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 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 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.

Pharmacokinetic Drug-drug Interaction of Antibiotics Used in Sepsis Care in China

2020 ◽  
Vol 21 ◽  
Author(s):  
Xuan Yu ◽  
Zixuan Chu ◽  
Jian Li ◽  
Rongrong He ◽  
Yaya Wang ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Penicillin G ◽  
Literature Mining ◽  
Human Pharmacokinetics ◽  
P450 Enzyme ◽  
Drug Drug Interaction ◽  
Pharmacokinetic Drug ◽  
Sepsis Care

Background: Many antibiotics have a high potential for having an interaction with drugs, as perpetrator and/or victim, in critically ill patients, and particularly in sepsis patients. Methods: The aim of this review is to summarize the pharmacokinetic drug-drug interaction (DDI) of 45 antibiotics commonly used in sepsis care in China. Literature mining was conducted to obtain human pharmacokinetics/dispositions of the antibiotics, their interactions with drug metabolizing enzymes or transporters, and their associated clinical drug interactions. Potential DDI is indicated by a DDI index > 0.1 for inhibition or a treated-cell/untreated-cell ratio of enzyme activity being > 2 for induction. Results: The literature-mined information on human pharmacokinetics of the identified antibiotics and their potential drug interactions is summarized. Conclusion: Antibiotic-perpetrated drug interactions, involving P450 enzyme inhibition, have been reported for four lipophilic antibacterials (ciprofloxacin, erythromycin, trimethoprim, and trimethoprim-sulfamethoxazole) and three lipophilic antifungals (fluconazole, itraconazole, and voriconazole). In addition, seven hydrophilic antibacterials (ceftriaxone, cefamandole, piperacillin, penicillin G, amikacin, metronidazole, and linezolid) inhibit drug transporters in vitro. Despite no reported clinical PK drug interactions with the transporters, caution is advised in the use of these antibacterials. Eight hydrophilic antibacterials (all β-lactams; meropenem, cefotaxime, cefazolin, piperacillin, ticarcillin, penicillin G, ampicillin, and flucloxacillin), are potential victims of drug interactions due to transporter inhibition. Rifampin is reported to perpetrate drug interactions by inducing CYP3A or inhibiting OATP1B; it is also reported to be a victim of drug interactions, due to the dual inhibition of CYP3A4 and OATP1B by indinavir. In addition, three antifungals (caspofungin, itraconazole, and voriconazole) are reported to be victims of drug interactions because of P450 enzyme induction. Reports for other antibiotics acting as victims in drug interactions are scarce.

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.

Comparison of eight screening tools to detect interactions between herbal supplements and oncology agents

2020 ◽  
Vol 26 (8) ◽  
pp. 1843-1849
Author(s):  
Faisal Shakeel ◽  
Fang Fang ◽  
Kelley M Kidwell ◽  
Lauren A Marcath ◽  
Daniel L Hertz
Keyword(s):  
Drug Interaction ◽  
Positive Predictive Value ◽  
Drug Interactions ◽  
Predictive Value ◽  
Screening Tools ◽  
Herbal Supplements ◽  
Interaction Detection ◽  
Natural Medicine ◽  
Interaction Screening ◽  
Drug Drug Interaction

Introduction Patients with cancer are increasingly using herbal supplements, unaware that supplements can interact with oncology treatment. Herb–drug interaction management is critical to ensure optimal treatment outcomes. Several screening tools exist to detect drug–drug interactions, but their performance to detect herb–drug interactions is not known. This study compared the performance of eight drug–drug interaction screening tools to detect herb–drug interaction with anti-cancer agents. Methods The herb–drug interaction detection performance of four subscription (Micromedex, Lexicomp, PEPID, Facts & Comparisons) and free (Drugs.com, Medscape, WebMD, RxList) drug–drug interaction tools was assessed. Clinical relevance of each herb–drug interaction was determined using Natural Medicine and each drug–drug interaction tool. Descriptive statistics were used to calculate sensitivity, specificity, positive predictive value, and negative predictive value. Linear regression was used to compare performance between subscription and free tools. Results All tools had poor sensitivity (<0.20) for detecting herb–drug interaction. Lexicomp had the highest positive predictive value (0.98) and best overall performance score (0.54), while Medscape was the best performing free tool (0.52). The worst subscription tools were as good as or better than the best free tools, and as a group subscription tools outperformed free tools on all metrics. Using an average subscription tool would detect one additional herb–drug interaction for every 10 herb–drug interactions screened by a free tool. Conclusion Lexicomp is the best available tool for screening herb–drug interaction, and Medscape is the best free alternative; however, the sensitivity and performance for detecting herb–drug interaction was far lower than for drug–drug interactions, and overall quite poor. Further research is needed to improve herb–drug interaction screening performance.

Educational intervention for physicians to address the risk of opioid abuse

2017 ◽  
Vol 13 (5) ◽  
pp. 303 ◽  
Author(s):  
Margaret K. Pasquale, PhD ◽  
Richard L. Sheer, BA ◽  
Jack Mardekian, PhD ◽  
Elizabeth T. Masters, MS, MPH ◽  
Nick C. Patel, PharmD, PhD ◽  
...  
Keyword(s):  
Mental Health ◽  
At Risk ◽  
Patient Information ◽  
Opioid Abuse ◽  
High Dose ◽  
Opioid Use ◽  
Educational Materials ◽  
Patients At Risk ◽  
Ed Visits ◽  
The Impact

Objective: To evaluate the impact of a pilot intervention for physicians to support their treatment of patients at risk for opioid abuse.Setting, design and patients, participants: Patients at risk for opioid abuse enrolled in Medicare plans were identified from July 1, 2012 to April 30, 2014 (N = 2,391), based on a published predictive model, and linked to 4,353 opioid-prescribing physicians. Patient-physician clusters were randomly assigned to one of four interventions using factorial design.Interventions: Physicians received one of the following: Arm 1, patient information; Arm 2, links to educational materials for diagnosis and management of pain; Arm 3, both patient information and links to educational materials; or Arm 4, no communication.Main outcome measures: Difference-in-difference analyses compared opioid and pain prescriptions, chronic high-dose opioid use, uncoordinated opioid use, and opioid-related emergency department (ED) visits. Logistic regression compared diagnosis of opioid abuse between cases and controls postindex.Results: Mailings had no significant impact on numbers of opioid or pain medications filled, chronic high-dose opioid use, uncoordinated opioid use, ED visits, or rate of diagnosed opioid abuse. Relative to Arm 4, odds ratios (95% CI) for diagnosed opioid abuse were Arm 1, 0.95(0.63-1.42); Arm 2, 0.83(0.55-1.27); Arm 3, 0.72(0.46-1.13). While 84.7 percent had ≥ 1 psychiatric diagnoses during preindex (p = 0.89 between arms), only 9.5 percent had ≥ 1 visit with mental health specialists (p = 0.53 between arms).Conclusions: Although this intervention did not affect pain-related outcomes, future interventions involving care coordination across primary care and mental health may impact opioid abuse and improve quality of life of patients with pain.

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