Predicting Factors of Re-Hospitalization After Medically Managed Intensive Inpatient Services in Opioid Use Disorder

IntroductionOpioid use disorder has continued to rise in prevalence across the United States, with an estimated 2.5 million Americans ailing from the condition (NIDA, 2020). Medically managed detoxification incurs substantial costs and, when used independently, may not be effective in preventing relapse (Kosten & Baxter, 2019). While numerous studies have focused on predicting the factors of developing opioid use disorder, few have identified predictors of readmission to medically managed withdrawal at an inpatient level of care. Utilizing a high-fidelity dataset from a large multi-site behavioral health hospital, these predictors are explored. MethodsPatients diagnosed with Opioid Use Disorder and hospitalized in the inpatient level of care were analyzed to identify readmission predictors. Factors including patient demographics, patient-reported outcome measures, and post-discharge treatment interventions were included. Patients re-hospitalized to the inpatient level of care were binary labeled in the dataset, and various machine learning algorithms were tested, including machine learning techniques. Methods include random forest, gradient boosting, and deep learning techniques. Evaluation statistics include specificity, accuracy, precision, and Matthew\u27s Coefficient. ResultsOverall, there was a wide variation if correctly predicting the class of patients that would readmit to a medically managed level of inpatient detoxification. Out of the six models evaluated, three of the six did not converge, thus not producing a viable feature ranking. However, of the other three models that did converge, the deep learning model produced almost perfect classification, producing an accuracy of .98. AdaBoost and the logistic regression model produced an accuracy of .97 and .61, respectively. Each of these models produced a similar set of features that were important to predicting which patient profile would readmit to medically managed inpatient detoxification. ConclusionsThe results indicate that overall reduction in the Quick Inventory of Depressive Symptomology, discharge disposition, age, length of stay, and a patient\u27s total number of diagnoses were important features at predicting readmission. Additionally, deep learning algorithms vastly outperformed other machine learning algorithms

Genetic and environmental prediction of opioid cessation using machine learning, GWAS, and a mouse model

The United States is currently experiencing an epidemic of opioid use, use disorder, and overdose-related deaths. While studies have identified several loci that are associated with opioid use disorder (OUD) risk, the genetic basis for the ability to discontinue opioid use has not been investigated. Furthermore, very few studies have investigated the non-genetic factors that are predictive of opioid cessation or their predictive ability. In this thesis, I studied a novel phenotype–opioid cessation, defined as the time since last use of illicit opioids (1 year ago as cease) among persons meeting lifetime DSM-5 criteria for opioid use disorder (OUD). In chapter two, I identified novel genetic variants and biological pathways that potentially regulate opioid cessation success through a genome wide study, as well as genetic overlap between opioid cessation and other substance cessation traits. In chapter three, I identified multiple non-genetic risk factors specific to each racial group that are predictive of opioid cessation from the same individuals analyzed in chapter two by applying several linear and non-linear machine learning techniques to a set of more than 3,000 variables assessed by a structured psychiatric interview. Factors identified from this atheoretical approach can be grouped into opioid use activities, other drug use, health conditions, and demographics, while the predictive accuracy as high as nearly 80% was achieved. The findings from this research generated more hypotheses for future studies to reference. In chapter four, I performed differential gene expression and network analysis on mice with different oxycodone (an opioid receptor agonist)-induced behaviors and compared the significantly associated genes and network modules with top-ranked genes identified in humans. The pathway cross-talks and gene homologs identified from both species illuminate the potential molecular mechanism of opioid behaviors. In summary, this thesis utilized statistical genetics, machine learning, and a computational biology framework to address factors that are associative with opioid cessation in humans, and cross-referenced the genetic findings in a mouse model. These findings serve as references for future studies and provide a framework for personalizing the treatment of OUD

A Spatial Risk Prediction Model for Drug Overdose

Drug overdose is a leading cause of unintentional death in the United States and has contributed significantly to a decline in life expectancy from 2015 to 2018. Overdose deaths, especially from opioids, have also been recognized in recent years as a significant public health issue. To address this public health problem, this study sought to identify neighborhood-level (e.g., block group) factors associated with drug overdose and develop a spatial model using machine learning (ML) algorithms to predict the likelihood or risk of drug overdoses across South Carolina. This study included block group level socio-demographic factors and drug use variables which may influence the incidence of drug overdose. In particular, this study developed a new index of access to measure spatial access to treatment facilities and incorporated these variables to assess the relationship between drug overdose and accessibility to the treatment centers. We explored different ML algorithms (e.g., XGBoost, Random Forest) to identify optimum predictors in each category. The categories were combined into a final ensemble predictive model that addressed spatial dependency. An evaluation was conducted to validate that the final model generalized well across the different datasets and geographical areas. Results of the study identified strong neighborhood-level predictors of a drug overdose, pinpointing the most critical neighborhood-level factor(s) that place a community at risk and protect communities from developing such problems. These factors included proportion of households receiving food stamps, households with income less than $35,000, high opioid prescription rates, smoking accessories expenditures, and low accessibility to opioid treatment programs and hospitals. The generalized error of spatial models did not increase considerably in spatial cross-validation compared to the error estimated from normal cross-validation. Our model also outperformed the geographic weighted regression method. Our Results show that variables regarding socio-demographic factors, drug use variables, and protective resources can assist in spatial drug overdose prediction. Our finding highlights several specific pathways toward community-level intervention targeted to a vulnerable population facing potentially high burdens of drug abuse and overdose

Predicting opioid dependence from electronic health records with machine learning

Abstract Background The opioid epidemic in the United States is averaging over 100 deaths per day due to overdose. The effectiveness of opioids as pain treatments, and the drug-seeking behavior of opioid addicts, leads physicians in the United States to issue over 200 million opioid prescriptions every year. To better understand the biomedical profile of opioid-dependent patients, we analyzed information from electronic health records (EHR) including lab tests, vital signs, medical procedures, prescriptions, and other data from millions of patients to predict opioid substance dependence. Results We trained a machine learning model to classify patients by likelihood of having a diagnosis of substance dependence using EHR data from patients diagnosed with substance dependence, along with control patients with no history of substance-related conditions, matched by age, gender, and status of HIV, hepatitis C, and sickle cell disease. The top machine learning classifier using all features achieved a mean area under the receiver operating characteristic (AUROC) curve of ~ 92%, and analysis of the model uncovered associations between basic clinical factors and substance dependence. Additionally, diagnoses, prescriptions, and procedures prior to the diagnoses of substance dependence were analyzed to elucidate the clinical profile of substance-dependent patients, relative to controls. Conclusions The predictive model may hold utility for identifying patients at risk of developing dependence, risk of overdose, and opioid-seeking patients that report other symptoms in their visits to the emergency room