Opioid Adjunct Drug Therapy: Evaluating Effectiveness Using Text Analytics of Real World Data

Opioid analgesics continue to be the mainstay of pharmacologic treatment of moderate to severe pain. An adjunct is a drug that in its pharmacological characteristic is not identified primarily as an analgesic, but that has been found in clinical practice to have either an independent analgesic effect or additive analgesic properties when used with opioids. By using an adjunct to maximize the level of analgesia, the required opioid dosage may be reduced, together with concomitant adverse effects. BACKGROUND Real World Data (RWD) refers to data that describe observations in normal clinical practice obtained by any non- interventional methodology, such as Randomized Controlled Trials (RCTs). The U.S. Food and Drug Administration (FDA) maintains one of the largest government databases in the country, the FDA Adverse Event Reporting System (FAERS). It is comprised of adverse event reports submitted to the FDA through the “MedWatch” reporting program and contains a plethora of Real World Data: thousands of case reports on opioids and adjunct drugs, comprised of unstructured textual data. The objective of this study is to identify the therapeutic effectiveness of adjunct drugs with opioids by examination of narrative text in MedWatch cases. METHODS This project follows the traditional approach of knowledge discovery in databases, comprised of five steps: 1) Data selection, 2) Pre-processing, 3) Transformation, 4) Data mining and 5) Interpretation. The strategy employed will transform the narrative text data into an organized and concise summary of key endpoints. An appropriate sample (500 to 1,000 relevant patient cases) that describe opioids and adjunct drugs will be included in the case report data set. Key task 1: Data selection and pre-processing (Steps 1,2). MedWatch narratives of patient cases that describe the types of opioid and adjunct drug combinations used in real-life clinical settings will be obtained from the FAERS database. Key task 2: Data transformation and mining (Steps 3,4). Cases will be organized in a Structured Query Language (SQL) database. A lexicon of words and terms clinically or theoretically related to opioid and adjunct drug therapy will be developed, which will serve as a reference for analysis of the text. Using Natural Language Processing (NLP) techniques, textual data will be transformed into n-grams using a MySQL n-gram parser. N-gram extraction will identify notes containing n-grams matching terms from the theory-and expert-derived lexicon. Categories will be formed from the most frequently identified n-grams and their total frequency. RESULTS (PROJECTED) Key task 3: Evaluate and interpret results (Step 5) and compile the information into a useful format for healthcare providers. The most commonly extracted n-grams will be identified by category, then frequency, and displayed in tabular format. N-gram analysis of the corpus of case reports reveals the frequency with which and adjunct drug was used with an opioid, and indicate impact on analgesic effect. Completion of key tasks provides evidence on the associated outcomes of treatment; whether the adjunct drug therapy indicates treatment success or failure. CONCLUSION Findings of this project will add to the existing body of knowledge on opioid adjunct therapy for analgesia and may corroborate or refute other existing evidence for adjunct drug therapeutic effectiveness derived from case reports or clinical trials

Enhancing Personal Health Record Adoption Through the Community Pharmacy Network: A Service Project

Personal Health Records, or PHRs, are designed to be created, maintained and securely managed by patients themselves. PHRs can reduce medical errors and increase quality of care in the health care system through efficiency and improving accessibility of health information. Adoption of PHRs has been disappointingly low. In this paper a project is described—essentially a call for action—whereby the skills, expertise, and accessibility of the community pharmacist is utilized to address the problem of poor PHR adoption. The objective of this proposed project is to promote the expansion of PHR adoption directly at the consumer level by utilizing the existing infrastructure of community pharmacies. The ADDIE model can provide the framework for PHR adoption in community pharmacies. ADDIE is an acronym that stands for the 5 phases contained in the model: 1) Analysis, 2) Design, 3) Development, 4) Implementation, and 5) Evaluation. ADDIE is a versatile educational model used for creating instructional materials, and has found utility as a guidance model for managing projects of all types. By bringing together these concepts: the highly accessible infrastructure of community pharmacies with the educational resources to inform consumers on the proper use of PHRs, the quality of care for patients will be greatly enhanced.   Type: Idea Pape

An Analysis of Covid-19 Vaccine Allergic Reactions

From our study, all three covid-19 vaccines have a similar proportion of adverse reaction reports in which the patient had a history of allergies. However, the proportion of life-threatening outcomes were lower for those with the Janssen vaccine (0.62% hospitalization rate for Janssen versus 2.59% for Pfizer and 0.60% death for Janssen versus 5.15% for Moderna). In terms of specific allergies, patients with *cillin or sulfa allergies had the most adverse reactions to covid-19 vaccines, however, Janssen again had the lowest percentage of reported deaths (1.39% for *cillin-related allergy deaths for Janssen versus 6.10% for Pfizer). In terms of patient age and gender, females has 2.9x the number of adverse reactions than males and a lower average age for reactions for the Pfizer and Moderna vaccines. We feel this data could be used by individuals and medical professionals to assist in choosing a vaccine to maximize patient safety based on their allergy history, age and gender

A Data Driven Approach to Profile Potential SARS-CoV-2 Drug Interactions Using TylerADE

We use a data driven approach on a cleaned adverse drug reaction database to determine the reaction severity of several covid-19 drug combinations currently under investigation. We further examine their safety for vulnerable populations such as individuals 65 years and older. Our key findings include 1. hydroxychloroquine/chloroquine are associated with increased adverse drug event severity versus other drug combinations already not recommended by NIH treatment guidelines, 2. hydroxychloroquine/azithromycin are associated with lower adverse drug event severity among older populations, 3. lopinavir/ritonavir had lower adverse reaction severity among toddlers and 4. the combination of azithromycin, hydroxychloroquine and tocilizumab is safer than its component drugs. While our approach does not consider drug efficacy, it can help prioritize clinical trials for drug combinations by focusing on those with the lowest reaction severity and thus increase potential treatment options for covid-19 patients

