Observational study to calculate addictive risk to opioids: a validation study of a predictive algorithm to evaluate opioid use disorder.
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ABSTRACT:
SUBMITTER: Brenton A
PROVIDER: S-EPMC5441670 | biostudies-literature | 2017
REPOSITORIES: biostudies-literature
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Observational study to calculate addictive risk to opioids: a validation study of a predictive algorithm to evaluate opioid use disorder.
Brenton Ashley A Richeimer Steven S Sharma Maneesh M Lee Chee C Kantorovich Svetlana S Blanchard John J Meshkin Brian B
Pharmacogenomics and personalized medicine 20170518
<h4>Background</h4>Opioid abuse in chronic pain patients is a major public health issue, with rapidly increasing addiction rates and deaths from unintentional overdose more than quadrupling since 1999.<h4>Purpose</h4>This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm incorporating phenotypic risk factors and neuroscience-associated single-nucleotide polymorphisms (SNPs).<h4>Patients and methods</h4>The Proove Opioid Risk (POR) ...[more]
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Project description:BACKGROUND:Opioid abuse in chronic pain patients is a major public health issue. Primary care providers are frequently the first to prescribe opioids to patients suffering from pain, yet do not always have the time or resources to adequately evaluate the risk of opioid use disorder (OUD). PURPOSE:This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm ("profile") incorporating phenotypic and, more uniquely, genotypic risk factors. METHODS AND RESULTS:In a validation study with 452 participants diagnosed with OUD and 1237 controls, the algorithm successfully categorized patients at high and moderate risk of OUD with 91.8% sensitivity. Regardless of changes in the prevalence of OUD, sensitivity of the algorithm remained >90%. CONCLUSION:The algorithm correctly stratifies primary care patients into low-, moderate-, and high-risk categories to appropriately identify patients in need for additional guidance, monitoring, or treatment changes.
| S-EPMC5574481 | biostudies-literature
Project description:PurposeThe purpose of this study was to determine the clinical utility of an algorithm-based decision tool designed to assess risk associated with opioid use. Specifically, we sought to assess how physicians were using the profile in patient care and how its use affected patient outcomes.Patients and methodsA prospective, longitudinal study was conducted to assess the utility of precision medicine testing in 5,397 patients across 100 clinics in the USA. Using a patent-protected, validated algorithm combining specific genetic risk factors with phenotypic traits, patients were categorized into low-, moderate-, and high-risk patients for opioid abuse. Physicians who ordered precision medicine testing were asked to complete patient evaluations and document their actions, decisions, and perceptions regarding the utility of the precision medicine tests. The patient outcomes associated with each treatment action were carefully documented.ResultsPhysicians used the profile to guide treatment decisions for over half of the patients. Of those, guided treatment decisions for 24.5% of the patients were opioid related, including changing the opioid prescribed, starting an opioid, or titrating a patient off the opioid. Treatment guidance was strongly influenced by profile-predicted opioid use disorder (OUD) risk. Most importantly, patients whose physicians used the profile to guide opioid-related treatment decisions had improved clinical outcomes, including better pain management by medication adjustments, with an average pain decrease of 3.4 points on a scale of 1-10.ConclusionPatients whose physicians used the profile to guide opioid-related treatment decisions had improved clinical outcomes, as measured by decreased pain levels resulting from better pain management with prescribed medications. The clinical utility of the profile is twofold. It provides clinically actionable recommendations that can be used to 1) prevent OUD through limiting initial opioid prescriptions and 2) reduce pain in patients at low risk of developing OUD.
