Deep Learning of Morphologic Correlations To Accurately Classify CD4+ and CD8+ T Cells by Diffraction Imaging Flow Cytometry.

The two major subtypes of human T cells, CD4+ and CD8+, play important roles in adaptive immune response by their diverse functions. To understand the structure-function relation at the single cell level, we isolated 2483 CD4+ and 2450 CD8+ T cells from fresh human splenocytes by immunofluorescent sorting and investigated their morphologic relations to the surface CD markers by acquisition and analysis of cross-polarized diffraction image (p-DI) pairs. A deep neural network of DINet-R has been built to extract 2560 features across multiple pixel scales of a p-DI pair per imaged cell. We have developed a novel algorithm to form a matrix of Pearson correlation coefficients by these features for selection of a support cell set with strong morphologic correlation in each subtype. The p-DI pairs of support cells exhibit significant pattern differences between the two subtypes defined by CD markers. To explore the relation between p-DI features and CD markers, we divided each subtype into two groups of A and B using the two support cell sets. The A groups comprise 90.2% of the imaged T cells and classification of them by DINet-R yields an accuracy of 97.3 ± 0.40% between the two subtypes. Analysis of depolarization ratios further reveals the significant differences in molecular polarizability between the two subtypes. These results prove the existence of a strong structure-function relation for the two major T cell subtypes and demonstrate the potential of diffraction imaging flow cytometry for accurate and label-free classification of T cell subtypes.

Opioid-Related Emergency Department Encounters: Patient, Encounter, and Community Characteristics Associated With Repeated Encounters.

STUDY OBJECTIVE: We describe the prevalence, trends, and factors associated with repeated emergency department (ED) encounters for opioid usage across multiple, independent hospital systems. METHODS: A statewide regional health information exchange system provided ED encounters from 4 Indiana hospital systems for 2012 to 2017. In accordance with a series of International Classification of Diseases, Ninth Revision (ICD-9) and ICD-10 diagnosis codes for opioid abuse, adverse effects of opioids, opioid dependence and unspecified use, and opioid poisoning, we identified patients with an ED encounter associated with opioid usage (9,295 individuals; 12,642 encounters). Multivariate logistic regression models then described the patient, encounter, prescription history, and community characteristics associated with the odds of a patient's incurring a subsequent opioid-related ED encounter. RESULTS: The prevalence of repeated nonfirst opioid-related ED encounters increased from 9.0% of all opioid encounters in 2012 to 34.3% in 2017. The number of previous opioid-related ED encounters, unique institutions at which the individual had had encounters, the encounter's being heroin-related, the individual's having a benzodiazepine prescription filled within 30 days before the encounter, and being either Medicaid insured or uninsured compared with private insurance were associated with significantly greater odds of having a subsequent encounter. CONCLUSION: The ED is increasingly a site utilized as the setting for repeated opioid-related care. Characteristics of the individual, encounter, and community associated with repeated opioid-related encounters may inform real-time risk-prediction tools in the ED setting. Additionally, the number of institutions to which the individual has presented may suggest the utility of health information exchange data and usage in the ED setting.

Fentanyl related overdose in Indianapolis: Estimating trends using multilevel Bayesian models.

INTRODUCTION: The opioid epidemic has been largely attributed to changes in prescribing practices over the past 20 years. Although current overdose trends appear driven by the opioid fentanyl, heroin has remained the focus of overdose fatality assessments. We obtained full toxicology screens on lethal overdose cases in a major US city, allowing more accurate assessment of the time-course of fentanyl-related deaths. METHODS: We used coroner data from Marion County, Indiana comprising 1583 overdose deaths recorded between January 1, 2010 and April 30, 2017. Bayesian multilevel models were fitted to predict likelihood of lethal fentanyl-related overdose using information about the victim's age, race, sex, zip code, and date of death. RESULTS: Three hundred and seventy-seven (23.8%) overdose deaths contained fentanyl across the seven-year period. Rates rose exponentially over time, beginning well below 15% from 2010 through 2013 before rising to approximately 50% by 2017. At the beginning of the study period, rates of fentanyl overdose were lowest among Black persons but increased more rapidly, eventually surpassing Whites. Currently, White females are at particularly low risk of fentanyl overdose whereas Black females are at high risk. Rates were highest for younger and middle-aged groups. Over time, fentanyl was more likely detected without the presence of other opioids. CONCLUSIONS: Fentanyl has increasingly been detected in fatal overdose deaths in Marion County. Policy and program responses must focus on education for those at highest risk of fentanyl exposure and death. These responses should also be tailored to meet the unique needs of high-risk demographics.

