Predictive nomogram for postoperative pancreatic fistula following pancreaticoduodenectomy: a retrospective study
Jian Shen1, Feng Guo1, Yan Sun1
1Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Insights
A new nomogram predicts postoperative pancreatic fistula (POPF) after pancreaticoduodenectomy (PD) with high accuracy. Albumin difference is identified as a novel, accessible predictor for POPF risk assessment.
Area of Science:
- Surgical Oncology
- Gastroenterology
- Clinical Prediction Models
Background:
- Postoperative pancreatic fistula (POPF) is the most frequent complication after pancreaticoduodenectomy (PD).
- Accurate prediction of POPF risk is crucial for patient management.
- Existing predictive models require enhancement for improved accuracy.
Purpose of the Study:
- To develop and validate an effective predictive nomogram for POPF following PD.
- To identify novel predictors for POPF risk.
Main Methods:
- Retrospective analysis of 459 patients undergoing PD.
- Development of a predictive nomogram using Lasso and multivariable logistic regression.
- Internal validation using AUC and calibration plots; comparison with the a-FRS model.
Main Results:
- The nomogram incorporated albumin difference, drain amylase on postoperative day 1, pancreas texture, and BMI.
- The nomogram demonstrated excellent discrimination (AUC=0.87) and calibration.
- The developed nomogram significantly outperformed the a-FRS model (AUC=0.87 vs 0.62).
Conclusions:
- The developed nomogram provides a reliable tool for assessing individual POPF risk after PD.
- Albumin difference emerges as a new, easily accessible predictor for POPF.
Background:
Postoperative pancreatic fistula (POPF) represents the most common complication following pancreaticoduodenectomy (PD). Predictive models are needed to select patients with a high risk of POPF. This study was aimed to establish an effective predictive nomogram for POPF following PD.
Methods:
Consecutive patients who had undergone PD between January 2016 and May 2020 at a single institution were analysed retrospectively. A predictive nomogram was established based on a training cohort, and Lasso regression and multivariable logistic regression analysis were used to evaluate predictors. The predictive abilities of the predicting model were assessed for internal validation by the area under the receiver operating characteristic curve (AUC) and calibration plot using bootstrap resampling. The performance of the nomogram was compared with that of the currently used a-FRS model.
Results:
A total of 459 patients were divided into a training cohort (n = 302) and a validation cohort (n = 157). No significant difference was observed between the two groups with respect to clinicopathological characteristics. The POPF rate was 16.56%. The risk factors of POPF POPF were albumin difference, drain amylase value on postoperative day 1, pancreas texture, and BMI, which were all selected into a nomogram. Nomogram application revealed good discrimination (AUC = 0.87, 95% CI: 0.81-0.94, P < 0.001) as well as calibration abilities in the validation cohort. The predictive value of the nomogram was better than that of the a-FRS model (AUC: 0.87 vs 0.62, P < 0.001).
Conclusions:
This predictive nomogram could be used to evaluate the individual risk of POPF in patients following PD, and albumin difference is a new, accessible predictor of POPF after PD.
Trial Registration:
This study was registered in the Chinese Clinical Trial Register ( ChiCTR2000034435 ).
