Prediction of Prolonged Opioid Use After Surgery in Adolescents: Insights From Machine Learning.

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Abstrakt

Tytuł:: Prediction of Prolonged Opioid Use After Surgery in Adolescents: Insights From Machine Learning.
Autorzy:: Ward A; From the Department of Electrical Engineering, Stanford University, Stanford, California.
Jani T; Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California.
De Souza E; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California.
Scheinker D; Department of Management Science and Engineering, Stanford University, Stanford, California.
Bambos N; From the Department of Electrical Engineering, Stanford University, Stanford, California.; Department of Management Science and Engineering, Stanford University, Stanford, California.
Anderson TA; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California.
Źródło:: Anesthesia and analgesia [Anesth Analg] 2021 Aug 01; Vol. 133 (2), pp. 304-313.
Typ publikacji:: Comparative Study; Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.
Język:: English
Imprint Name(s):: Publication: 1998- : Baltimore, Md. : Lippincott Williams & Wilkins
Original Publication: Cleveland, International Anesthesia Research Society.
MeSH Terms:: Decision Support Techniques*
Machine Learning*
Pain Management*
Analgesics, Opioid/*administration & dosage
Pain, Postoperative/*prevention & control
Surgical Procedures, Operative/*adverse effects
Adolescent ; Age Factors ; Child ; Drug Administration Schedule ; Female ; Humans ; Male ; Pain, Postoperative/diagnosis ; Pain, Postoperative/etiology ; Predictive Value of Tests ; Retrospective Studies ; Risk Assessment ; Risk Factors ; Time Factors ; Treatment Outcome ; Young Adult
References:: Degenhardt L, Whiteford HA, Ferrari AJ, et al. Global burden of disease attributable to illicit drug use and dependence: findings from the Global Burden of Disease Study 2010. Lancet. 2013;382:1564–1574.
Florence CS, Zhou C, Luo F, Xu L. The economic burden of prescription opioid overdose, abuse, and dependence in the United States, 2013. Med Care. 2016;54:901–906.
Shei A, Hirst M, Kirson NY, Enloe CJ, Birnbaum HG, Dunlop WC. Estimating the health care burden of prescription opioid abuse in five European countries. Clinicoecon Outcomes Res. 2015;7:477–488.
Guy GP Jr, Zhang K, Bohm MK, et al. Vital signs: changes in opioid prescribing in the United States, 2006-2015. MMWR Morb Mortal Wkly Rep. 2017;66:697–704.
DePhillips M, Watts J, Lowry J, Dowd MD. Opioid prescribing practices in pediatric acute care settings. Pediatr Emerg Care. 2019;35:16–21.
Meckler GD, Sheridan DC, Charlesworth CJ, Lupulescu-Mann N, Kim H, Sun BC. Opioid prescribing practices for pediatric headache. J Pediatr. 2019;204:240–244.e2.
Tomaszewski DM, Arbuckle C, Yang S, Linstead E. Trends in opioid use in pediatric patients in US Emergency Departments From 2006 to 2015. JAMA Netw Open. 2018;1:e186161.
Shah A, Hayes CJ, Martin BC. Characteristics of initial prescription episodes and likelihood of long-term opioid use—United States, 2006-2015. MMWR Morb Mortal Wkly Rep. 2017;66:265–269.
Whyte AJ, Torregrossa MM, Barker JM, Gourley SL. Editorial: long-term consequences of adolescent drug use: evidence from pre-clinical and clinical models. Front Behav Neurosci. 2018;12:83.
Miech R, Johnston L, O’Malley PM, Keyes KM, Heard K. Prescription opioids in adolescence and future opioid misuse. Pediatrics. 2015;136:e1169–e1177.
Ladha KS, Neuman MD, Broms G, et al. Opioid prescribing after surgery in the United States, Canada, and Sweden. JAMA Netw Open. 2019;2:e1910734.
Brummett CM, Waljee JF, Goesling J, et al. New persistent opioid use after minor and major surgical procedures in US adults. JAMA Surg. 2017;152:e170504.
Johnson SP, Chung KC, Zhong L, et al. Risk of prolonged opioid use among opioid-naïve patients following common hand surgery procedures. J Hand Surg Am. 2016;41:947–957.e3.
Forlenza EM, Lavoie-Gagne O, Lu Y, et al. Preoperative opioid use predicts prolonged postoperative opioid use and inferior patient outcomes following anterior cruciate ligament reconstruction. Arthroscopy. 2020;36:2681–2688, e2681.
Lawal OD, Gold J, Murthy A, et al. Rate and risk factors associated with prolonged opioid use after surgery: a systematic review and meta-analysis. JAMA Netw Open. 2020;3:e207367.
Harbaugh CM, Lee JS, Hu HM, et al. Persistent opioid use among pediatric patients after surgery. Pediatrics. 2018;141:e20172439.
Ward A, De Souza E, Miller D, et al. Incidence of and factors associated with prolonged and persistent postoperative opioid use in children 0-18 years of age. Anesth Analg. 2020;131:1237–1248.
Brummett CM, England C, Evans-Shields J, et al. Health care burden associated with outpatient opioid use following inpatient or outpatient surgery. J Manag Care Spec Pharm. 2019;25:973–983.
Bardach NS, Vittinghoff E, Asteria-Peñaloza R, et al. Measuring hospital quality using pediatric readmission and revisit rates. Pediatrics. 2013;132:429–436.
Gagne JJ, He M, Bateman BT. Trends in opioid prescription in children and adolescents in a commercially insured population in the United States, 2004-2017. JAMA Pediatr. 2018;173:98–99.
Karhade AV, Ogink PT, Thio Q, et al. Machine learning for prediction of sustained opioid prescription after anterior cervical discectomy and fusion. Spine J. 2019;19:976–983.
Lo-Ciganic WH, Huang JL, Zhang HH, et al. Evaluation of machine-learning algorithms for predicting opioid overdose risk among medicare beneficiaries with opioid prescriptions. JAMA Netw Open. 2019;2:e190968.
Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:6.
Tianqi Chen CG. XGBoost: A Scalable Tree Boosting System. 2016; Paper presented at: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; San Francisco, CA.
Strobl C, Boulesteix AL, Zeileis A, Hothorn T. Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics. 2007;8:25.
Altmann A, Toloşi L, Sander O, Lengauer T. Permutation importance: a corrected feature importance measure. Bioinformatics. 2010;26:1340–1347.
Fields AC, Cavallaro PM, Correll DJ, et al. Predictors of prolonged opioid use following colectomy. Dis Colon Rectum. 2019;62:1117–1123.
Hah JM, Bateman BT, Ratliff J, Curtin C, Sun E. Chronic opioid use after surgery: implications for perioperative management in the face of the opioid epidemic. Anesth Analg. 2017;125:1733–1740.
Politzer CS, Kildow BJ, Goltz DE, Green CL, Bolognesi MP, Seyler TM. Trends in opioid utilization before and after total knee arthroplasty. J Arthroplasty. 2018;33:S147–S153.e1.
Romero-Brufau S, Huddleston JM, Escobar GJ, Liebow M. Why the C-statistic is not informative to evaluate early warning scores and what metrics to use. Crit Care. 2015;19:285.
Carter HB, Pearson JD. Prostate-specific antigen testing for early diagnosis of prostate cancer: formulation of guidelines. Urology. 1999;54:780–786.
Maxim LD, Niebo R, Utell MJ. Screening tests: a review with examples. Inhal Toxicol. 2014;26:811–828.
Karhade AV, Cha TD, Fogel HA, et al. Predicting prolonged opioid prescriptions in opioid-naïve lumbar spine surgery patients. Spine J. 2020;20:888–895.
Karhade AV, Ogink PT, Thio QCBS, et al. Development of machine learning algorithms for prediction of prolonged opioid prescription after surgery for lumbar disc herniation. Spine J. 2019;19:1764–1771.
Karhade AV, Schwab JH, Bedair HS. Development of machine learning algorithms for prediction of sustained postoperative opioid prescriptions after total hip arthroplasty. J Arthroplasty. 2019;34:2272–2277.e1.
Grant Information:: UL1 TR001085 United States TR NCATS NIH HHS
Substance Nomenclature:: 0 (Analgesics, Opioid)
Entry Date(s):: Date Created: 20210503 Date Completed: 20210913 Latest Revision: 20210913
Update Code:: 20240104
DOI:: 10.1213/ANE.0000000000005527
PMID:: 33939656
: Czasopismo naukowe

