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Wisdom of the CROUD: Development and validation of a patient-level prediction model for opioid use disorder using population-level claims data.

ABSTRACT: OBJECTIVE:Some patients who are given opioids for pain could develop opioid use disorder. If it was possible to identify patients who are at a higher risk of opioid use disorder, then clinicians could spend more time educating these patients about the risks. We develop and validate a model to predict a person's future risk of opioid use disorder at the point before being dispensed their first opioid. METHODS:A cohort study patient-level prediction using four US claims databases with target populations ranging between 343,552 and 384,424 patients. The outcome was recorded diagnosis of opioid abuse, dependency or unspecified drug abuse as a proxy for opioid use disorder from 1 day until 365 days after the first opioid is dispensed. We trained a regularized logistic regression using candidate predictors consisting of demographics and any conditions, drugs, procedures or visits prior to the first opioid. We then selected the top predictors and created a simple 8 variable score model. RESULTS:We estimated the percentage of new users of opioids with reported opioid use disorder within a year to range between 0.04%-0.26% across US claims data. We developed an 8 variable Calculator of Risk for Opioid Use Disorder (CROUD) score, derived from the prediction models to stratify patients into higher and lower risk groups. The 8 baseline variables were age 15-29, medical history of substance abuse, mood disorder, anxiety disorder, low back pain, renal impairment, painful neuropathy and recent ER visit. 1.8% of people were in the high risk group for opioid use disorder and had a score > = 23 with the model obtaining a sensitivity of 13%, specificity of 98% and PPV of 1.14% for predicting opioid use disorder. CONCLUSIONS:CROUD could be used by clinicians to obtain personalized risk scores. CROUD could be used to further educate those at higher risk and to personalize new opioid dispensing guidelines such as urine testing. Due to the high false positive rate, it should not be used for contraindication or to restrict utilization.

SUBMITTER: Reps JM

PROVIDER: S-EPMC7017997 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Objective(s)To understand how contraception method use differed between women prescribed and not prescribed medications for opioid use disorder (MOUD) among commercially-insured and Medicaid-insured women.Study designIBM Watson Health MarketScan Commercial Claims and Encounters database and the Multi-State Medicaid database were used to calculate the (1) crude prevalence, and (2) adjusted odds ratios (adjusted for demographic characteristics) of using long-acting reversible or short-acting hormonal contraception methods or female sterilization compared with none of these methods (no method) in 2018 by MOUD status among women with OUD, aged 20 to 49 years, with continuous health insurance coverage through commercial insurance or Medicaid for ≥6 years. Claims data was used to define contraception use. Fisher exact test or χ2 test with a P-value ≤ 0.0001, based on the Holm-Bonferroni method, and 95% confidence intervals were used to determine statistically significant differences for prevalence estimates and adjusted odds ratios, respectively.ResultsOnly 41% of commercially-insured and Medicaid-insured women with OUD were prescribed MOUD. Medicaid-insured women with OUD prescribed MOUD had a significantly lower crude prevalence of using no method (71.1% vs 79.0%) and higher odds of using female sterilization (aOR, 1.33; 95% CI: 1.06-1.67 vs no method) than those not prescribed MOUD. Among commercially-insured women there were no differences in contraceptive use by MOUD status and 66% used no method.ConclusionsAmong women with ≥ 6 years of continuous insurance coverage, contraceptive use differed by MOUD status and insurance. Prescribing MOUD for women with OUD can be improved to ensure quality care.ImplicationsOnly two in five women with OUD had evidence of being prescribed MOUD, and majority did not use prescription contraception or female sterilization. Our findings support opportunities to improve prescribing for MOUD and integrate contraception and MOUD services to improve clinical care among women with OUD.

Project description:IntroductionUS claims data contain medical data on large heterogeneous populations and are excellent sources for medical research. Some claims data do not contain complete death records, limiting their use for mortality or mortality-related studies. A model to predict whether a patient died at the end of the follow-up time (referred to as the end of observation) is needed to enable mortality-related studies.ObjectiveThe objective of this study was to develop a patient-level model to predict whether the end of observation was due to death in US claims data.MethodsWe used a claims dataset with full death records, Optum© De-Identified Clinformatics® Data-Mart-Database-Date of Death mapped to the Observational Medical Outcome Partnership common data model, to develop a model that classifies the end of observations into death or non-death. A regularized logistic regression was trained using 88,514 predictors (recorded within the prior 365 or 30 days) and externally validated by applying the model to three US claims datasets.ResultsApproximately 25 in 1000 end of observations in Optum are due to death. The Discriminating End of observation into Alive and Dead (DEAD) model obtained an area under the receiver operating characteristic curve of 0.986. When defining death as a predicted risk of?>?0.5, only 2% of the end of observations were predicted to be due to death and the model obtained a sensitivity of 62% and a positive predictive value of 74.8%. The external validation showed the model was transportable, with area under the receiver operating characteristic curves ranging between 0.951 and 0.995 across the US claims databases.ConclusionsUS claims data often lack complete death records. The DEAD model can be used to impute death at various sensitivity, specificity, or positive predictive values depending on the use of the model. The DEAD model can be readily applied to any observational healthcare database mapped to the Observational Medical Outcome Partnership common data model and is available from https://github.com/OHDSI/StudyProtocolSandbox/tree/master/DeadModel .

Project description:BackgroundPrevention and management of chronic diseases are the main goals of national health maintenance programs. Previously widely used screening tools, such as Health Risk Appraisal, are restricted in their achievement this goal due to their limitations, such as static characteristics, accessibility, and generalizability. Hypertension is one of the most important chronic diseases requiring management via the nationwide health maintenance program, and health care providers should inform patients about their risks of a complication caused by hypertension.ObjectiveOur goal was to develop and compare machine learning models predicting high-risk vascular diseases for hypertensive patients so that they can manage their blood pressure based on their risk level.MethodsWe used a 12-year longitudinal dataset of the nationwide sample cohort, which contains the data of 514,866 patients and allows tracking of patients' medical history across all health care providers in Korea (N=51,920). To ensure the generalizability of our models, we conducted an external validation using another national sample cohort dataset, comprising one million different patients, published by the National Health Insurance Service. From each dataset, we obtained the data of 74,535 and 59,738 patients with essential hypertension and developed machine learning models for predicting cardiovascular and cerebrovascular events. Six machine learning models were developed and compared for evaluating performances based on validation metrics.ResultsMachine learning algorithms enabled us to detect high-risk patients based on their medical history. The long short-term memory-based algorithm outperformed in the within test (F1-score=.772, external test F1-score=.613), and the random forest-based algorithm of risk prediction showed better performance over other machine learning algorithms concerning generalization (within test F1-score=.757, external test F1-score=.705). Concerning the number of features, in the within test, the long short-term memory-based algorithms outperformed regardless of the number of features. However, in the external test, the random forest-based algorithm was the best, irrespective of the number of features it encountered.ConclusionsWe developed and compared machine learning models predicting high-risk vascular diseases in hypertensive patients so that they may manage their blood pressure based on their risk level. By relying on the prediction model, a government can predict high-risk patients at the nationwide level and establish health care policies in advance.

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Wisdom of the CROUD: Development and validation of a patient-level prediction model for opioid use disorder using population-level claims data.

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