| S-EPMC5759857 | biostudies-literature
Project description:BackgroundSmartphone-based emergency response apps are increasingly being used to identify and dispatch volunteer first responders (VFRs) to medical emergencies to provide faster first aid, which is associated with better prognoses. Volunteers' availability and willingness to respond are uncertain, leading in recent studies to response rates of 17% to 47%. Dispatch algorithms that select volunteers based on their estimated time of arrival (ETA) without considering the likelihood of response may be suboptimal due to a large percentage of alerts wasted on VFRs with shorter ETA but a low likelihood of response, resulting in delays until a volunteer who will actually respond can be dispatched.ObjectiveThis study aims to improve the decision-making process of human emergency medical services dispatchers and autonomous dispatch algorithms by presenting a novel approach for predicting whether a VFR will respond to or ignore a given alert.MethodsWe developed and compared 4 analytical models to predict VFRs' response behaviors based on emergency event characteristics, volunteers' demographic data and previous experience, and condition-specific parameters. We tested these 4 models using 4 different algorithms applied on actual demographic and response data from a 12-month study of 112 VFRs who received 993 alerts to respond to 188 opioid overdose emergencies. Model 4 used an additional dynamically updated synthetic dichotomous variable, frequent responder, which reflects the responder's previous behavior.ResultsThe highest accuracy (260/329, 79.1%) of prediction that a VFR will ignore an alert was achieved by 2 models that used events data, VFRs' demographic data, and their previous response experience, with slightly better overall accuracy (248/329, 75.4%) for model 4, which used the frequent responder indicator. Another model that used events data and VFRs' previous experience but did not use demographic data provided a high-accuracy prediction (277/329, 84.2%) of ignored alerts but a low-accuracy prediction (153/329, 46.5%) of responded alerts. The accuracy of the model that used events data only was unacceptably low. The J48 decision tree algorithm provided the best accuracy.ConclusionsVFR dispatch has evolved in the last decades, thanks to technological advances and a better understanding of VFR management. The dispatch of substitute responders is a common approach in VFR systems. Predicting the response behavior of candidate responders in advance of dispatch can allow any VFR system to choose the best possible response candidates based not only on ETA but also on the probability of actual response. The integration of the probability to respond into the dispatch algorithm constitutes a new generation of individual dispatch, making this one of the first studies to harness the power of predictive analytics for VFR dispatch. Our findings can help VFR network administrators in their continual efforts to improve the response times of their networks and to save lives.
| S-EPMC10716760 | biostudies-literature
Project description:BackgroundThe USA is in the midst of an opioid overdose epidemic. To address the epidemic, we conducted a large-scale population study on opioid overdose.ObjectivesThe primary objective of this study was to evaluate the temporal trends and risk factors of inpatient opioid overdose. Based on its patterns, the secondary objective was to examine the innate properties of opioid analgesics underlying reduced overdose effects.MethodsA retrospective cross-sectional study was conducted based on a large-scale inpatient electronic health records database, Cerner Health Facts®, with (1) inclusion criteria for participants as patients admitted between 1 January, 2009 and 31 December, 2017 and (2) measurements as opioid overdose prevalence by year, demographics, and prescription opioid exposures.ResultsA total of 4,720,041 patients with 7,339,480 inpatient encounters were retrieved from Cerner Health Facts®. Among them, 30.2% patients were aged 65+ years, 57.0% female, 70.1% Caucasian, 42.3% single, 32.0% from the South, and 80.8% in an urban area. From 2009 to 2017, annual opioid overdose prevalence per 1000 patients significantly increased from 3.7 to 11.9 with an adjusted odds ratio (aOR): 1.16, 95% confidence interval (CI) 1.15-1.16. Compared to the major demographic counterparts, being in (1) age group: 41-50 years (overall aOR 1.36, 95% CI 1.31-1.40) or 51-64 years (overall aOR 1.35, 95% CI 1.32-1.39), (2) marital status: divorced (overall aOR 1.19, 95% CI 1.15-1.23), and (3) census region: West (overall aOR 1.32, 95% CI 1.28-1.36) were significantly associated with a higher odds of opioid overdose. Prescription opioid exposures were also associated with an increased odds of opioid overdose, such as meperidine (overall aOR 1.09, 95% CI 1.06-1.13) and tramadol (overall aOR 2.20, 95% CI 2.14-2.27). Examination on the relationships between opioid analgesic properties and their association strengths, aORs, and opioid overdose showed that lower aOR values were significantly associated with (1) high molecular weight, (2) non-interaction with multi-drug resistance protein 1 or interaction with cytochrome P450 3A4, and (3) non-interaction with the delta opioid receptor or kappa opioid receptor.ConclusionsThe significant increasing trends of opioid overdose at the inpatient care setting from 2009 to 2017 suggested an ongoing need for efforts to combat the opioid overdose epidemic in the USA. Risk factors associated with opioid overdose included patient demographics and prescription opioid exposures. Moreover, there are physicochemical, pharmacokinetic, and pharmacodynamic properties underlying reduced overdose effects, which can be utilized to develop better opioids.