A Decision Tree Analysis of Opioid and Prescription Drug Interactions Leading to Death Using the FAERS Database

Can unknown and possibly dangerous interactions between opioids and prescription drugs be identified? Is it possible? Our research seeks to answer these questions by applying a supervised machine learning algorithm to the FDA’s Adverse Event Reporting System (FAERS). We trained a decision tree classifier to investigate heroin and prescription drug interactions with an accuracy of 84.9%. We found that heroin and buprenorphine, a commonly prescribed opioid detox drug, led to a 28.0% survival rate among patients. Heroin, buprenorphine, and quinine were even deadlier with a 24.0% survival rate. Our technique can be applied to previously unknown drug combinations to predict mortality and perhaps improve patient safety

Calculating a Severity Score of an Adverse Drug Event Using Machine Learning on the FAERS Database

An Adverse Drug Event (ADE) is a medical injury that can result from a prescription or over the counter drug that causes an allergic reaction, overdose, reaction with other drugs or is the result of a medication error. Vulnerable populations such as children and the elderly are most susceptible to ADEs. This lack of standardized data has kept FAERS from fulfilling its full potential as a pharmacovigilance tool and its limitations have been the subject of numerous studies. Our motivation is to improve drug safety by creating a new type of pharmacovigilence system that 1. Performs data cleaning and standardization of FAERS data, 2. Computes a drug reaction severity score for each ADE based on the reported indications and coded using a modified Hartwig Severity scale, 3. Models the data to A) empirically identify drug-interaction events and their relative strength of event in specific symptom-related incidents and to B) identify drug-disease event severity for specific indications such as hypertension, stroke and cardiac failure, 4. Computes a predicted severity score for the models using machine learning algorithms 5. Evaluates the accuracy of the predicted severity score versus actual severity on a holdout dataset, and 6. Builds a predictive clinical tool for physicians that can interact with a patient’s EHR and identify adverse reaction potential at the point of prescription. We propose a global data-driven approach with the TylerADE System. This system uses advanced machine-learning techniques to sift through data and uncover potentially unknown drug events. This research has the potential to 1) improve the efficiency of pharmacological research by identifying potentially unknown n-drug events that merit further study; 2) create a risk score of potential medication events that physicians can use in a clinical setting; and 3) improve patient safety

Effects of Adjuncts on Opioids

Opioids are used to treat chronic pain due to their effectiveness but can be very harmful and addictive. One way that the negative side effects of opioids can be avoided is by taking an adjunct drug alongside the opioid. Certain adjuncts greatly decrease the severity of opioids, but there is an underwhelming amount of research on the topic. We have analyzed over 135 million records from the FDA\u27s FAERS dataset, which records adverse drug events. After cleaning and formatting the FAERS database, we checked for outcomes of opioid usage and compared it to adjunct opioid combinations. Some adjuncts significantly lowered severity in certain opioids, whereas others made the effects more severe. For example, adding Diazepam to Methadone lowered severity percentage by 11.82%, while the addition of Diazepam to Oxycodone increased the death rate by 36.96%. We also found that adding Bupivacaine to Fentanyl lowers death rate from 43.2% to 6.21

Irony of the FAERS Database: An Analysis of Data Input Errors and Potential Consequences

One of the most common data entry problems occurs during the data input process. Even a seemingly insignificant typographical error can cause short- and long-term problems which may lead to inaccurate records, misinformation, and disorganization. The objective of this report is to present an analysis of specific files within the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database involving errors and inconsistencies in reporting of drug names and assess potential consequences

Pharmacy students’ perceptions and attitudes toward face-to-face vs. virtual team-based learning (TBL) in the didactic curriculum: A mixed-methods study

Introduction Virtual TBL is an online adaptation of the team-based learning (TBL) instructional strategy, emphasizing collaborative learning and problem-solving. The emergency shift to virtual TBL during the COVID-19 pandemic presented unique challenges. This study aims to 1) compare overall pharmacy students’ perceptions and attitudes toward face-to-face (FTF) TBL vs. virtual TBL in the didactic curriculum and stratify their perceptions and attitudes by various students’ characteristics; 2) evaluate students’ perceptions of the strengths and weaknesses of virtual TBL. Methods This mixed-methods, pre-post, cross-sectional study utilized an anonymous survey to collect the data. Pharmacy students completed a survey to compare their perceptions and attitudes toward learning, class experience, learning outcomes achieved, and satisfaction with FTF TBL vs. virtual TBL using a 5-point Likert-type scale. Additionally, the survey included two open-ended questions to gather students’ perceptions of the strengths and weaknesses of virtual TBL. Quantitative survey data were analyzed using the Wilcoxon matched-pairs signed rank exact test, while qualitative survey data were analyzed using thematic analysis. Results A total of 117 students (response rate of 59.4%) completed the study survey. Pharmacy students perceived FTF TBL to be superior to virtual TBL in their attitudes toward learning, class experience, learning outcomes achieved, and overall satisfaction across various students’ characteristics. While the students identified some unique strengths of using virtual TBL, they also highlighted several weaknesses of using this learning modality compared to FTF TBL. Conclusions Pharmacy students perceived FTF TBL to be superior to virtual TBL across various students’ characteristics. These findings can be helpful to pharmacy programs considering the implementation of virtual TBL in their didactic curricula. Future research should explore whether a purposefully designed virtual TBL environment, as opposed to the pandemic-driven emergency TBL planning, can influence students’ perceptions and attitudes toward virtual TBL