Assessment of risk behaviors in patients with opioid prescriptions: A study of indiana's inspect data.

BACKGROUND AND OBJECTIVES: Prescription Drug Monitoring Programs (PDMPs) can serve as screening tools and support the clinical decision-making process in patients receiving opioids. The objective of the study was to utilize 2014 INSPECT (Indiana's PDMP) data to identify factors that increase patients' likelihood to engage in opioid-related risk behaviors. METHODS: Based on a literature review, four risk behaviors were identified: Receiving >90 morphine milligram equivalents (MME), having >4 opioid prescribers, obtaining opioids from >4 pharmacies, and concurrent use of opioids and benzodiazepines. Two binary logistic regression analyses (engaging in at least one risk behaviors; engaging in all four risk behaviors) and an ordinal regression analysis (engaging in 0-4 risk behaviors) were conducted to identify factors associated with these opioid-related risk behaviors. RESULTS: Of the 1,538,120 unique opioid patients included in the study, 18.4% engaged in one, 5.3% in two, 1.6% in three, and .4% in all four risk behaviors. Depending on the model, prescribing a second monthly opioid increased patients' odds to engage in risk behaviors by a factor of 10 or more and prescribing two or more benzodiazepines annually increased the odds at least 13-fold. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: About one-fourth of all patients consuming opioids engaged in one or more risk behaviors; higher number of opioid prescriptions and addition of even a small number of benzodiazepine prescriptions dramatically increased these odds. PDMPs can be helpful in identifying opioid users at high-risk for misuse. This information could be used to target efforts to reduce the prescription drug epidemic. (Am J Addict 2017;26:822-829).

Prescription drug monitoring program data tracking of opioid addiction treatment outcomes in integrated dual diagnosis care involving injectable naltrexone.

BACKGROUND AND OBJECTIVES: Fourfold increases in opioid prescribing and dispensations over 2 decades in the U.S. has paralleled increases in opioid addictions and overdoses, requiring new preventative, diagnostic, and treatment strategies. This study examines Prescription Drug Monitoring Program (PDMP) tracking as a novel measure of opioid addiction treatment outcomes in a university-affiliated integrated mental health-addiction treatment clinic. METHODS: Repeated measure parametrics examined PDMP and urine drug screening (UDS) data before and after first injection for all patients (N = 68) who received at least one long-acting naltrexone injection (380 mg/IM) according to diagnostic groupings of having either (i) alcohol (control); (ii) opioid; or (iii) combined alcohol and opioid use disorders. RESULTS: There were no group differences post-injection in treatment days, injections delivered, or treatment service encounters. UDS and PDMP measures of opioid exposures were greater in opioid compared to alcohol-only patients. Post-first injection, UDS's positive for opioids declined (p < .05) along with PDMP measures of opioid prescriptions (p < .001), doses (p < .01), types (p < .001), numbers of dispensing prescribers (p < .001) and pharmacies (p < .001). Opioid patients without alcohol disorders showed the best outcomes with 50% to 80% reductions in PDMP-measures of opioids, down to levels of alcohol-only patients. CONCLUSIONS: This study shows PDMP utility for measuring opioid addiction treatment outcomes, supporting the routine use of PDMPs in clinical and research settings. SCIENTIFIC SIGNIFICANCE: These findings demonstrate that opioid addiction in patients with complex addictions and mental illnesses comorbidities can show effective treatment responses as measured by PDMP tracking of decreases in opioid prescriptions to those patients. (Am J Addict 2016;25:557-564).

Pseudoaddiction: Fact or Fiction? An Investigation of the Medical Literature.

