prior research studies done on a similar aspect. To increase the translational value of our work, the predictive models and the associated software code are made available under the MIT License.

Low-Dose Naltrexone Co-Treatment in the Prevention of Opioid-Induced Hyperalgesia

Opioid-induced hyperalgesia (OIH) is characterized by a heightened sensitivity to pain that occurs in patients following opioid use. Prescription of opioids is currently the standard form of pain management for both neuropathic and nociceptive pain, due to the relief that patients typically report following their use. Opioids, which aim to provide analgesic effects, can paradoxically cause increasing degrees of pain among the users. The increased nociception can be either due to the underlying pain for which the opioid was initially prescribed, or other unrelated pain. As a result, those who are initially prescribed opioids for chronic pain relief may instead be left with no overall relief, and experience additional algesia. While OIH can be treated through the reduction of opioid use, antagonistic treatment can also be utilized. In an attempt to reduce OIH in patients, low doses of the opioid antagonist naltrexone can be given concurrently. This review will analyze the current role and effectiveness of the use of naltrexone in managing OIH in opioid users as described in clinical and non-clinical studies. Additionally, it seeks to characterize the underlying mechanisms that enable opioid antagonist naltrexone to reduce OIH while still allowing opioids to act as an analgesic. The authors find that OIH is a prevalent condition, and in order to effectively combat it, clinicians and patients can benefit from an extended study on how naltrexone can be utilized as a treatment alongside opioids prescribed for pain management.

Delayed Presentation of Foot Compartment Syndrome

A 30-year-old male after consuming alcohol and drugs lost consciousness and passed out in his bathroom. After 10 hours, he was found immobilized stuporous on his bathroom floor and brought by emergency service to the emergency room. He was found to have compartment syndrome of the right lower limb and underwent emergency fasciotomy of three compartments of the thigh and four compartments of the leg. The postoperative course was complicated by acute kidney injury necessitating hemodialysis. On postoperative day three from the index fasciotomy patient was found to develop foot compartment syndrome despite not having foot compartment involved in the initial presentation. In opioid abuse, patients' additional compartments can get involved during the disease process, and it is essential to identify them with regular physical examination and use of biomarkers.