Swiss Federal Institute of Technology Zurich (ETH) Reports Findings in Prescription Opioids (Development and internal validation of a prediction model for long-term opioid use-an analysis of insurance claims data): Opioids – Prescription Opioids

2023 OCT 16 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- New research on Opioids - Prescription Opioids is the subject of a report. According to news reporting from Zurich, Switzerland, by NewsRx journalists, research stated, “In the United States, a public-health crisis of opioid overuse has been observed, and in Europe, prescriptions of opioids are strongly increasing over time. The objective was to develop and validate a multivariable prognostic model to be used at the beginning of an opioid prescription episode, aiming to identify individual patients at high risk for long-term opioid use based on routinely collected data.”

The news correspondents obtained a quote from the research from the Swiss Federal Institute of Technology Zurich (ETH), “Predictors including demographics, comorbid diseases, comedication, morphine dose at episode initiation, and prescription practice were collected. The primary outcome was long-term opioid use, defined as opioid use of either >90 days duration and 10 claims or >120 days, independent of the number of claims. Traditional generalized linear statistical regression models and machine learning approaches were applied. The area under the curve, calibration plots, and the scaled Brier score assessed model performance. More than four hundred thousand opioid episodes were included. The final risk prediction model had an area under the curve of 0.927 (95% confidence interval 0.924-0.931) in the validation set, and this model had a scaled Brier score of 48.5%. Using a threshold of 10% predicted probability to identify patients at high risk, the overall accuracy of this risk prediction model was 81.6% (95% confidence interval 81.2% to 82.0%). Our study demonstrated that long-term opioid use can be predicted at the initiation of an opioid prescription episode, with satisfactory accuracy using data routinely collected at a large health insurance company.”

According to the news reporters, the research concluded: “Traditional statistical methods resulted in higher discriminative ability and similarly good calibration as compared with machine learning approaches.”

This research has been peer-reviewed.

For more information on this research see: Development and internal validation of a prediction model for long-term opioid use-an analysis of insurance claims data. Pain, 2023. Pain can be contacted at: Lippincott Williams & Wilkins, Two Commerce Sq, 2001 Market St, Philadelphia, PA 19103, USA. (Elsevier - www.elsevier.com; Pain - http://www.journals.elsevier.com/pain/)

Our news journalists report that additional information may be obtained by contacting Tom Forzy, Master Program Statistics, Swiss Federal Institute of Technology Zurich (ETH), Zurich, Switzerland. Additional authors for this research include Ulrike Held, Andri Signorell, Manja Deforth, Jakob M. Burgstaller and Maria M. Wertli.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1097/j.pain.0000000000003023. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

Publisher contact information for the journal Pain is: Lippincott Williams & Wilkins, Two Commerce Sq, 2001 Market St, Philadelphia, PA 19103, USA.

(Our reports deliver fact-based news of research and discoveries from around the world.)