Machine learning approach to predict postoperative opioid requirements in ambulatory surgery patients

Nair, Akira A.; Velagapudi, Mihir; Lang, Jonathan A.; Behara, Lakshmana; Venigandla, Ravitheja; Velagapudi, Nishant; Fong, Christine; Horibe, Mayumi; Lang, Janet M.; Nair, Bala G.

doi:10.1371/journal.pone.0236833

Cited by 30 publications

(53 citation statements)

References 24 publications

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“…Moreover, machine-learning models predicted chronic opioid use after anterior cervical discectomy and fusion with an AUC–ROC of 0.8, and after knee arthroscopy with an AUC–ROC of 0.74. Finally, artificial intelligence may help clinicians with the prediction of postoperative opioid requirements in surgical patients [ 95 ].…”

Section: Artificial Intelligence and Machine-learning Methodsmentioning

confidence: 99%

Big Data in Studying Acute Pain and Regional Anesthesia

Müller-Wirtz

Volk

2021

JCM

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The digital transformation of healthcare is advancing, leading to an increasing availability of clinical data for research. Perioperative big data initiatives were established to monitor treatment quality and benchmark outcomes. However, big data analyses have long exceeded the status of pure quality surveillance instruments. Large retrospective studies nowadays often represent the first approach to new questions in clinical research and pave the way for more expensive and resource intensive prospective trials. As a consequence, the utilization of big data in acute pain and regional anesthesia research has considerably increased over the last decade. Multicentric clinical registries and administrative databases (e.g., healthcare claims databases) have collected millions of cases until today, on which basis several important research questions were approached. In acute pain research, big data was used to assess postoperative pain outcomes, opioid utilization, and the efficiency of multimodal pain management strategies. In regional anesthesia, adverse events and potential benefits of regional anesthesia on postoperative morbidity and mortality were evaluated. This article provides a narrative review on the growing importance of big data for research in acute postoperative pain and regional anesthesia.

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Section: Artificial Intelligence and Machine-learning Methodsmentioning

confidence: 99%

Big Data in Studying Acute Pain and Regional Anesthesia

Müller-Wirtz

Volk

2021

JCM

View full text Add to dashboard Cite

show abstract

“…Moreover, machine learning models predicted chronic opioid use after anterior cervical discectomy and fusion with an AUC-ROC of 0.8, and after knee arthroscopy with an AUC-ROC of 0.74. And finally, artificial intelligence may help clinicians with the prediction of postoperative opioid requirements in surgical patients [95].…”

Section: Artificial Intelligence and Machine Learning Methodsmentioning

confidence: 99%

Big Data in Studying Acute Pain and Regional Anesthesia

Müller-Wirtz¹,

Volk²

2021

Preprint

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The digital transformation of healthcare is advancing, leading to an increasing availability of clinical data for research. Perioperative big data initiatives were established to monitor treatment quality and benchmark outcomes. However, big data analyzes have long exceeded the status of pure quality surveillance instruments. Large retrospective studies nowadays often represent the first approach to new questions in clinical research and pave the way for more expensive and resource intensive prospective trials. As a consequence, utilization of big data in acute pain and regional anesthesia research considerably increased over the last decade. Multicentric clinical registries and administrative databases (e.g., healthcare claims databases) have collected millions of cases until today, on which basis several important research questions were approached. In acute pain research, big data was used to assess postoperative pain outcomes, opioid utilization, and the efficiency of multimodal pain management strategies. In regional anesthesia, adverse events and potential benefits of regional anesthesia on postoperative morbidity and mortality were evaluated. This article provides a narrative review on the growing importance of big data for research in acute postoperative pain and regional anesthesia.

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“… 32 Given the amount of patient and provider data stored in EHRs coupled with advances in clinical research informatics tools, EHR data should be used to study and identify risk factors for the development of CPSP, with the ultimate goal of developing global clinical data research networks that have the capability of deep clinical phenotyping. 33 Similarly, data mining techniques such as machine learning has been utilized to predict postsurgical opioid use 34 , 35 and have also been proposed as a method to apply a systems biology framework to elucidate novel biological pathways involved in acute postoperative pain and CPSP, with a recent study using machine learning for targeted genetic profiling to explore CPSP risk in adult and pediatric patients. 36 …”

Section: State I: Presurgicalmentioning

confidence: 99%

Preventing pediatric chronic postsurgical pain: Time for increased rigor

Sieberg

Karunakaran

Kussman

et al. 2022

Canadian Journal of Pain

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Chronic postsurgical pain (CPSP) results from a cascade of events in the peripheral and central nervous systems following surgery. Several clinical predictors, including the prior pain state, premorbid psychological state (e.g., anxiety, catastrophizing), intraoperative surgical load (establishment of peripheral and central sensitization), and acute postoperative pain management, may contribute to the patient’s risk of developing CPSP. However, research on the neurobiological and biobehavioral mechanisms contributing to pediatric CPSP and effective preemptive/treatment strategies are still lacking. Here we evaluate the perisurgical process by identifying key problems and propose potential solutions for the pre-, intra-, and postoperative pain states to both prevent and manage the transition of acute to chronic pain. We propose an eight-step process involving preemptive and preventative analgesia, behavioral interventions, and the use of biomarkers (brain-based, inflammatory, or genetic) to facilitate timely evaluation and treatment of premorbid psychological factors, ongoing surgical pain, and postoperative pain to provide an overall improved outcome. By achieving this, we can begin to establish personalized precision medicine for children and adolescents presenting to surgery and subsequent treatment selection.

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Machine learning approach to predict postoperative opioid requirements in ambulatory surgery patients

Cited by 30 publications

References 24 publications

Big Data in Studying Acute Pain and Regional Anesthesia

Big Data in Studying Acute Pain and Regional Anesthesia

Big Data in Studying Acute Pain and Regional Anesthesia

Preventing pediatric chronic postsurgical pain: Time for increased rigor

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