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A New Risk Score to Predict Intensive Care Unit Admission for Patients with Acute Pancreatitis 48 Hours After Admission: Multicenter Study

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Abstract

Aims

The objective of this study was to develop and validate an easy-to-use risk score (APRS) to predict which patients with acute pancreatitis (AP) will need intensive care unit (ICU) treatment within 48 h post-hospitalization on the basis of the ubiquitously available clinical records.

Methods

Patients with acute pancreatitis were retrospectively included from three independent institutions (RM cohort, 5280; TJ cohort, 262; SN cohort, 196), with 56 candidate variables collected within 48 h post-hospitalization. The RM cohort was randomly divided into a training set (N = 4220) and a test set (N = 1060). The most predictive features were extracted by LASSO from the RM cohort and entered into multivariate analysis. APRS was constructed using the coefficients of the statistically significant variables weighted by the multivariable logistic regression model. The APRS was validated by RM, TJ, and SN cohorts. The C-statistic was employed to evaluate the APRS’s discrimination. DeLong test was used to compare area under the receiver operating characteristic curve (AUC) differences.

Results

A total of 5738 patients with AP were enrolled. Eleven variables were selected by LASSO and entered into multivariate analysis. APRS was inferred using the above five factors (pleural effusion, ALT/AST, ALB/GLB, urea, and glucose) weighted by their regression coefficients in the multivariable logistic regression model. The C-statistics of APRS were 0.905 (95% CI 0.82–0.98) and 0.889 (95% CI 0.81–0.96) in RM and TJ validation. An online APRS web-based calculator was constructed to assist the clinician to earlier assess the clinical outcomes of patients with AP.

Conclusion

APRS could effectively stratify patients with AP into high and low risk of ICU admission within 48 h post-hospitalization, offering clinical value in directing management and personalize therapeutic selection for patients with AP.

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Data Availability

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

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Acknowledgments

Special thanks to S.W. and C.J. for their assistance with statistical analysis. Special thanks to L.X.F. and L.Y. for their expert technical assistance with radiological findings.

Funding

This work was funded by the National Natural Science Foundation of China (81901817, 62171230, U1809205, 61771249, 91959207, 81871352 and 92159301); Innovation Seed Funding of Wuhan University (TFZZ2018020); Hubei Provincial Key Laboratory Project (2021KYC0036).

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Author notes

  1. Lei Yuan, Lei Shen, Mengyao Ji, and Xinyu Wen are co-authors.

Authors and Affiliations

  1. School of Automation, Nanjing University of Information Science and Technology, Nanjing, China

    Lei Yuan

  2. Department of Information Center, Wuhan University Renmin Hospital, Wuhan, Hubei, China

    Lei Yuan

  3. Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China

    Lei Yuan & Jun Xu

  4. Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei, China

    Lei Shen, Mengyao Ji, Xinyu Wen & Shuo Wang

  5. Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Pingxiao Huang

  6. Key Laboratory of Hubei Province for Digestive System Disease, Wuhan University Renmin Hospital, Wuhan, Hubei, China

    Lei Yuan, Lei Shen & Mengyao Ji

  7. Suining Central Hospital, Suining, Sichuan, China

    Yong Li

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Contributions

M.Y.J. and L.S. had the idea for and designed the study. L.Y. and X.Y.W. contributed to writing the paper. X.Y.W., S.W., Y.L., and P.X.H. contributed to data collection. L.Y. contributed to the statistical analysis. All authors contributed to data acquisition, data analysis, or data interpretation, and reviewed and approved the final version.

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Correspondence to Jun Xu.

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Yuan, L., Shen, L., Ji, M. et al. A New Risk Score to Predict Intensive Care Unit Admission for Patients with Acute Pancreatitis 48 Hours After Admission: Multicenter Study. Dig Dis Sci 68, 2069–2079 (2023). https://doi.org/10.1007/s10620-022-07768-2

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