Predicting high-risk opioid prescriptions before they are given.
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ABSTRACT:
SUBMITTER: Hastings JS
PROVIDER: S-EPMC6994994 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ImportanceCurrent approaches to identifying individuals at high risk for opioid overdose target many patients who are not truly at high risk.ObjectiveTo develop and validate a machine-learning algorithm to predict opioid overdose risk among Medicare beneficiaries with at least 1 opioid prescription.Design, setting, and participantsA prognostic study was conducted between September 1, 2017, and December 31, 2018. Participants (n = 560 057) included fee-for-service Medicare beneficiaries without cancer who filled 1 or more opioid prescriptions from January 1, 2011, to December 31, 2015. Beneficiaries were randomly and equally divided into training, testing, and validation samples.ExposuresPotential predictors (n = 268), including sociodemographics, health status, patterns of opioid use, and practitioner-level and regional-level factors, were measured in 3-month windows, starting 3 months before initiating opioids until loss of follow-up or the end of observation.Main outcomes and measuresOpioid overdose episodes from inpatient and emergency department claims were identified. Multivariate logistic regression (MLR), least absolute shrinkage and selection operator-type regression (LASSO), random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN) were applied to predict overdose risk in the subsequent 3 months after initiation of treatment with prescription opioids. Prediction performance was assessed using the C statistic and other metrics (eg, sensitivity, specificity, and number needed to evaluate [NNE] to identify one overdose). The Youden index was used to identify the optimized threshold of predicted score that balanced sensitivity and specificity.ResultsBeneficiaries in the training (n = 186 686), testing (n = 186 685), and validation (n = 186 686) samples had similar characteristics (mean [SD] age of 68.0 [14.5] years, and approximately 63% were female, 82% were white, 35% had disabilities, 41% were dual eligible, and 0.60% had at least 1 overdose episode). In the validation sample, the DNN (C statistic = 0.91; 95% CI, 0.88-0.93) and GBM (C statistic = 0.90; 95% CI, 0.87-0.94) algorithms outperformed the LASSO (C statistic = 0.84; 95% CI, 0.80-0.89), RF (C statistic = 0.80; 95% CI, 0.75-0.84), and MLR (C statistic = 0.75; 95% CI, 0.69-0.80) methods for predicting opioid overdose. At the optimized sensitivity and specificity, DNN had a sensitivity of 92.3%, specificity of 75.7%, NNE of 542, positive predictive value of 0.18%, and negative predictive value of 99.9%. The DNN classified patients into low-risk (76.2% [142 180] of the cohort), medium-risk (18.6% [34 579] of the cohort), and high-risk (5.2% [9747] of the cohort) subgroups, with only 1 in 10 000 in the low-risk subgroup having an overdose episode. More than 90% of overdose episodes occurred in the high-risk and medium-risk subgroups, although positive predictive values were low, given the rare overdose outcome.Conclusions and relevanceMachine-learning algorithms appear to perform well for risk prediction and stratification of opioid overdose, especially in identifying low-risk subgroups that have minimal risk of overdose.
| S-EPMC6583312 | biostudies-literature
Project description:ImportanceAs opioid-related mortality continues to increase, naloxone remains a critical intervention in preventing overdose death. Opportunities to expand access through the health care setting should be optimized.ObjectiveTo determine the characteristics of naloxone prescribing for US patients at high risk of opioid overdose.Design, setting, and participantsThis retrospective cohort study used Truven Health MarketScan data from October 1, 2015, through December 31, 2016, of individuals with International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes related to opioid use, misuse, dependence, and overdose. The cohort included 138 108 commercially insured individuals aged 15 years or older in the United States with claims related to opioid misuse or dependence, opioid-related overdose, or both.ExposuresOutpatient naloxone pharmacy claims.Main outcomes and measuresDemographic characteristics, clinical characteristics, health care service use, and proportion prescribed naloxone were included in multivariable logistic regression analyses to test the association of opioid