Articles: analgesics.
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Reg Anesth Pain Med · Jan 2024
What evidence is needed to inform postoperative opioid consumption guidelines? A cohort study of the Michigan Surgical Quality Collaborative.
To balance adequate pain management while minimizing opioid-related harms after surgery, opioid prescribing guidelines rely on patient-reported use after surgery. However, it is unclear how many patients are required to develop precise guidelines. We aimed to compare patterns of use, required sample size, and the precision for patient-reported opioid consumption after common surgical procedures. ⋯ This study demonstrates that profiles of opioid consumption share more similarities than differences for certain surgical procedures. Future investigations on patient-reported consumption are required for procedures not currently included in prescribing guidelines to ensure surgeons and perioperative providers can appropriately tailor recommendations to the postoperative needs of patients.
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Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures. ⋯ Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely.
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Standard dosages of analgesic and sedative drugs are given to intensive care patients. The resulting range of blood concentrations and corresponding clinical responses need to be better examined. The purpose of this study was to describe daily dosages, measured blood concentrations, and clinical responses in critically ill patients. The purpose was also to contribute to establishing whole blood concentration reference values of the drugs investigated. ⋯ Using recommended dose intervals for analgesic and sedative drugs in the ICU setting combined with regular monitoring of clinical responses such as RASS and NRS leads to 97% of concentrations being below the upper limit in the therapeutic interval. This study contributes to whole blood drug concentration reference values regarding these 10 drugs.
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Opioid nonadherence represents a significant barrier to cancer pain treatment efficacy. However, there is currently no effective prediction method for opioid adherence in patients with cancer pain. We aimed to develop and validate a machine learning (ML) model and evaluate its feasibility to predict opioid nonadherence in patients with cancer pain. ⋯ The best model obtained in this study, the LR model, had an AUC_ROC of 0.82, accuracy of 0.82, and specificity of 0.71. The DCA showed that clinical interventions for patients at high risk of opioid nonadherence based on the LR model can benefit patients. The strongest predictors for adherence were, in order of importance, beliefs about medicines questionnaire (BMQ)-harm, time since the start of opioid, and BMQ-necessity. Discussion. ML algorithms can be used as an effective means of predicting adherence to opioids in patients with cancer pain, which allows for proactive clinical intervention to optimize cancer pain management. This trial is registered with ChiCTR2000033576.