Machine Learning Model Identifies Preoperative Opioid Use, Male Sex, and Elevated Body Mass Index as Predictive Factors for Prolonged Opioid Consumption Following Arthroscopic Meniscal Surgery

Castle, Joshua; Jildeh, Toufic R.; Chaudhry, Farhan; Turner, Elizabeth H.G.; Abbas, Muhammad J.; Mahmoud, Ossama; Hengy, Meredith; Okoroha, Kelechi R.; Lynch, T. Sean

doi:10.1016/j.arthro.2022.12.025

Cited by 9 publications

(2 citation statements)

References 30 publications

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“…Development of postoperative complications: Most studies focused on the prediction of postoperative acute complications 26–28,42,44–49,59,99,110,112,113,118,123 such as pain and opioid use, 53–57 postoperative atrial fibrillation (new-onset atrial fibrillation), 82 postoperative risk of stroke or myocardial infarction, 50,71,77 and delirium or cognitive decline. 65–70 Other models focused on the risk of developing pneumonia or respiratory failure, 83,85,125 acute kidney injury, 43,52,58,60–63,120–122 liver failure 117 or development of sepsis or surgical site infection.…”

Section: Resultsmentioning

confidence: 99%

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

Arina,

Kaczorek,

Hofmaenner

et al. 2023

Anesthesiology

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Background The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation. Methods A multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies. Results A total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications. Conclusions The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

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Section: Resultsmentioning

confidence: 99%

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

Arina,

Kaczorek,

Hofmaenner

et al. 2023

Anesthesiology

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show abstract

“…Risk factors including male sex, increased body mass index, chronic preoperative opioid use, and history of anxiety/depression have been established for opioid use in pain management and length of postoperative opioid use. 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 A prospective study of 250 patients (36 Black patients, 100 Hispanic patients, and 114 white patients) after open reduction and internal fixation of limb fractures by Ng et al. 37 found that Black and Hispanic patients received lower-dose analgesics during their hospitalization (average LOS of 4.45 days) than white patients (Black: 16 mg/day, Hispanic: 13 mg/day, white: 22 mg/day, P < .002).…”

Section: Health Disparities In Pain Controlmentioning

confidence: 99%

Cultural Competence and the Postoperative Experience: Pain Control and Rehabilitation

Yelton

Jildeh

2023

Arthroscopy, Sports Medicine, and Rehabilitation

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Editorial Commentary: Machine Learning and Artificial Intelligence Are Tools Requiring Physician and Patient Input When Screening Patients at Risk for Extended, Postoperative Opioid Use

Mirle

Forsythe

2023

Arthroscopy: The Journal of Arthroscopic & Related Surgery

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Machine Learning Model Identifies Preoperative Opioid Use, Male Sex, and Elevated Body Mass Index as Predictive Factors for Prolonged Opioid Consumption Following Arthroscopic Meniscal Surgery

Cited by 9 publications

References 30 publications

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

Cultural Competence and the Postoperative Experience: Pain Control and Rehabilitation

Editorial Commentary: Machine Learning and Artificial Intelligence Are Tools Requiring Physician and Patient Input When Screening Patients at Risk for Extended, Postoperative Opioid Use

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