| S-EPMC8324607 | biostudies-literature
Project description:Mesoscale eddies stir along the neutral plane, and the resulting neutral diffusion is a fundamental aspect of subgrid-scale tracer transport in ocean models. Calculating neutral diffusion traditionally involves calculating neutral slopes and three-dimensional tracer gradients. The calculation of the neutral slope traditionally occurs by computing the ratio of the horizontal to vertical locally referenced potential density derivative. However, this approach is problematic in regions of weak vertical stratification, prompting the use of a variety of ad hoc regularization methods that can lead to rather nonphysical dependencies for the resulting neutral tracer gradients. Here we use a VErtical Non-local Method "VENM," a search algorithm that requires no ad hoc regularization and significantly improves the numerical accuracy of calculating neutral slopes, neutral tracer gradients, and associated neutral diffusive fluxes. We compare and contrast VENM against a more traditional method, using an independent objective neutrality condition combined with estimates of spurious diffusion, heat transport, and water mass transformation rates. VENM is more accurate, both physically and numerically, and should form the basis for future efforts involving neutral diffusion calculations from observations and possibly numerical model simulations.
| S-EPMC6774288 | biostudies-literature
Project description:BackgroundHeatwaves could cause the population excess death numbers to be ranged from tens to thousands within a couple of weeks in a local area. An excess mortality due to a special event (e.g., a heatwave or an epidemic outbreak) is estimated by subtracting the mortality figure under 'normal' conditions from the historical daily mortality records. The calculation of the excess mortality is a scientific challenge because of the stochastic temporal pattern of the daily mortality data which is characterised by (a) the long-term changing mean levels (i.e., non-stationarity); (b) the non-linear temperature-mortality association. The Hilbert-Huang Transform (HHT) algorithm is a novel method originally developed for analysing the non-linear and non-stationary time series data in the field of signal processing, however, it has not been applied in public health research. This paper aimed to demonstrate the applicability and strength of the HHT algorithm in analysing health data.MethodsSpecial R functions were developed to implement the HHT algorithm to decompose the daily mortality time series into trend and non-trend components in terms of the underlying physical mechanism. The excess mortality is calculated directly from the resulting non-trend component series.ResultsThe Brisbane (Queensland, Australia) and the Chicago (United States) daily mortality time series data were utilized for calculating the excess mortality associated with heatwaves. The HHT algorithm estimated 62 excess deaths related to the February 2004 Brisbane heatwave. To calculate the excess mortality associated with the July 1995 Chicago heatwave, the HHT algorithm needed to handle the mode mixing issue. The HHT algorithm estimated 510 excess deaths for the 1995 Chicago heatwave event. To exemplify potential applications, the HHT decomposition results were used as the input data for a subsequent regression analysis, using the Brisbane data, to investigate the association between excess mortality and different risk factors.ConclusionsThe HHT algorithm is a novel and powerful analytical tool in time series data analysis. It has a real potential to have a wide range of applications in public health research because of its ability to decompose a nonlinear and non-stationary time series into trend and non-trend components consistently and efficiently.
| S-EPMC3946180 | biostudies-literature
Project description:ASC-G4 is an algorithm for the calculation of the advanced structural characteristics of G-quadruplexes (G4). It allows the unambiguous determination of the intramolecular G4 topology, based on the oriented strand numbering. It also resolves the ambiguity in the determination of the guanine glycosidic configuration. With this algorithm, we showed that the use of the C3' or C5' atoms to calculate the groove width in G4 is more appropriate than the P atoms and that the groove width does not always reflect the space available within the groove. For the latter, the minimum groove width is more appropriate. The application of ASC-G4 to 207 G4 structures guided the choices made for the calculations. A website based on ASC-G4 (http://tiny.cc/ASC-G4) was created, where the user uploads his G4 structure and gets its topology, the types of its loops and their lengths, the presence of snapbacks and bulges, the distribution of guanines in the tetrads and strands, the glycosidic configuration of these guanines, their rise, the groove widths, the minimum groove widths, the tilt and twist angles, the backbone dihedral angles, etc. It also provides a large number of atom-atom and atom-plane distances that are relevant to evaluating the quality of the structure.