risk group with naloxone claim.ResultsOf 138 108 high-risk individuals (mean [SD] age, 43.4 [0.4] years; 72 435 [52.4%] men), 2135 (1.5%) were prescribed naloxone. Having prior diagnoses of both opioid misuse or dependence and overdose was associated with a greater likelihood of receiving naloxone (odds ratio [OR], 2.32; 95% CI, 1.98-2.72; P < .001) compared with having a prior diagnosis of opioid misuse or dependence without overdose. Having a prior diagnosis of opioid overdose alone was associated with a decreased likelihood of receiving naloxone (OR, 0.73; 95% CI, 0.57-0.94; P = .01) compared with having a prior diagnosis of opioid misuse or dependence without overdose. Factors associated with lower naloxone prescription included being aged 30 to 44 years (OR, 0.72; 95% CI, 0.62-0.84; P < .001) and being from the Midwest (OR, 0.62; 95% CI, 0.54-0.71; P < .001) or West (OR, 0.85; 95% CI, 0.74-0.98; P = .03). Opioid use disorder treatment, such as use of medication-assisted therapy (OR, 1.68; 95% CI, 1.53-1.86; P < .001), visiting a detoxification facility (OR, 1.51; 95% CI, 1.31-1.76; P < .001), or receiving other substance use disorder treatment (OR, 1.16; 95% CI, 1.04-1.30; P = .01), were associated with increased likelihood of receiving naloxone, as were receiving outpatient care from a pain specialist (OR, 1.57; 95% CI, 1.40-1.76; P < .001), psychologist (OR, 1.49; 95% CI, 1.29-1.70; P < .001), or surgeon (OR, 1.19; 95% CI, 1.08-1.32; P < .001). Overall, 98.5% (n = 135 973) of high-risk patients did not received naloxone, despite many interactions with the health care system, including 88 618 hospitalizations, 229 680 emergency department visits, 298 058 internal medicine visits, and 568 448 family practice visits.Conclusions and relevancePatients at high risk of opioid overdose rarely received prescriptions for naloxone despite numerous interactions with the health care system. Prescribing in emergency, inpatient, and outpatient settings represents an opportunity to improve access.
| S-EPMC6503491 | biostudies-literature
Project description:BACKGROUND:Opioids and multimodal analgesia are widely administered to manage postoperative pain. However, little is known on how improvements in inpatient pain control are correlated with high-risk (> 90 daily OME) discharge opioid prescriptions for opioid naïve surgical patients. METHODS:We conducted a retrospective observational study of adult opioid-naïve patients undergoing surgery from June 2012 through December 2018 at a large academic medical center. We used multivariate logistic regression to assess whether multimodal analgesic drugs consumed in the 24 h prior to discharge was associated with a reduction in high-risk opioid discharge prescriptions. We identified other risk factors for receiving a high-risk discharge opioid prescription. RESULTS:Among the 32,511 patients, 83% of patients were discharged with an opioid prescription. In 2013, 34.1% of patients with a discharge opioid prescription received a high-risk prescription and this declined to 17.7% by 2018. Use of multimodal analgesic agents during the final 24 h of hospitalization increased each year, with over 80% receiving at least one multimodal analgesic agent by 2018. The median OME consumed in the 24 h prior to discharge peaked in 2013 at 31 and steadily decreased to 19.8 by 2018. There was a significant association between the use of acetaminophen in the 24 h prior to discharge and a high-risk prescription at discharge (p < 0.01). OMEs consumed in the 24 h prior to discharge was a significant predictor of receiving a high-risk discharge prescription, even at low doses. Other factors associated with receipt of a high-risk discharge opioid prescription included male gender, race, history of anxiety disorder, and discharge service. DISCUSSION:Use of multimodal analgesia regimens in hospitalized surgical patients in the 24 h prior to hospital discharge increased between 2012 and 2018. Simultaneously, opioid use prior to hospital discharge decreased. Despite these gains, approximately one in five discharge prescriptions was high-risk (> 90 daily OME). In addition, we found that prescribing of discharge opioids above inpatient opioid requirements remains common in opioid naive surgical patients. CONCLUSION:Providers should account for pre-discharge opioid consumption and use of multimodal analgesia when considering the total and daily OME's that may be appropriate for an individual surgical patient on the discharge opioid prescription.
