Efficacy of intravenous dexamethasone on postoperative pain after caesarean delivery under spinal anaesthesia with an intrathecal long-acting opioid: a systematic review and meta-analysis

Kamimura, Yuji; Kamijo, Kyosuke; Banno, Masahiro; Tsuji, Tatsuya; Aoki, Yusuke; Ito, Hidekazu; Tanaka, Motoshi; Sobue, Kazuya

doi:10.1007/s00540-023-03183-7

Cited by 3 publications

References 26 publications

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Consensus Statement on Pain Management for Pregnant Patients with Opioid-Use Disorder from the Society for Obstetric Anesthesia and Perinatology, Society for Maternal-Fetal Medicine, and American Society of Regional Anesthesia and Pain Medicine

Lim,

Carvalho,

George

et al. 2024

Anesthesia &Amp; Analgesia

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Pain management in pregnant and postpartum people with an opioid-use disorder (OUD) requires a balance between risks associated with opioid tolerance, including withdrawal or return to opioid use, considerations around social needs of the maternal-infant dyad, and the provision of adequate pain relief for the birth episode that is often characterized as the worst pain a person will experience in their lifetime. This multidisciplinary consensus statement between the Society for Obstetric Anesthesia and Perinatology (SOAP), Society for Maternal-Fetal Medicine (SMFM), and American Society of Regional Anesthesia and Pain Medicine (ASRA) provides a framework for pain management in obstetric patients with OUD. The purpose of this consensus statement is to provide practical and evidence-based recommendations and is targeted to health care providers in obstetrics and anesthesiology. The statement is focused on prenatal optimization of pain management, labor analgesia, and postvaginal delivery pain management, and postcesarean delivery pain management. Topics include a discussion of nonpharmacologic and pharmacologic options for pain management, medication management for OUD (eg, buprenorphine, methadone), considerations regarding urine drug testing, and other social aspects of care for maternal-infant dyads, as well as a review of current practices. The authors provide evidence-based recommendations to optimize pain management while reducing risks and complications associated with OUD in the peripartum period. Ultimately, this multidisciplinary consensus statement provides practical and concise clinical guidance to optimize pain management for people with OUD in the context of pregnancy to improve maternal and perinatal outcomes.

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Consensus Statement on Pain Management for Pregnant Patients with Opioid-Use Disorder from the Society for Obstetric Anesthesia and Perinatology, Society for Maternal-Fetal Medicine, and American Society of Regional Anesthesia and Pain Medicine

Lim,

Carvalho,

George

et al. 2024

Anesthesia &Amp; Analgesia

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Development of machine learning-based prediction models of inadequate postoperative analgesia after noncardiac surgery: a retrospective cohort study

Zhan,

Yan,

Sun

et al. 2024

Preprint

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Background Early identification of high-risk patients with inadequate postoperative analgesia is crucial in noncardiac surgery. This study aimed to develop prediction models for inadequate postoperative analgesia in noncardiac surgery using machine learning algorithms. Methods This article reports on a study that enrolled 199,517 adult patients who underwent noncardiac surgery. The discovery cohort included patients who had surgical procedures performed between June 2018 and April 2022, while the validation cohort included those who had surgeries between May 2022 and October 2022. Machine learning algorithms such as logistic regression (LR), random forest (RF), eXtreme gradient boosting tree (XGBoost), artificial neural network (ANN), and gradient boosting machine (GBM) were used to develop predictive models of inadequate postoperative analgesia based on perioperative variables. Results The overall prevalence of inadequate postoperative analgesia was 18.1%. Among the five machine learning algorithms we considered, GBM performed the best performance, with an AUROC value of 0.831 [95% CI, 0.827–0.834]. The SHAP analysis showed that surgery duration, type of surgery, anesthesia method, intraoperative fluid management, and use of hormones were the strongest five predictors. In addition, we observed that as the number of predicted features gradually decreased from all to 20, the performance of the prediction model exhibited only a marginal decline from 0.83 to 0.81. Conclusions This study demonstrates that the GBM algorithm demonstrated superior predictive performance in comparison to all other algorithms utilized. Screening for inadequate postoperative analgesia based on the prediction model could improve postoperative pain management.

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Year 2023 in review - Pain management

Málek

2023

Anest. intenziv. Med.

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Efficacy of intravenous dexamethasone on postoperative pain after caesarean delivery under spinal anaesthesia with an intrathecal long-acting opioid: a systematic review and meta-analysis

Cited by 3 publications

References 26 publications

Consensus Statement on Pain Management for Pregnant Patients with Opioid-Use Disorder from the Society for Obstetric Anesthesia and Perinatology, Society for Maternal-Fetal Medicine, and American Society of Regional Anesthesia and Pain Medicine

Consensus Statement on Pain Management for Pregnant Patients with Opioid-Use Disorder from the Society for Obstetric Anesthesia and Perinatology, Society for Maternal-Fetal Medicine, and American Society of Regional Anesthesia and Pain Medicine

Development of machine learning-based prediction models of inadequate postoperative analgesia after noncardiac surgery: a retrospective cohort study

Year 2023 in review - Pain management

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