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Research Studies is a monthly compilation of research articles funded by AHRQ or authored by AHRQ researchers and recently published in journals or newsletters.
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Parthipan A, Banerjee I, Humphreys K
Predicting inadequate postoperative pain management in depressed patients: a machine learning approach.
Researchers employed a machine-learning approach to identify patients who were prescribed a combination of selective serotonin reuptake inhibitors (SSRIs) and prodrug opioids in order to examine the effect of this combination on postoperative pain control. They identified patients who received surgery over a 9-year period by using EHR data from an academic medical center, then developed and validated natural language processing (NLP) algorithms to extract depression-related information from both structured and unstructured data elements. The machine-learning algorithm accurately predicted the increase or decrease of the discharge, 3-week, and 8-week follow-up pain scores when compared to the pre-operative pain score; pre-operative pain, surgery type, and opioid tolerance were the strongest predictors of postoperative pain control. The researchers conclude that their study results provide the first direct clinical evidence that the known ability of SSRIs to inhibit prodrug opioid effectiveness is associated with worse pain control among depressed patients. They suggest that prescribers might choose direct acting opioids such as oxycodone or morphine for depressed patients on SSRIs instead of prodrug opioids.
Citation: Parthipan A, Banerjee I, Humphreys K . Predicting inadequate postoperative pain management in depressed patients: a machine learning approach. PLoS One 2019 Feb 6;14(2):e0210575. doi: 10.1371/journal.pone.0210575..
Keywords: Care Management, Depression, Medication, Opioids, Pain, Surgery