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

2021 ◽  
Vol 4 ◽  
Author(s):  
Ramya Vunikili ◽  
Benjamin S. Glicksberg ◽  
Kipp W. Johnson ◽  
Joel T. Dudley ◽  
Lakshminarayanan Subramanian ◽  
...  
Keyword(s):  
At Risk ◽  
Drug Interactions ◽  
Predictive Models ◽  
Causal Effect ◽  
Opioid Abuse ◽  
Gradient Boosting ◽  
Diabetic Patients ◽  
Opioid Overdose ◽  
Extreme Gradient Boosting ◽  
Patients At Risk

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.

scholarly journals Predictive modeling of susceptibility to substance abuse, mortality and drug-drug interactions in opioid patients

2018 ◽  
Author(s):  
Ramya Vunikili ◽  
Benjamin S Glicksberg ◽  
Kipp W Johnson ◽  
Joel Dudley ◽  
Lakshminarayanan Subramanian ◽  
...  
Keyword(s):  
At Risk ◽  
Drug Interaction ◽  
Drug Interactions ◽  
Predictive Models ◽  
Opioid Abuse ◽  
Gradient Boosting ◽  
Interaction Patterns ◽  
Extreme Gradient Boosting ◽  
Patients At Risk ◽  
Drug Drug Interaction

Opioid addiction causes high degree of morbidity and mortality. Preemptive identification of patients at risk of opioid dependence and developing intelligent clinical decisions to deprescribe opioids to the vulnerable patient population may help in reducing the burden. Identifying patients susceptible to mortality due to opioid-induced side effects and understanding the landscape of drug-drug interaction pairs aggravating opioid usage are significant, yet, unexplored research questions. In this study, we present a collection of predictive models to identify patients at risk of opioid abuse, mortality and drug-drug interactions in the context of opioid usage. Using publicly available dataset from MIMIC-III, we developed predictive models (opioid abuse models a=Logistic Regression; b=Extreme Gradient Boosting and mortality model= Extreme Gradient Boosting) and identified potential drug-drug interaction patterns. To enable the translational value of our work, the predictive model and all associated software code is provided. This repository could be used to build clinical decision aids and thus improve the optimization of prescription rates for vulnerable population.

scholarly journals Using explainable machine learning to identify patients at risk of reattendance at discharge from emergency departments

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
F. P. Chmiel ◽  
D. K. Burns ◽  
M. Azor ◽  
F. Borca ◽  
M. J. Boniface ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Emergency Departments ◽  
Gradient Boosting ◽  
Tree Model ◽  
Post Discharge ◽  
Test Set ◽  
Increased Risk ◽  
Extreme Gradient Boosting ◽  
Patients At Risk

AbstractShort-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance could help reduce the number of missed critical illnesses and could reduce avoidable utilization of emergency departments by enabling targeted post-discharge intervention. In this manuscript, we present a retrospective, single-centre study where we created and evaluated an extreme gradient boosting decision tree model trained to identify patients at risk of reattendance within 72 h of discharge from an emergency department (University Hospitals Southampton Foundation Trust, UK). Our model was trained using 35,447 attendances by 28,945 patients and evaluated on a hold-out test set featuring 8847 attendances by 7237 patients. The set of attendances from a given patient appeared exclusively in either the training or the test set. Our model was trained using both visit level variables (e.g., vital signs, arrival mode, and chief complaint) and a set of variables available in a patients electronic patient record, such as age and any recorded medical conditions. On the hold-out test set, our highest performing model obtained an AUROC of 0.747 (95% CI 0.722–0.773) and an average precision of 0.233 (95% CI 0.194–0.277). These results demonstrate that machine-learning models can be used to classify patients, with moderate performance, into low and high-risk groups for reattendance. We explained our models predictions using SHAP values, a concept developed from coalitional game theory, capable of explaining predictions at an attendance level. We demonstrated how clustering techniques (the UMAP algorithm) can be used to investigate the different sub-groups of explanations present in our patient cohort.

Performance of a Predictive Model versus Prescription-Based Thresholds in Identifying Patients at Risk of Fatal Opioid Overdose

2021 ◽  
Vol 56 (3) ◽  
pp. 396-403
Author(s):  
Lindsey M. Ferris ◽  
Brendan Saloner ◽  
Kate Jackson ◽  
B. Casey Lyons ◽  
Vijay Murthy ◽  
...  
Keyword(s):  
At Risk ◽  
Predictive Model ◽  
Opioid Overdose ◽  
Patients At Risk ◽  
Model Versus

Piloting a Statewide Emergency Department Take‐Home Naloxone Program: Improving the Quality of Care for Patients at Risk of Opioid Overdose

2021 ◽  
Author(s):  
Aaron Dora‐Laskey ◽  
Joan Kellenberg ◽  
Chin Hwa Dahlem ◽  
Elizabeth English ◽  
Monica Gonzalez Walker ◽  
...  
Keyword(s):  
At Risk ◽  
Emergency Department ◽  
Quality Of Care ◽  
Opioid Overdose ◽  
Patients At Risk

scholarly journals Predicting Persistent Opioid Use, Abuse, and Toxicity Among Cancer Survivors