Background: Long-term opioid use has negative health care consequences. Patients who undergo surgery are at risk for prolonged opioid use after surgery (POUS). While risk factors have been previously identified, no methods currently exist to determine higher-risk patients. We assessed the ability of a variety of machine-learning algorithms to predict adolescents at risk of POUS and to identify factors associated with this risk.
Methods: A retrospective cohort study was conducted using a national insurance claims database of adolescents aged 12-21 years who underwent 1 of 1297 surgeries, with general anesthesia, from January 1, 2011 to December 30, 2017. Logistic regression with an L2 penalty and with a logistic regression with an L1 lasso (Lasso) penalty, random forests, gradient boosting machines, and extreme gradient boosted models were trained using patient and provider characteristics to predict POUS (≥1 opioid prescription fill within 90-180 days after surgery) risk. Predictive capabilities were assessed using the area under the receiver-operating characteristic curve (AUC)/C-statistic, mean average precision (MAP); individual decision thresholds were compared using sensitivity, specificity, Youden Index, F1 score, and number needed to evaluate. The variables most strongly associated with POUS risk were identified using permutation importance.
Results: Of 186,493 eligible patient surgical visits, 8410 (4.51%) had POUS. The top-performing algorithm achieved an overall AUC of 0.711 (95% confidence interval [CI], 0.699-0.723) and significantly higher AUCs for certain surgeries (eg, 0.823 for spinal fusion surgery and 0.812 for dental surgery). The variables with the strongest association with POUS were the days' supply of opioids and oral morphine milligram equivalents of opioids in the year before surgery.
Conclusions: Machine-learning models to predict POUS risk among adolescents show modest to strong results for different surgeries and reveal variables associated with higher risk. These results may inform health care system-specific identification of patients at higher risk for POUS and drive development of preventative measures.
Competing Interests: The authors declare no conflicts of interest.
(Copyright © 2021 International Anesthesia Research Society.)

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