CoChat
Sign Up
PreprintCited by 35Anesthesia and Pain ManagementPediatric Pain Management TechniquesPain Management and Opioid Use

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

A Random Forest Algorithm-Based Prediction Model for Moderate to Severe Acute Postoperative Pain After Orthopedic Surgery Under General Anesthesia

Open access
Shi, Gaoxiang, Liu, Geliang, Gao, Qichao, Zhang, Shengxiao, Wang, Qi, Wu, Li, He, Peifeng, Yu, Qi·2023·BMC Anesthesiology
DOI 10.21203/rs.3.rs-2969052/v1PMC PMC10626723

Preprint — published as 10.1186/s12871-023-02328-1 · Paper page

Open this paper in CoChat — free accountOpen PDF

Abstract

Background Postoperative pain is one of the most common complications after surgery. In order to detect early and intervene in time for moderate to severe postoperative pain, it is necessary to identify risk factors and construct clinical prediction models. This study aimed to identify significant risk factors and establish a better-performing model to predict moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia. Methods Patients who underwent orthopedic surgery under general anesthesia were divided into patients with moderate to severe pain group (group P) and patients without moderate to severe pain group (group N) based on VAS scores. The features selected by Lasso regression were processed by the random forest and multivariate logistic regression models to predict pain outcomes. The classification performance of the two models was evaluated through the testing set. The area under the curves (AUC), the accuracy of the classifiers, and the classification error rate for both classifiers were calculated, the better-performing model was used to predict moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia. Results A total of 327 patients were enrolled in this study (228 in the training set and 99 in the testing set). The incidence of moderate to severe postoperative pain was 41.3%. The random forest model revealed a classification error rate of 25.2% and an AUC of 0.810 in the testing set. The multivariate logistic regression model revealed a classification error rate of 31.3% and an AUC of 0.764 in the testing set. The random forest model was chosen for predicting clinical outcomes in this study. The risk factors with the greatest and second contribution were immobilization and duration of surgery, respectively. Conclusions The random forest model can be used to predict moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia, which is of potential clinical application value.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.21203/rs.3.rs-2969052/v1