Tremendous growth in opioid prescribing over two decades in the USA has correlated with proportional increases in diversion, addiction, and overdose deaths. Pseudoaddiction, a concept coined in 1989, has frequently been cited to indicate that under-treatment of pain, rather than addiction, is the more pressing and authentic clinical problem in opioid-seeking patients. This investigative review searched Medline articles containing the term "pseudoaddiction" to determine its footprint in the literature with a focus on how it has been characterized and empirically validated. By 2014, pseudoaddiction was discussed in 224 articles. Only 18 of these articles contributed to or questioned pseudoaddiction from an anecdotal or theoretical standpoint, and none empirically tested or confirmed its existence. Twelve of these articles, including all four that acknowledged pharmaceutical funding, were proponents of pseudoaddiction. These papers described pseudoaddiction as an iatrogenic disease resulting from withholding opioids for pain that can be diagnosed, prevented, and treated with more aggressive opioid treatment. In contrast, six articles, none with pharmaceutical support, questioned pseudoaddiction as a clinical construct. Empirical evidence supporting pseudoaddiction as a diagnosis distinct from addiction has not emerged. Nevertheless, the term has been accepted and proliferated in the literature as a justification for opioid therapy for non-terminal pain in patients who may appear to be addicted but should not, from the perspective of pseudoaddiction, be diagnosed with addiction. Future studies should examine whether acceptance of pseudoaddiction has complicated accurate pain assessment and treatment, and whether it has contributed to or reflected medical-cultural shifts that produced the iatrogenic opioid addiction epidemic.

Prescription drug monitoring program inquiry in psychiatric assessment: detection of high rates of opioid prescribing to a dual diagnosis population.

OBJECTIVE: An epidemic of prescription drug abuse is disproportionately impacting the mentally ill. We examined the utility of a state prescription drug monitoring database for assessing recent controlled substance prescribing to patients presenting for dual diagnosis treatment. METHOD: In a community mental health center that provides integrated dual diagnosis care, we queried the Indiana Scheduled Prescription Electronic Collection and Tracking (INSPECT) system for all cases that were open as of August 2, 2011, and had been practitioner-diagnosed (per DSM-IV criteria) by January 2, 2012. INSPECT provided a record of controlled substance dispensations to each patient; diagnostic evaluation was conducted blind from prescription data compilation covering the prior 12 months. Demographic data, insurance status, and DSM-IV diagnoses were compiled from the clinic's electronic medical record. RESULTS: The sample (N = 201) was 51% female, 56% white, and two-thirds uninsured. Over 80% were dually diagnosed with substance use disorders and psychotic, mood, or anxiety disorders. Nicotine and alcohol disorders were identified in most, with about a third diagnosed with cannabis, cocaine, or opioid disorders. A majority of patients (n = 115) had been prescribed opioids in the prior year, with nearly 1 in 5 prescribed an opioid and benzodiazepine simultaneously. Patients were dispensed a mean of 4 opioid prescriptions and 213 opioid pills. More opioid prescriptions correlated with opioid dependence (OR = 1.08; 95% CI, 1.016-1.145), and more prescribers correlated with personality disorder diagnoses (OR = 1.112; 95% CI, 1.001-1.235). Higher rates and riskier patterns of controlled substance prescribing were identified in patients with Medicaid/Medicare insurance compared to uninsured patients. CONCLUSIONS: Prescription drug monitoring is a powerful tool for assessing addictions and high frequencies of patient exposures to prescribed opioids in a dual diagnosis clinic. Improved prevention and treatment strategies for addictions as facilitated by more research and clinical use of prescription drug monitoring in psychiatric care are warranted.

The iatrogenic epidemic of prescription drug abuse: county-level determinants of opioid availability and abuse.

BACKGROUND: Opioid use and abuse in the United States continues to expand at an alarming rate. In this study, we examine the county-level determinants of the availability and abuse of prescription opioids to better understand the socio-ecological context, and in particular the role of the healthcare delivery system, on the prescription drug abuse epidemic. METHODS: We use community-level information, data from Indiana's prescription drug monitoring program in 2011, and geospatial regression methods to identify county-level correlates of the availability and abuse of prescription opioids among Indiana's 92 counties. RESULTS: The findings suggest that access to healthcare generally, and to dentists and pharmacists in particular, increases the availability of prescription opioids in communities, which, in turn, is associated with higher rates of opioid abuse. CONCLUSIONS: The results suggest that the structure of the local healthcare system is a major determinant of community-level access to opioids adding to a growing body of evidence that the problem of prescription opioid abuse is, at least in part, an "iatrogenic epidemic."