Evidence map›Paper›PMID 37408033›Full record

ArticleBMC musculoskeletal disorders2023

The Skeletal Oncology Research Group Machine Learning Algorithm (SORG-MLA) for predicting prolonged postoperative opioid prescription after total knee arthroplasty: an international validation study using 3,495 patients from a Taiwanese cohort.

Cheng-Chen Tsai, Chuan-Ching Huang, Ching-Wei Lin, Paul T Ogink, Chih-Chi Su, Shin-Fu Chen, Mao-Hsu Yen, Jorrit-Jan Verlaan, Joseph H Schwab, Chen-Ti Wang and 3 more

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Article in BMC musculoskeletal disorders, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Cheng-Chen Tsai *Department of Medical Education, National Taiwan University Hospital, Taipei, Taiwan.
Chuan-Ching Huang *Department of Orthopaedic Surgery, National Taiwan University Hospital, No.7 Chung-Shan South Road, Taipei, 10002, Taiwan.
Ching-Wei Lin *Department of Orthopedic Surgery, Chang Gung Memorial Hospital, Taoyuan, Taiwan.
Paul T OginkDepartment of Orthopaedics, University Medical Center Utrecht, Utrecht, The Netherlands.
Chih-Chi SuDepartment of Medical Education, National Taiwan University Hospital, Taipei, Taiwan.
Shin-Fu ChenDepartment of Medical Education, National Taiwan University Hospital, Taipei, Taiwan.
Mao-Hsu YenDepartment of Computer Science and Engineering, National Taiwan Ocean University, Taipei, Taiwan.
Jorrit-Jan VerlaanDepartment of Orthopaedics, University Medical Center Utrecht, Utrecht, The Netherlands.
Joseph H SchwabDepartment of Orthopaedic Surgery, Massachusetts General Hospital, Boston, USA.
Chen-Ti WangDepartment of Orthopaedic Surgery, National Taiwan University Hospital, No.7 Chung-Shan South Road, Taipei, 10002, Taiwan.
Olivier Q GrootDepartment of Orthopaedics, University Medical Center Utrecht, Utrecht, The Netherlands.
Ming-Hsiao HuDepartment of Orthopaedic Surgery, National Taiwan University Hospital, No.7 Chung-Shan South Road, Taipei, 10002, Taiwan. minghsiaohu@yahoo.com.tw.
Hongsen ChiangDepartment of Orthopaedic Surgery, National Taiwan University Hospital, No.7 Chung-Shan South Road, Taipei, 10002, Taiwan. hongsen@ntu.edu.tw.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPreoperative prediction of prolonged postoperative opioid use (PPOU) after total knee arthroplasty (TKA) could identify high-risk patients for increased surveillance. The Skeletal Oncology Research Group machine learning algorithm (SORG-MLA) has been tested internally while lacking external support to assess its generalizability. The aims of this study were to externally validate this algorithm in an Asian cohort and to identify other potential independent factors for PPOU.

methodsIn a tertiary center in Taiwan, 3,495 patients receiving TKA from 2010-2018 were included. Baseline characteristics were compared between the external validation cohort and the original developmental cohorts. Discrimination (area under receiver operating characteristic curve [AUROC] and precision-recall curve [AUPRC]), calibration, overall performance (Brier score), and decision curve analysis (DCA) were applied to assess the model performance. A multivariable logistic regression was used to evaluate other potential prognostic factors.

resultsThere were notable differences in baseline characteristics between the validation and the development cohort. Despite these variations, the SORG-MLA ( https://sorg-apps.shinyapps.io/tjaopioid/ ) remained its good discriminatory ability (AUROC, 0.75; AUPRC, 0.34) and good overall performance (Brier score, 0.029; null model Brier score, 0.032). The algorithm could bring clinical benefit in DCA while somewhat overestimating the probability of prolonged opioid use. Preoperative acetaminophen use was an independent factor to predict PPOU (odds ratio, 2.05).

conclusionsThe SORG-MLA retained its discriminatory ability and good overall performance despite the different pharmaceutical regulations. The algorithm could be used to identify high-risk patients and tailor personalized prevention policy.

Indexed as

Arthroplasty, Replacement, KneeOpioid-Related DisordersAlgorithmsAnalgesics, OpioidHumansMachine LearningPrescriptionsRetrospective StudiesAnalgesics, OpioidAcetaminophen useAsian groupMachine learningPrediction modelProlonged opioid useTotal knee arthroplasty

Identifiers

PMID37408033
PMCPMC10320986

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.