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 29

  1. Buvanendran A, Fiala J, Patel KA, Golden AD, Moric M, Kroin JS. The Incidence and Severity of Postoperative Pain following Inpatient Surgery. Pain Med. 2015;16:2277–83. https://doi.org/10.1111/pme.12751. · DOI
  2. Gramke HF, de Rijke JM, van Kleef M, Raps F, Kessels AG, Peters ML, et al. The prevalence of postoperative pain in a cross-sectional group of patients after day-case surgery in a university hospital. Clin J Pain. 2007;23:543–8. https://doi.org/10.1097/AJP.0b013e318074c970. · DOI
  3. Gan TJ. Poorly controlled postoperative pain: prevalence, consequences, and prevention. J Pain Res. 2017;10:2287–98. https://doi.org/10.2147/JPR.S144066. · DOI
  4. Fregoso G, Wang A, Tseng K, Wang J. Transition from Acute to Chronic Pain: Evaluating Risk for Chronic Postsurgical Pain. Pain Physician. 2019;22:479–88.
  5. Scholten W, Nygren-Krug H, Zucker HA. The World Health Organization paves the way for action to free people from the shackles of pain. Anesth Analg. 2007;105:1–4. https://doi.org/10.1213/01.ane.0000267542.72315.34. · DOI
  6. Pitchon DN, Dayan AC, Schwenk ES, Baratta JL, Viscusi ER. Updates on Multimodal Analgesia for Orthopedic Surgery. Anesthesiol Clin. 2018;36:361–73. https://doi.org/10.1016/j.anclin.2018.05.001. · DOI
  7. Wang LH, Chen FY, Su ZC, Zhang J. Effect of General Anesthesia and Combined Spinal-epidural Anesthesia on the Postoperative Pains, Adverse Reactions and Analgesia Effect of Senile Patients with Hip Joint Replacement. Syst Med. 2018;3(05):1–3. https://doi.org/10.19368/j.cnki.2096-1782.2018.05.001. · DOI
  8. Liu SS, Buvanendran A, Rathmell JP, Sawhney M, Bae JJ, Moric M, et al. Predictors for moderate to severe acute postoperative pain after total hip and knee replacement. Int Orthop. 2012;36:2261–7. https://doi.org/10.1007/s00264-012-1623-5. · DOI · Paper page
  9. Biau G, Scornet E. A random forest guided tour. Test. 2016;25:197–227. · DOI · Paper page
  10. Borges NC, Pereira LV, de Moura LA, Silva TC, Pedroso CF. Predictors for Moderate to Severe Acute Postoperative Pain after Cesarean Section. Pain Res Manag. 2016; 2016: 5783817. https://doi.org/10.1155/2016/5783817. · DOI
  11. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350:g7594. https://doi.org/10.1136/bmj.g7594. · DOI · Paper page
  12. Gerbershagen HJ, Rothaug J, Kalkman CJ, Meissner W. Determination of moderate-to-severe postoperative pain on the numeric rating scale: a cut-off point analysis applying four different methods. Br J Anaesth. 2011;107:619–26. https://doi.org/10.1093/bja/aer195. · DOI · Paper page
  13. Apfelbaum JL, Chen C, Mehta SS, Gan TJ. Postoperative pain experience: results from a national survey suggest postoperative pain continues to be undermanaged. Anesth Analg. 2003;97:534–40. https://doi.org/10.1213/01.ANE.0000068822.10113.9E. · DOI
  14. Baca Q, Marti F, Poblete B, Gaudilliere B, Aghaeepour N, Angst MS. Predicting Acute Pain After Surgery: A Multivariate Analysis. Ann Surg. 2021;273:289–98. https://doi.org/10.1097/SLA.0000000000003400. · DOI
  15. Chung F, Ritchie E, Su J. Postoperative pain in ambulatory surgery. Anesth Analg. 1997;85:808–16. https://doi.org/10.1097/00000539-199710000-00017. · DOI
  16. Vasilopoulos T, Wardhan R, Rashidi P, Fillingim RB, Wallace MR, Crispen PL, et al. Patient and Procedural Determinants of Postoperative Pain Trajectories. Anesthesiology. 2021;134:421–34. https://doi.org/10.1097/ALN.0000000000003681. · DOI
  17. Sun K, Liu D, Chen J, Yu S, Bai Y, Chen C, et al. Moderate-severe postoperative pain in patients undergoing video-assisted thoracoscopic surgery: A retrospective study. Sci Rep. 2020;10:795. https://doi.org/10.1038/s41598-020-57620-8. · DOI
  18. Abrecht CR, Cornelius M, Wu A, Jamison RN, Janfaza D, Urman RD, et al. Prediction of Pain and Opioid Utilization in the Perioperative Period in Patients Undergoing Primary Knee Arthroplasty: Psychophysical and Psychosocial Factors. Pain Med. 2019;20:161–71. https://doi.org/10.1093/pm/pny020. · DOI
  19. Zaslansky R, Meissner W, Chapman CR. Pain after orthopaedic surgery: differences in patient reported outcomes in the United States vs internationally. An observational study from the PAIN OUT dataset. Br J Anaesth. 2018;120:790–7. https://doi.org/10.1016/j.bja.2017.11.109. · DOI
  20. Hartwig M, Allvin R, Backstrom R, Stenberg E. Factors Associated with Increased Experience of Postoperative Pain after Laparoscopic Gastric Bypass Surgery. Obes Surg. 2017;27:1854–8. https://doi.org/10.1007/s11695-017-2570-4. · DOI
  21. Zhong M, Zhang H, Yu C, Jiang J, Duan X. Application of machine learning in predicting the risk of postpartum depression: A systematic review. J Affect Disord. 2022;318:364–79. https://doi.org/10.1016/j.jad.2022.08.070. · DOI
  22. Xing F, Luo R, Liu M, Zhou Z, Xiang Z, Duan XA. New Random Forest Algorithm-Based Prediction Model of Post-operative Mortality in Geriatric Patients With Hip Fractures. Front Med (Lausanne). 2022;9:829977. https://doi.org/10.3389/fmed.2022.829977. · DOI
  23. Binder M, Pfisterer F, Lang M, Schneider L, Kotthoff L. and Bischl B. mlr3pipelines—Flexible machine learning pipelines in r. J Mach Learn Res. 2021;22:8314–20.
  24. Lau H, Patil NG. Acute pain after endoscopic totally extraperitoneal (TEP) inguinal hernioplasty: multivariate analysis of predictive factors. Surg Endosc. 2004;18:92–6. https://doi.org/10.1007/s00464-003-9068-y. · DOI
  25. Kanaan SF, Melton BL, Waitman LR, Simpson MH, Sharma NK. The effect of age and gender on acute postoperative pain and function following lumbar spine surgeries. Physiother Res Int. 2021;26:e1888. https://doi.org/10.1002/pri.1888. · DOI
  26. Staud R, Cannon RC, Mauderli AP, Robinson ME, Price DD, Vierck CJ Jr. Temporal summation of pain from mechanical stimulation of muscle tissue in normal controls and subjects with fibromyalgia syndrome. Pain. 2003;102:87–95. https://doi.org/10.1016/s0304-3959(02)00344-5. · DOI
  27. Kumar N, Yadav C, Singh S, Kumar A, Vaithlingam A, Yadav S. Evaluation of pain in bilateral total knee replacement with and without tourniquet; a prospective randomized control trial. J Clin Orthop Trauma. 2015;6:85–8. https://doi.org/10.1016/j.jcot.2015.01.095. · DOI
  28. Li S, Li P, Wang R, Li H. Different interventions for preventing postoperative catheter-related bladder discomfort: a systematic review and meta-analysis. Eur J Clin Pharmacol. 2022;78:897–906. https://doi.org/10.1007/s00228-021-03251-5. · DOI
  29. Meijer F, Honing M, Roor T, Toet S, Calis P, Olofsen E, et al. Reduced postoperative pain using Nociception Level-guided fentanyl dosing during sevoflurane anaesthesia: a randomised controlled trial. Br J Anaesth. 2020;125:1070–8. https://doi.org/10.1016/j.bja.2020.07.057. · DOI

