Comprehensive Diagnostic Nomogram for Predicting Clinically Relevant Postoperative Pancreatic Fistula After Pancreatoduodenectomy

Optimizing prediction models for pancreatic fistula after pancreatectomy: Current status and future perspectives

Postoperative pancreatic fistula (POPF) is a frequent complication after pancreatectomy, leading to increased morbidity and mortality. Optimizing prediction models for POPF has emerged as a critical focus in surgical research. Although over sixty models following pancreaticoduodenectomy, predominantly reliant on a variety of clinical, surgical, and radiological parameters, have been documented, their predictive accuracy remains suboptimal in external validation and across diverse populations. As models after distal pancreatectomy continue to be progressively reported, their external validation is eagerly anticipated. Conversely, POPF prediction after central pancreatectomy is in its nascent stage, warranting urgent need for further development and validation. The potential of machine learning and big data analytics offers promising prospects for enhancing the accuracy of prediction models by incorporating an extensive array of variables and optimizing algorithm performance. Moreover, there is potential for the development of personalized prediction models based on patient- or pancreas-specific factors and postoperative serum or drain fluid biomarkers to improve accuracy in identifying individuals at risk of POPF. In the future, prospective multicenter studies and the integration of novel imaging technologies, such as artificial intelligence-based radiomics, may further refine predictive models. Addressing these issues is anticipated to revolutionize risk stratification, clinical decision-making, and postoperative management in patients undergoing pancreatectomy.

Development and validation of a novel nomogram to predict postoperative pancreatic fistula after pancreatoduodenectomy using lasso-logistic regression: an international multi-institutional observational study

BACKGROUND

Existing prediction models for clinically relevant postoperative pancreatic fistula (POPF) after pancreatoduodenectomy (PD) lack discriminatory power or are too complex. This study aimed to develop a simple nomogram that could accurately predict clinically relevant POPF after PD.

METHODS

A high-volume, multicenter cohort of patients who underwent PD from the American College of Surgeons-National Surgical Quality Improvement Program database in the United States during 2014-2017 was used as the model training cohort ( n =3609), and patients who underwent PD from the Pancreatic Center of the National Cancer Center Hospital in China during 2014-2019 were used as the external validation cohort ( n =1347). The study used lasso penalized regression to screen large-scale variables, then logistic regression was performed to screen the variables and build a model. Finally, a prediction nomogram for clinically relevant POPF was established based on the logistic model, and polynomial equations were extracted. The performance of the nomogram was evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis.

RESULTS

In the training and validation cohorts, there were 16.7% (601/3609) and 16.6% (224/1347) of patients who developed clinically relevant POPF, respectively. After screening using lasso and logistic regression, only six predictors were independently associated with clinically relevant POPF, including two preoperative indicators (weight and pancreatic duct size), one intraoperative indicator (pancreatic texture), and three postoperative indicators (deep surgical site infection, delayed gastric emptying, and pathology). The prediction of the new nomogram was accurate, with an area under the curve of 0.855 (95% CI: 0.702-0.853) in the external validation cohort, and the predictive performance was superior to three previously proposed POPF risk score models (all P <0.001, likelihood ratio test).

CONCLUSIONS

A reliable lasso-logistic method was applied to establish a novel nomogram based on six readily available indicators, achieving a sustained, dynamic, and precise POPF prediction for PD patients. With a limited number of variables and easy clinical application, this new model will enable surgeons to proactively predict, identify, and manage pancreatic fistulas to obtain better outcomes from this daunting postoperative complication.

A Machine Learning Approach to Predict Postoperative Pancreatic Fistula After Pancreaticoduodenectomy Using Only Preoperatively Known Data

BACKGROUND

Clinically-relevant postoperative pancreatic fistula (CR-POPF) following pancreaticoduodenectomy (PD) is a major postoperative complication and the primary determinant of surgical outcomes. However, the majority of current risk calculators utilize intraoperative and postoperative variables, limiting their utility in the preoperative setting. Therefore, we aimed to develop a user-friendly risk calculator to predict CR-POPF following PD using state-of-the-art machine learning (ML) algorithms and only preoperatively known variables.

METHODS

Adult patients undergoing elective PD for non-metastatic pancreatic cancer were identified from the ACS-NSQIP targeted pancreatectomy dataset (2014-2019). The primary endpoint was development of CR-POPF (grade B or C). Secondary endpoints included discharge to facility, 30-day mortality, and a composite of overall and significant complications. Four models (logistic regression, neural network, random forest, and XGBoost) were trained, validated and a user-friendly risk calculator was then developed.