| S-EPMC8672612 | biostudies-literature
Project description:ImportanceThe COVID-19 pandemic substantially disrupted routine health care and exacerbated existing barriers to health care access. Although postpartum women frequently experience pain that interferes with activities of daily living, which is often successfully treated with prescription opioid analgesics, they are also at high risk for opioid misuse.ObjectiveTo compare postpartum opioid prescription fills after the onset of the COVID-19 pandemic in March 2020 with fills before the pandemic.Design, setting, and participantsIn this cross-sectional study of 460 371 privately insured postpartum women who delivered a singleton live newborn between July 1, 2018, and December 31, 2020, postpartum opioid fills before March 1, 2020, were compared with fills after March 1, 2020. Statistical analysis was performed from December 1, 2021, to September 15, 2022.ExposureCOVID-19 pandemic onset in March 2020.Main outcomes and measuresThe main outcome was postpartum opioid fills, defined as patient fills of opioid prescriptions during the 6 months after birth. Opioid prescriptions were explored in terms of 5 measures: mean number of fills per person, mean filled morphine milligram equivalents (MMEs) per day, mean days supplied, percentage of patients filling a prescription for a schedule II opioid, and percentage of patients filling a prescription for a schedule III or higher opioid.ResultsAmong 460 371 postpartum women (mean [SD] age at delivery, 29.0 [10.8] years), those who gave birth to a single, live newborn after March 2020 were 2.8 percentage points more likely to fill an opioid prescription than expected based on the preexisting trend (forecasted, 35.0% [95% CI, 34.0%-35.9%]; actual, 37.8% [95% CI, 36.8%-38.7%]). The COVID-19 period was also associated with an increase in MMEs per day (forecasted mean [SD], 34.1 [2.0] [95% CI, 33.6-34.7]; actual mean [SD], 35.8 [1.8] [95% CI, 35.3-36.3]), number of opioid fills per patient (forecasted, 0.49 [95% CI, 0.48-0.51]; actual, 0.54 [95% CI, 0.51-0.55]), and percentage of patients filling a schedule II opioid prescription (forecasted, 28.7% [95% CI, 27.9%-29.6%]; actual, 31.5% [95% CI, 30.6%-32.3%]). There was no significant association with days' suppy of opioids per prescription or percentage of patients filling a prescription for a schedule III or higher opioid. Results stratified by delivery modality showed that the observed increases were larger for patients who delivered by cesarean birth than those delivering vaginally.Conclusions and relevanceThis cross-sectional study suggests that the onset of the COVID-19 pandemic was associated with significant increases in postpartum opioid fills. Increases in opioid prescriptions may be associated with increased risk of opioid misuse, opioid use disorder, and opioid-related overdose among postpartum women.
| S-EPMC10071338 | biostudies-literature
Project description:Policies and practices have proliferated to optimize prescribers' use of their states' prescription drug monitoring programs, which are statewide databases of controlled substances dispensed at retail pharmacies. Our study assessed the effectiveness of three such policies: comprehensive legislative mandates to use the program, laws that allow prescribers to delegate its use to office staff, and state participation in interstate data sharing. Our analysis of information from a large commercial insurance database indicated that comprehensive use mandates implemented during 2011-15 were associated with a 6-9 percent reduction in opioid prescriptions with high risk for misuse and overdose. We also found delegate laws to be associated with reductions of a similar magnitude for selected outcomes. In general, the effects of all three policies strengthened over time, especially beginning in the second year after implementation. Our findings support comprehensive use mandates and delegate laws to optimize prescribers' use of drug monitoring programs, but the results will need updates in the context of evolving state opioid policies-including the increasing integration of drug monitoring data with electronic health records.
| S-EPMC6362993 | biostudies-literature
Project description:IntroductionIt is unknown whether dental opioid prescriptions are associated with opioid overdose in patients or their family members, who may have access to patients' opioids.MethodsDuring July-October 2020, the 2011-2018 IBM MarketScan Dental, IBM MarketScan Commercial, and Medicaid Multi-State Databases were analyzed. Two analyses were conducted. In the patient analysis, dental procedures for privately and publicly insured patients aged 13-64 years were identified. The exposure was ≥1 initial prescription (dispensed opioid prescription within 3 days of the procedure). The association between the exposure and ≥1 overdose within 90 days of the procedure was evaluated using logistic regression. In the family analysis, procedures for privately insured patients in family plans were identified. The association between the exposure and ≥1 overdose in a family member within 90 days was evaluated using logistic regression. In both analyses, the average marginal effect of the exposure was calculated, representing the change in the probability of the outcome if all versus if no procedures were associated with ≥1 initial prescription.ResultsThe patient analysis included 8,544,098 procedures. When ≥1 initial prescription did and did not occur, the 90-day risk of overdose was 5.8 versus 2.2 per 10,000 procedures (average marginal effect=1.5, 95% CI=1.2, 1.8). The family analysis included 3,461,469 procedures. When ≥1 initial prescription did and did not occur, the 90-day risk of overdose in a family member was 1.7 versus 1.0 per 10,000 procedures (average marginal effect=0.4, 95% CI=0.1, 0.7).ConclusionsFindings further highlight the importance of avoiding unnecessary dental opioid prescribing.
