| Title |
Prediction Model for Two-Year Risk of Opioid Overdose Among Patients Prescribed Chronic Opioid Therapy
|
|---|---|
| Published in |
Journal of General Internal Medicine, January 2018
|
| DOI | 10.1007/s11606-017-4288-3 |
| Pubmed ID | |
| Authors |
Jason M. Glanz, Komal J. Narwaney, Shane R. Mueller, Edward M. Gardner, Susan L. Calcaterra, Stanley Xu, Kristin Breslin, Ingrid A. Binswanger |
| Abstract |
Naloxone is a life-saving opioid antagonist. Chronic pain guidelines recommend that physicians co-prescribe naloxone to patients at high risk for opioid overdose. However, clinical tools to efficiently identify patients who could benefit from naloxone are lacking. To develop and validate an overdose predictive model which could be used in primary care settings to assess the need for naloxone. Retrospective cohort. Derivation site was an integrated health system in Colorado; validation site was a safety-net health system in Colorado. We developed a predictive model in a cohort of 42,828 patients taking chronic opioid therapy and externally validated the model in 10,708 patients. Potential predictors and outcomes (nonfatal pharmaceutical and heroin overdoses) were extracted from electronic health records. Fatal overdose outcomes were identified from state vital records. To match the approximate shelf-life of naloxone, we used Cox proportional hazards regression to model the 2-year risk of overdose. Calibration and discrimination were assessed. A five-variable predictive model showed good calibration and discrimination (bootstrap-corrected c-statistic = 0.73, 95% confidence interval [CI] 0.69-0.78) in the derivation site, with sensitivity of 66.1% and specificity of 66.6%. In the validation site, the model showed good discrimination (c-statistic = 0.75, 95% CI 0.70-0.80) and less than ideal calibration, with sensitivity and specificity of 82.2% and 49.5%, respectively. Among patients on chronic opioid therapy, the predictive model identified 66-82% of all subsequent opioid overdoses. This model is an efficient screening tool to identify patients who could benefit from naloxone to prevent overdose deaths. Population differences across the two sites limited calibration in the validation site. |
X Demographics
The data shown below were collected from the profiles of 164 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 68 | 41% |
| Canada | 23 | 14% |
| Australia | 8 | 5% |
| United Kingdom | 6 | 4% |
| Argentina | 1 | <1% |
| New Zealand | 1 | <1% |
| Malaysia | 1 | <1% |
| Sweden | 1 | <1% |
| Venezuela, Bolivarian Republic of | 1 | <1% |
| Other | 0 | 0% |
| Unknown | 54 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 109 | 66% |
| Scientists | 27 | 16% |
| Practitioners (doctors, other healthcare professionals) | 21 | 13% |
| Science communicators (journalists, bloggers, editors) | 7 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 129 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 129 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 20 | 16% |
| Student > Bachelor | 13 | 10% |
| Student > Ph. D. Student | 12 | 9% |
| Student > Master | 11 | 9% |
| Student > Doctoral Student | 8 | 6% |
| Other | 24 | 19% |
| Unknown | 41 | 32% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 24 | 19% |
| Psychology | 12 | 9% |
| Nursing and Health Professions | 11 | 9% |
| Social Sciences | 8 | 6% |
| Unspecified | 5 | 4% |
| Other | 20 | 16% |
| Unknown | 49 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 162. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 April 2021.
All research outputs
#256,605
of 25,713,737 outputs
Outputs from Journal of General Internal Medicine
#214
of 8,243 outputs
Outputs of similar age
#5,902
of 452,762 outputs
Outputs of similar age from Journal of General Internal Medicine
#12
of 149 outputs
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So far Altmetric has tracked 8,243 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.9. This one has done particularly well, scoring higher than 97% of its peers.
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We're also able to compare this research output to 149 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.