Metadata

Authors
Shi, G., Liu, G., Gao, Q., Zhang, S., Wang, Q., Wu, L., He, P., & Yu, Q.
Affiliations
  • Shanxi Medical University (CN)
  • Key Laboratory of Cellular Physiology, Ministry of Education, Shanxi Medical University
Corresponding author
  • He, Peifeng
  • Yu, Qi
ORCID
Venue
BMC Anesthesiology
Publisher
Research Square Platform LLC
Published
2023-05-30
Volume
23
Issue
1
Pages
361-361
ISSN
1471-2253 (ISSN-L)
DOI
10.21203/rs.3.rs-2969052/v1
PMC
PMC10626723
OpenAlex
W4388420151
Cited by
35
References
1
Citations by year
2026: 10 · 2025: 19 · 2024: 6
FWCI
9.34
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Anesthesiology (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Shanxi Provincial Education Department
Topics
  • Health Sciences › Medicine › Anesthesiology and Pain Medicine › Anesthesia and Pain Management
  • Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pediatric Pain Management Techniques
  • Health Sciences › Medicine › Anesthesiology and Pain Medicine › Pain Management and Opioid Use
Keywords
postoperative pain, acute postoperative pain, orthopedic surgery, general anesthesia, random forest, risk factors, immobilization, surgery duration, Lasso regression feature selection, multivariate logistic regression
MeSH terms
Random Forest, Humans, Postoperative Pain, Risk Factors, Orthopedic Procedures

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review