Predicting postoperative opioid use with machine learning and insurance claims in opioid-naïve patients - 12/08/21
, Jenna Wiens a, ⁎, 2 Abstract |
Background |
The clinical impact of postoperative opioid use requires accurate prediction strategies to identify at-risk patients. We utilize preoperative claims data to predict postoperative opioid refill and new persistent use in opioid-naïve patients.
Methods |
A retrospective study was conducted on 112,898 opioid-naïve adult postoperative patients from Optum’s de-identified Clinformatics® Data Mart database. Potential predictors included sociodemographic data, comorbidities, and prescriptions within one year prior to surgery.
Results |
Compared to linear models, non-linear models led to modest improvements in predicting refills – area under the receiver operating characteristics curve (AUROC) 0.68 vs. 0.67 (p < 0.05) – and performed identically in predicting new persistent use – AUROC = 0.66. Undergoing major surgery, opioid prescriptions within 30 days prior to surgery, and abdominal pain were useful in predicting refills; back/joint/head pain were the most important features in predicting new persistent use.
Conclusions |
Preoperative patient attributes from insurance claims could potentially be useful in guiding prescription practices for opioid-naïve patients.
Il testo completo di questo articolo è disponibile in PDF.Highlights |
• | A large retrospective study on opioid-naïve patient was conducted. |
• | Machine learning models were trained using insurance claims data. |
• | Non-linear models performed modestly better than linear models. |
• | Opioid refills are associated with the nature of the surgery. |
• | New persistent opioid use is associated with underlying chronic pain conditions. |
Keywords : Machine learning, Claims data, Postoperative opioid use
Mappa
Vol 222 - N° 3
P. 659-665 - settembre 2021 Ritorno al numero
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