2019 ◽  
Vol 112 (7) ◽  
pp. 720-727 ◽  
Author(s):  
Lucas K Vitzthum ◽  
Paul Riviere ◽  
Paige Sheridan ◽  
Vinit Nalawade ◽  
Rishi Deka ◽  
...  
Keyword(s):  
Risk Factors ◽  
At Risk ◽  
Cancer Survivors ◽  
Critical Role ◽  
Multivariable Analysis ◽  
Area Under The Curve ◽  
Opioid Abuse ◽  
Opioid Use ◽  
Multivariable Logistic Regression Model ◽  
Patients At Risk

Abstract Background Although opioids play a critical role in the management of cancer pain, the ongoing opioid epidemic has raised concerns regarding their persistent use and abuse. We lack data-driven tools in oncology to understand the risk of adverse opioid-related outcomes. This project seeks to identify clinical risk factors and create a risk score to help identify patients at risk of persistent opioid use and abuse. Methods Within a cohort of 106 732 military veteran cancer survivors diagnosed between 2000 and 2015, we determined rates of persistent posttreatment opioid use, diagnoses of opioid abuse or dependence, and admissions for opioid toxicity. A multivariable logistic regression model was used to identify patient, cancer, and treatment risk factors associated with adverse opioid-related outcomes. Predictive risk models were developed and validated using a least absolute shrinkage and selection operator regression technique. Results The rate of persistent opioid use in cancer survivors was 8.3% (95% CI = 8.1% to 8.4%); the rate of opioid abuse or dependence was 2.9% (95% CI = 2.8% to 3.0%); and the rate of opioid-related admissions was 2.1% (95% CI = 2.0% to 2.2%). On multivariable analysis, several patient, demographic, and cancer and treatment factors were associated with risk of persistent opioid use. Predictive models showed a high level of discrimination when identifying individuals at risk of adverse opioid-related outcomes including persistent opioid use (area under the curve [AUC] = 0.85), future diagnoses of opioid abuse or dependence (AUC = 0.87), and admission for opioid abuse or toxicity (AUC = 0.78). Conclusion This study demonstrates the potential to predict adverse opioid-related outcomes among cancer survivors. With further validation, personalized risk-stratification approaches could guide management when prescribing opioids in cancer patients.

A Pharmacist-Driven Glycemic Control Protocol to Reduce the Rate of Severe Hypoglycemia in High-Risk Patients

2020 ◽  
pp. 001857872097389
Author(s):  
Colleen A. Cook ◽  
Victor Vakayil ◽  
Kyle Pribyl ◽  
Derek Yerxa ◽  
John Kriz ◽  
...  
Keyword(s):  
At Risk ◽  
High Risk ◽  
Glycemic Control ◽  
Risk Reduction ◽  
Severe Hypoglycemia ◽  
Diabetic Patients ◽  
Control Protocol ◽  
Before And After ◽  
Patients At Risk ◽  
Acceptance Rates

Purpose: Hospital pharmacists contribute to patient safety and quality initiatives by overseeing the prescribing of antidiabetic medications. A pharmacist-driven glycemic control protocol was developed to reduce the rate of severe hypoglycemia events (SHE) in high-risk hospitalized patients. Methods: We retrospectively analyzed the rates of SHE (defined as blood glucose ≤40 mg/dL), before and after instituting a pharmacist-driven glycemic control protocol over a 4-year period. A hospital glucose management team that included a lead Certified Diabetes Educator Pharmacist (CDEP), 5 pharmacists trained in diabetes, a lead hospitalist, critical care and hospital providers established a process to first identify patients at risk for severe hypoglycemia and then implement our protocol. Criteria from the American Diabetes Association and the American Association of Clinical Endocrinologists was utilized to identify and treat patients at risk for SHE. We analyzed and compared the rate of SHE and physician acceptance rates before and after protocol initiation. Results: From January 2015 to March 2019, 18 297 patients met criteria for this study; 139 patients experienced a SHE and approximately 80% were considered high risk diabetes patients. Physician acceptance rates for the new protocol ranged from 77% to 81% from the year of initiation (2016) through 2018. The absolute risk reduction of SHE was 9 events per 1000 hospitalized diabetic patients and the relative risk reduction was 74% SHE from the start to the end of the protocol implementation. Linear regression analysis demonstrated that SHE decreased by 1.5 events per 1000 hospitalized diabetic patients (95% confidence interval, −1.54 to −1.48, P < .001) during the 2 years following the introduction of the protocol. This represents a 15% relative reduction of SHE per year. Conclusion: The pharmacist-driven glycemic control protocol was well accepted by our hospitalists and led to a significant reduction in SHE in high-risk diabetes patient groups at our hospital. It was cost effective and strengthened our physician-pharmacist relationship while improving diabetes care.

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