| S-EPMC10018348 | biostudies-literature
Project description:ObjectiveTo examine the associations between stopping treatment with opioids, length of treatment, and death from overdose or suicide in the Veterans Health Administration.DesignObservational evaluation.SettingVeterans Health Administration.Participants1 394 102 patients in the Veterans Health Administration with an outpatient prescription for an opioid analgesic from fiscal year 2013 to the end of fiscal year 2014 (1 October 2012 to 30 September 2014).Main outcome measuresA multivariable Cox non-proportional hazards regression model examined death from overdose or suicide, with the interaction of time varying opioid cessation by length of treatment (≤30, 31-90, 91-400, and >400 days) as the main covariates. Stopping treatment with opioids was measured as the time when a patient was estimated to have no prescription for opioids, up to the end of the next fiscal year (2014) or the patient's death.Results2887 deaths from overdose or suicide were found. The incidence of stopping opioid treatment was 57.4% (n=799 668) overall, and based on length of opioid treatment was 32.0% (≤30 days), 8.7% (31-90 days), 22.7% (91-400 days), and 36.6% (>400 days). The interaction between stopping treatment with opioids and length of treatment was significant (P<0.001); stopping treatment was associated with an increased risk of death from overdose or suicide regardless of the length of treatment, with the risk increasing the longer patients were treated. Hazard ratios for patients who stopped opioid treatment (with reference values for all other covariates) were 1.67 (≤30 days), 2.80 (31-90 days), 3.95 (91-400 days), and 6.77 (>400 days). Descriptive life table data suggested that death rates for overdose or suicide increased immediately after starting or stopping treatment with opioids, with the incidence decreasing over about three to 12 months.ConclusionsPatients were at greater risk of death from overdose or suicide after stopping opioid treatment, with an increase in the risk the longer patients had been treated before stopping. Descriptive data suggested that starting treatment with opioids was also a risk period. Strategies to mitigate the risk in these periods are not currently a focus of guidelines for long term use of opioids. The associations observed cannot be assumed to be causal; the context in which opioid prescriptions were started and stopped might contribute to risk and was not investigated. Safer prescribing of opioids should take a broader view on patient safety and mitigate the risk from the patient's perspective. Factors to address are those that place patients at risk for overdose or suicide after beginning and stopping opioid treatment, especially in the first three months.
| S-EPMC7249243 | biostudies-literature
Project description:Background. In a previous publication we introduced a novel approach to identify genes that hold predictive information about treatment outcome. A linear regression model was fitted by using the least angle regression algorithm (LARS) with the expression profiles of a construction set of 18 glioma progenitor cells enhanced for brain tumor initiating cells (BTIC) before and after in vitro treatment with the tyrosine kinase inhibitor Sunitinib. Profiles from treated progenitor cells allowed predicting therapy-induced impairment of proliferation in vitro. Prediction performance was validated in leave one out cross validation. Methods. In this study, we used an additional validation set of 18 serum-free short-term treated in vitro cell cultures to test the predictive properties of the signature in an independent cohort. We assessed proliferation rates together with transcriptome-wide expression profiles after Sunitinib treatment of each individual cell culture, following the methods of the previous publication. Results. We confirmed treatment-induced expression changes in our validation set, but our signature failed to predict proliferation inhibition. Conclusion. Although the gene signature published from our construction set exhibited good prediction accuracy in cross validation, we were not able to validate the signature in an independent validation data set. Reasons could be regression to the mean, the moderate numbers of samples, or too low differences in the response to proliferation inhibition. At this stage and based on the presented results, we conclude that the signature does not warrant further developmental steps towards clinical application.
2016-02-21 | GSE76990 | GEO
Project description:PURPOSE:A standardized definition for serious opioid overdose has not been clearly established for disease surveillance or assessing the impact of risk mitigation strategies. The purpose of this study was to use medical chart review to clinically validate a claims-based algorithm to identify serious opioid overdose events. METHODS:The algorithm for serious opioid overdose required an opioid poisoning or external cause ICD-9-CM code occurring within 1 day of (a) an adverse effect code for serious central nervous system or respiratory depression or (b) a mechanical ventilation or critical care CPT code. The claims coding algorithm identified a sample of 145 individuals 18 years or older among patients that presented to the emergency department of two large hospitals in metropolitan Atlanta, Georgia from January 2014 to August 2015. Claims-defined cases were evaluated against rigorous clinical definitions for serious opioid overdose using (a) literature-based criteria for typical clinical manifestations of opioid overdose and/or (b) clinical response to the opioid-specific reversal agent naloxone. The positive predictive value (PPV) for a serious opioid overdose was calculated as the percentage of clinically confirmed cases (definite or probable). RESULTS:Among 140 evaluable claims-defined cases, 107 fulfilled clinical criteria for a serious opioid overdose [95 definite and 12 probable; PPV of 76.4% (95% CI 69.4%, 83.5%)]. Among 30 nonconfirmed cases, 20 were polyintoxications involving one or more nonopioid psychoactive agents. CONCLUSIONS:An administrative claims coding algorithm for serious opioid overdose had high clinical predictive performance in a medical chart review.
| S-EPMC6810754 | biostudies-literature