| S-EPMC8319034 | biostudies-literature
Project description: Not available
| S-EPMC8725834 | biostudies-literature
Project description: Not available
| S-EPMC9386536 | biostudies-literature
Project description:BackgroundIn 2015, Oregon's Medicaid program implemented a performance improvement project to reduce high-dose opioid prescribing across its 16 coordinated care organizations (CCOs). The objective of this study was to evaluate the effect of that program on prescription opioid use and outcomes.MethodsUsing Medicaid claims data from 2014 to 2017, we conducted interrupted time-series analyses to examine changes in the prescription opioid use and overdose rates before (July 2014 to June 2015) and after (January 2016 to December 2017) implementation of Oregon's high-dose policy initiative (July 2015 to December 2015). Prescribing outcomes were: 1) total opioid prescriptions 2) high-dose [> 90 morphine milligram equivalents per day] opioid prescriptions, and 3) proportion of opioid prescriptions that were high-dose. Opioid overdose outcomes included emergency department visits or hospitalizations that involved an opioid-related poisoning (total, heroin-involved, non-heroin involved). Analyses were performed at the state and CCO level.ResultsThere was an immediate reduction in high dose opioid prescriptions after the program was implemented (- 1.55 prescription per 1000 enrollee; 95% CI - 2.26 to - 0.84; p < 0.01). Program implementation was also associated with an immediate drop (- 1.29 percentage points; 95% CI - 1.94 to - 0.64 percentage points; p < 0.01) and trend reduction (- 0.23 percentage point per month; 95% CI - 0.33 to - 0.14 percentage points; p < 0.01) in the monthly proportion of high-dose opioid prescriptions. The trend in total, heroin-involved, and non-heroin overdose rates increased significantly following implementation of the program.ConclusionsAlthough Oregon's high-dose opioid performance improvement project was associated with declines in high-dose opioid prescriptions, rates of opioid overdose did not decrease. Policy efforts to reduce opioid prescribing risks may not be sufficient to address the growing opioid crisis.
| S-EPMC8760718 | biostudies-literature
Project description:ImportancePractice guidelines recommend nonopioid medications in children after tonsillectomy, but to date, studies have not used recent national data to assess perioperative opioid prescribing patterns or the factors associated with these patterns in this population. Closing this knowledge gap may help in assessing whether such prescribing and prescription duration could be safely reduced.ObjectiveTo assess national perioperative opioid prescribing patterns, clinical and demographic factors associated with these patterns, and association between these patterns and complications in children after tonsillectomy compared with children not using opioids.Design, setting, and participantsThis cohort analysis used the 2016 to 2017 claims data from the database of a large national private insurer in the United States. Opioid-naive children aged 1 to 18 years with a claims code for tonsillectomy with or without adenoidectomy between April 1, 2016, and December 15, 2017, were identified (n = 22 567) and screened against the exclusion criteria. The final sample included 15 793 children.Main outcomes and measuresThe percentage of children with 1 or more perioperative fills (prescription drug claims for opioids between 7 days before to 1 day after tonsillectomy) was calculated, along with the duration of perioperative prescriptions (days supplied). Linear regression was used to identify the demographic and clinical factors associated with the duration of perioperative opioid prescriptions. Logistic regression was used to assess the association between having 1 or more perioperative fills and their duration and the risk of return visits 2 to 14 days after tonsillectomy for pain or dehydration, secondary hemorrhage, and constipation compared with children not using opioids.ResultsAmong 15 793 children, the mean (SD) age was 7.8 (4.2) years, 12 807 (81.1%) were younger than 12 years, 2986 (18.9%) were between 12 and 18 years of age, and 8289 (52.6%) were female. In total, 9411 (59.6%) children had 1 or more perioperative fills, and the median (25th-75th percentile) duration was 8 (6-10) days; 6382 had no perioperative fills. The probability of having 1 or more perioperative fills and the duration of prescription varied across US census divisions. Having 1 or more perioperative fills was not associated with return visits for pain or dehydration (adjusted odds ratio [AOR], 1.13; 95% CI, 0.95-1.34) or secondary hemorrhage (AOR, 0.90; 95% CI, 0.73-1.10) compared with children not using opioids, but it was associated with increased risk of return visits for constipation (AOR, 2.02; 95% CI, 1.24-3.28). Duration was not associated with return visits for complications.Conclusions and relevanceThese findings suggest that reducing perioperative opioid prescribing and the duration of perioperative opioid prescriptions may be possible without increasing the risk of these complications.
| S-EPMC6692671 | biostudies-literature