Published online Sep 7, 2022. doi: 10.3748/wjg.v28.i33.4846
Peer-review started: June 12, 2022
First decision: July 12, 2022
Revised: July 25, 2022
Accepted: August 16, 2022
Article in press: August 16, 2022
Published online: September 7, 2022
Processing time: 80 Days and 6.1 Hours
The frequency of acute hypertriglyceridemic pancreatitis (AHTGP) is in-creasing worldwide. AHTGP may be associated with a more severe clinical course and greater mortality than pancreatitis caused by other causes. Early identification of patients with severe inclination is essential for clinical deci-sion-making and improving prognosis. Hence, constructing a risk prediction score with high predictive accuracy and clinical utility for assessing the severity of AHTGP patients is of great importance.
Early prediction and detection of AHTGP patients who are likely to develop severe acute pancreatitis (SAP) is of great importance. Almost of existing clinical scores were developed for all etiologies of pancreatitis and not for hypertriglyceridemia (HTG)-induced pancreatitis separately. To the best of our knowledge, this is the first study attempting to develop a risk prediction score for HTG-induced pancreatitis. This risk score may help guide clinical decisions for these patients.
The purpose of this study was to establish a risk prediction score with easy use and high performance for predicting the severity of AHTGP patients in China, which will help doctors make rational clinical decisions.
We performed a retrospective study of patients with AHTGP. Least absolute shrinkage and selection operator and logistic regression were used to screen predictive variables to construct a nomogram for predicting the severity of AHTGP. The predictive accuracy of the nomogram was estimated using the concordance index. The performance of the nomogram was estimated using a calibration curve. We evaluated the predictive accuracy and net benefit of the risk score and compared it with existing scoring systems via receiver operating characteristic curve analysis and decision curve analysis. We used the best cutoff value for SAP to determine the risk stratification classification.
A risk prediction score consisting of three predictors commonly measured on admission was constructed to predict the severity of SAP. More importantly, our nomogram exhibited high predictive accuracy and good performance. In addition, our nomogram has shown improved prognostic reliability, accuracy and the best net benefit when compared to other clinical scoring systems, such as Bedside Index of Severity in AP, Ranson, Acute Physiology and Chronic Health Evaluation II, modified computed tomography severity index and an artificial intelligence model, the early achievable severity index prediction score. Moreover, the risk prediction score could distinguish patients into low-risk and high-risk groups according to the best cutoff point. The cutoff point can help doctors in making medical decisions.
This risk prediction score have potential usefulness in predicting the presence of SAP at an early stage. It could be of great value in guiding clinical decisions as a convenient and specific tool and optimizing the use of medical resources by supporting appropriate treatment.
To the best of our knowledge, this is the first study attempting to develop a risk prediction score for HTG-induced pancreatitis. But, this was a single-center study with a small sample size, which lacked multi-center data verification. The next step is to conduct a multicenter prospective cohort study with a large sample size to construct specific risk score and externally validate the risk score prior to clinical use.