RESULTS

Of the 8666 patients who underwent elective PD, 13% (n = 1160) developed CR-POPF. XGBoost was the best performing model (AUC = 0.72), and the top five preoperative variables associated with CR-POPF were non-adenocarcinoma histology, lack of neoadjuvant chemotherapy, pancreatic duct size less than 3 mm, higher BMI, and higher preoperative serum creatinine. Model performance for 30-day mortality, discharge to a facility, and overall and significant complications ranged from AUC 0.62-0.78.

CONCLUSIONS

In this study, we developed and validated an ML model using only preoperatively known variables to predict CR-POPF following PD. The risk calculator can be used in the preoperative setting to inform clinical decision-making and patient counseling.

Serum Creatinine and Amylase in Drain to Predict Pancreatic Fistula Risk after Pancreatoduodenectomy

INTRODUCTION

The identification of patients with low risk of clinically relevant postoperative pancreatic fistula (CR-POPF) and postoperative hemorrhage (PPH) can guide drain removal after pancreatoduodenectomy (PD). However, drain fluid amylase (DFA) ≤5,000 U/L on postoperative day (POD) 1 does not robustly predict the absence of CR-POPF.

METHODS

Consecutive patients undergoing PD at Sun Yat-sen University Cancer Center between July 2018 and October 2021 were analyzed. Recursive partitioning analysis was used to classify patients into groups with different risks of CR-POPF and PPH.

RESULTS

Among 288 consecutive patients included, 99 patients (34.38%) developed CR-POPF (86 grade B and 13 grade C). Patients with CR-POPF had increased levels of preoperative creatinine (CRE) and POD1 CRE. The combination of POD1 CRE (>104 μmol/L or not) and POD1 DFA (>5,000 U/L or not) stratified patients into subgroups with the maximum difference in CR-POPF risk. The CR-POPF rates were 17.82% (36/202) in group A (POD1 CRE ≤104 μmol/L and POD1 DFA ≤5,000 U/L), 53.33% (8/15) in group B (POD1 CRE >104 μmol/L and POD1 DFA ≤5,000 U/L), and 77.46% (55/71) in group C (POD1 DFA >5,000 U/L). The PPH rates were 1.98% (4/202), 20.00% (3/15), and 19.72% (14/71) in groups A, B, and C, respectively.

CONCLUSION

Patients with POD1 DFA ≤5,000 U/L and POD1 CRE >104 μmol/L have a high risk of CR-POPF and may not benefit from early drain removal. Patients with POD1 DFA ≤5,000 U/L and POD1 CRE ≤104 μmol/L have low risk of CR-POPF and PPH.

Drain fluid biomarkers for the diagnosis of clinically relevant postoperative pancreatic fistula: a diagnostic accuracy systematic review and meta-analysis

BACKGROUND

Pancreatectomy is the only curative treatment available for pancreatic cancer and a necessity for patients with challenging pancreatic pathology. To optimize outcomes, postsurgical complications such as clinically relevant postoperative pancreatic fistula (CR-POPF) should be minimized. Central to this is the ability to predict and diagnose CR-POPF, potentially through drain fluid biomarkers. This study aimed to assess the utility of drain fluid biomarkers for predicting CR-POPF by conducting a diagnostic test accuracy systematic review and meta-analysis.

METHODS

Five databases were searched for relevant and original papers published from January 2000 to December 2021, with citation chaining capturing additional studies. The QUADAS-2 tool was used to assess the risk of bias and concerns regarding applicability of the selected studies.

RESULTS

Seventy-eight papers were included in the meta-analysis, encompassing six drain biomarkers and 30 758 patients with a CR-POPF prevalence of 17.42%. The pooled sensitivity and specificity for 15 cut-offs were determined. Potential triage tests (negative predictive value >90%) were identified for the ruling out of CR-POPF and included postoperative day 1 (POD1) drain amylase in pancreatoduodenectomy (PD) patients (300 U/l) and in mixed surgical cohorts (2500 U/l), POD3 drain amylase in PD patients (1000-1010 U/l) and drain lipase in mixed surgery groups (180 U/l). Notably, drain POD3 lipase had a higher sensitivity than POD3 amylase, while POD3 amylase had a higher specificity than POD1.

CONCLUSIONS

The current findings using the pooled cut-offs will offer options for clinicians seeking to identify patients for quicker recovery. Improving the reporting of future diagnostic test studies will further clarify the diagnostic utility of drain fluid biomarkers, facilitating their inclusion in multivariable risk-stratification models and the improvement of pancreatectomy outcomes.

