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Diagnostic accuracy of updated risk assessment criteria and development of novel computational prediction models for patients with suspected choledocholithiasis

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Abstract

Background

There are risks of choledocholithiasis in symptomatic gallstones, and some surgeons have proposed the identification of choledocholithiasis before cholecystectomy. Our goal was to evaluate the diagnostic accuracy of the latest guidelines and create computational prediction models for the accurate prediction of choledocholithiasis.

Methods

We retrospectively reviewed symptomatic gallstone patients hospitalized with suspected choledocholithiasis. The diagnostic performance of 2019 and 2010 guidelines of the American Society for Gastrointestinal Endoscopy (ASGE) and 2019 guideline of the European Society of Gastrointestinal Endoscopy (ESGE) in different risks. Lastly, we developed novel prediction models based on the preoperative predictors.

Results

A total of 1199 patients were identified and 681 (56.8%) had concurrent choledocholithiasis and were included in the analysis. The specificity of the 2019 ASGE, 2010 ASGE, and 2019 ESGE high-risk criteria was 85.91%, 72.2%, and 88.42%, respectively, and their positive predictive values were 85.5%, 77.4%, and 87.3%, respectively. For Mid-risk patients who followed 2019 ASGE about 61.8% of them did not have CBD stones in our study. On the choice of surgical procedure, laparoscopic cholecystectomy + laparoscopic transcystic common bile duct exploration can be considered the optimal treatment choice for cholecysto-choledocholithiasis instead of Endoscopic Retrograde Cholangio-Pancreatography (ERCP). We build seven machine learning models and an AI diagnosis prediction model (ModelArts). The area under the receiver operating curve of the machine learning models was from 0.77 to 0.81. ModelArts AI model showed predictive accuracy of 0.97, recall of 0.97, precision of 0.971, and F1 score of 0.97, surpassing any other available methods.

Conclusion

The 2019 ASGE guideline and 2019 ESGE guideline have demonstrated higher specificity and positive predictive value for high-risk criteria compared to the 2010 ASGE guideline. The excellent diagnostic performance of the new artificial intelligence prediction model may make it a better choice than traditional guidelines for managing patients with suspected choledocholithiasis in future.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

AUS:

Abdominal ultrasound

ACC:

Acute calculus cholecystitis

ALT:

Alanine aminotransferase

ALP:

Alkaline phosphatase

ASGE:

American society for gastrointestinal endoscopy

AI:

Artificial intelligence

AST:

Aspartate aminotransferase

CT:

Computed tomography

CI:

Confidence interval

ESGE:

European society of gastrointestinal endoscopy

LC:

Laparoscopic cholecystectomy

LTCBDE:

Laparoscopic transcystic common bile duct exploration

LCBDE:

Laparoscopic common bile duct exploration

MRCP:

Magnetic resonance cholangio-pancreatography

−LR:

Negative likelihood ratio

NPV:

Negative predictive value

OBS:

Object storage service

OR:

Odds ratio

 + LR:

Positive likelihood ratio

PPV:

Positive predictive value

STARD:

Reporting Diagnostic Accuracy

TBIL:

Total bilirubin

GGT:

γ-Glutamyltransferase

AOR:

Adjusted odds ratios

WBC:

White blood count

Log-Reg:

Logistic regression classification learner

IDA:

Linear discriminant analysis classification learner

QDA:

Quadratic discriminant analysis classification learner

Naive-Bayes:

Naive Bayes classification learner

KKNN:

K-nearest-neighbor classification learner

Rpart:

Classification tree learner

Ranger:

Ranger classification learner

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Acknowledgements

Thanks to “YIDUCLOUD” data process & application platform for the technical support.

Funding

Liaoning Provincial Health Care Commission Chinese Medicine Clinical (Specialized). Department Capacity Building Project (194-2018125). Dalian High-level Talent Innovation Support Program (2019RD11)

Author information

Author notes

  1. Haoxiang Zhang, Jiangping Gao, and Zhen Sun have contributed equally to this work.

Authors and Affiliations

  1. Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China

    Haoxiang Zhang, Qingkai Zhang, Bing Qi, Xingchi Jiang, Shuang Li & Dong Shang

  2. Institute (College) of Integrative Medicine, Dalian Medical University, Dalian, Liaoning, China

    Qingkai Zhang, Bing Qi, Xingchi Jiang, Shuang Li & Dong Shang

  3. Department of Hepatopancreatobiliary Surgery, Fujian Provincial Hospital, Fuzhou, Fujian, China

    Haoxiang Zhang

  4. Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Haoxiang Zhang

  5. Department of Surgical Oncology, Gansu Provincial Hospital, Lanzhou, China

    Jiangping Gao

  6. Department of Gastrointestinal Surgery, The Second Hospital of Jilin University, Changchun, China

    Zhen Sun

Authors
  1. Haoxiang Zhang
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Contributions

HXZ contributed to conception and design; data analysis and interpretation; article drafting; and critical revision of the article for important intellectual content. JPG and ZS contributed to data analysis and interpretation. QKZ, BQ, XCJ, and SL contributed to critical revision of the article for important intellectual content. DS contributed to conception and design; analysis and interpretation of the data; drafting of the article; critical revision of the article for important intellectual content; and final approval of the article.

Corresponding author

Correspondence to Dong Shang.

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Disclosure

Haoxiang Zhang, Jiangping Gao, Zhen Sun, Qingkai Zhang, Bing Qi, Xingchi Jiang, Shuang Li, and Dong Shang declare that they have no competing interests.

Ethical approval

This project was approved by the Ethics Committee of the First Affiliated Hospital of Dalian Medical University (No. PJ-KS-KY-2019–63).

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Zhang, H., Gao, J., Sun, Z. et al. Diagnostic accuracy of updated risk assessment criteria and development of novel computational prediction models for patients with suspected choledocholithiasis. Surg Endosc 37, 7348–7357 (2023). https://doi.org/10.1007/s00464-023-10087-w

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