Risk factor analysis and prediction of postoperative clinically relevant pancreatic fistula after distal pancreatectomy

OBJECTIVE

Postoperative pancreatic fistula (POPF) following distal pancreatectomy (DP) is a serious complication. In the present study, we aimed to identify the risk factors associated with clinically relevant postoperative pancreatic fistula (CR-POPF) and establish a nomogram model for predicting CR-POPF after DP.

METHODS

In total, 115 patients who underwent DP at the General Hospital of Northern Theater Command between January 2005 and December 2020 were retrospectively studied. Univariate and multivariable logistic regression analyses were used to identify the independent risk factors associated with CR-POPF. Then, a nomogram was formulated based on the results of multivariable logistic regression analysis. The predictive performance was evaluated with receiver operating characteristic (ROC) curves. Decision curve and clinical impact curve analyses were used to validate the clinical application value of the model.

RESULTS

The incidence of CR-POPF was 33.0% (38/115) in the present study. Multivariate logistic regression analysis identified the following variables as independent risk factors for POPF: body mass index (BMI) (OR 4.658, P = 0.004), preoperative albumin level (OR 7.934, P = 0.001), pancreatic thickness (OR 1.256, P = 0.003) and pancreatic texture (OR 3.143, P = 0.021). We created a nomogram by incorporating the above mentioned risk factors. The nomogram model showed better predictive value, with a concordance index of 0.842, sensitivity of 0.710, and specificity of 0.870 when compared to each risk factor. Decision curve and clinical impact curve analyses also indicated that the nomogram conferred a high clinical net benefit.

CONCLUSION

Our nomogram could accurately and objectively predict the risk of postoperative CR-POPF in individuals who underwent DP, which could help clinicians with early identification of patients who might develop CR-POPF and early development of a suitable fistula mitigation strategy and postoperative management.

Effects of Dexmedetomidine on Systemic Inflammation and Postoperative Complications in Laparoscopic Pancreaticoduodenectomy: A Double-blind Randomized Controlled Trial

BACKGROUND

Laparoscopic pancreaticoduodenectomy (LPD) may induce intense inflammatory response which might be related to the patient's outcomes. Clinical dexmedetomidine (DEX) has been widely used for opioid-sparing anesthesia and satisfactory sedation. The objective of this study was to investigate the influence of DEX on inflammatory response and postoperative complications in LPD.

METHODS

Ninety-nine patients undergoing LPD were randomly assigned to two groups: normal saline (NS) and DEX. The primary outcome was the neutrophil-to-lymphocyte ratio (NLR) differences postoperatively within 48 h. Secondary outcomes were postoperative complications, the length of postoperative hospital stay and the incidence of ICU admission. Other outcomes included anesthetics consumption and intraoperative vital signs.

RESULTS

NLR at postoperative day 2 to baseline ratio decreased significantly in the DEX group (P = 0.032). Less major complications were observed in the DEX group such as pancreatic fistula, delayed gastric emptying and intra-abdominal infection (NS vs. DEX, 21.7% vs. 13.6%, P = 0.315; 10.9% vs. 2.3%, P = 0.226; 17.4% vs. 11.4%, P = 0.416, respectively) though there were no statistical differences. Three patients were transferred to the ICU after surgery in the NS group, while there was none in the DEX group (P = 0.242). The median postoperative hospital stay between groups were similar (P = 0.313). Both intraoperative propofol and opioids were less in the DEX group (P < 0.05).

CONCLUSIONS

Intraoperative DEX reduced the early postoperative inflammatory response in LPD. It also reduced the use of narcotics that may related to reduced major complications, which need additional research further.

Drain fluid biomarkers for prediction and diagnosis of clinically relevant postoperative pancreatic fistula: A narrative review

Clinically relevant postoperative pancreatic fistula (CR-POPF) has continued to compromise patient recovery post-pancreatectomy despite decades of research seeking to improve risk prediction and diagnosis. The current diagnostic criteria for CR-POPF requires elevated drain fluid amylase to present alongside POPF-related complications including infection, haemorrhage and organ failure. These worrying sequelae necessitate earlier and easily obtainable biomarkers capable of reflecting evolving CR-POPF. Drain fluid has recently emerged as a promising source of biomarkers as it is derived from the pancreas and hence, capable of reflecting its postoperative condition. The present review aims to summarise the current knowledge of CR-POPF drain fluid biomarkers and identify gaps in the field to invigorate future research in this critical area of clinical need. These findings may provide robust diagnostic alternatives for CR-POPF and hence, to clarify their clinical utility require further reports detailing their diagnostic and/or predictive accuracy.