Development and Validation of a New 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.

Risk Models for Developing Pancreatic Fistula After Pancreatoduodenectomy: Validation in a Nationwide Prospective Cohort

OBJECTIVE

To evaluate the performance of published fistula risk models by external validation, and to identify independent risk factors for postoperative pancreatic fistula (POPF).

BACKGROUND

Multiple risk models have been developed to predict POPF after pancreatoduodenectomy. External validation in high-quality prospective cohorts is, however, lacking or only performed for individual models.

METHODS

A post hoc analysis of data from the stepped-wedge cluster cluster-randomized Care After Pancreatic Resection According to an Algorithm for Early Detection and Minimally Invasive Management of Pancreatic Fistula versus Current Practice (PORSCH) trial was performed. Included were all patients undergoing pancreatoduodenectomy in the Netherlands (January 2018-November 2019). Risk models on POPF were identified by a systematic literature search. Model performance was evaluated by calculating the area under the receiver operating curves (AUC) and calibration plots. Multivariable logistic regression was performed to identify independent risk factors associated with clinically relevant POPF.

RESULTS

Overall, 1358 patients undergoing pancreatoduodenectomy were included, of whom 341 patients (25%) developed clinically relevant POPF. Fourteen risk models for POPF were evaluated, with AUCs ranging from 0.62 to 0.70. The updated alternative fistula risk score had an AUC of 0.70 (95% confidence intervals [CI]: 0.69-0.72). The alternative fistula risk score demonstrated an AUC of 0.70 (95% CI: 0.689-0.71), whilst an AUC of 0.70 (95% CI: 0.699-0.71) was also found for the model by Petrova and colleagues. Soft pancreatic texture, pathology other than pancreatic ductal adenocarcinoma or chronic pancreatitis, small pancreatic duct diameter, higher body mass index, minimally invasive resection and male sex were identified as independent predictors of POPF.

CONCLUSION

Published risk models predicting clinically relevant POPF after pancreatoduodenectomy have a moderate predictive accuracy. Their clinical applicability to identify high-risk patients and guide treatment strategies is therefore questionable.

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 Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis analysis to evaluate the quality of reporting of postoperative pancreatic fistula prediction models after pancreatoduodenectomy: A systematic review

BACKGROUND

Postoperative pancreatic fistula is a frequent and potentially lethal complication after pancreatoduodenectomy. Several models have been developed to predict postoperative pancreatic fistula risk. This study was performed to evaluate the quality of reporting of postoperative pancreatic fistula prediction models after pancreatoduodenectomy using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist that provides guidelines on reporting prediction models to enhance transparency and to help in the decision-making regarding the implementation of the appropriate risk models into clinical practice.

METHODS

Studies that described prediction models to predict postoperative pancreatic fistula after pancreatoduodenectomy were searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The TRIPOD checklist was used to evaluate the adherence rate. The area under the curve and other performance measures were extracted if reported. A quadrant matrix chart is created to plot the area under the curve against TRIPOD adherence rate to find models with a combination of above-average TRIPOD adherence and area under the curve.

RESULTS

In total, 52 predictive models were included (23 development, 15 external validation, 4 incremental value, and 10 development and external validation). No risk model achieved 100% adherence to the TRIPOD. The mean adherence rate was 65%. Most authors failed to report on missing data and actions to blind assessment of predictors. Thirteen models had an above-average performance for TRIPOD checklist adherence and area under the curve.

CONCLUSION

Although the average TRIPOD adherence rate for postoperative pancreatic fistula models after pancreatoduodenectomy was 65%, higher compared to other published models, it does not meet TRIPOD standards for transparency. This study identified 13 models that performed above average in TRIPOD adherence and area under the curve, which could be the appropriate models to be used in clinical practice.

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.

Learning curves of resection and reconstruction procedures in robotic-assisted pancreatoduodenectomy by a single surgeon: a retrospective cohort study of 160 consecutive cases

Background

Robotic-assisted pancreatoduodenectomy (RPD) has been routinely performed in a few of centers worldwide. This study aimed to evaluate the perioperative outcomes and the learning curves of resection and reconstruction procedures in RPD by one single surgeon.

Methods

Consecutive patients undergoing RPD by a single surgeon at the First Affiliated Hospital of Sun Yat-sen University (Guangzhou, China) between July 2016 and October 2022 were included. The perioperative outcomes and learning curves were retrospectively analysed by using cumulative sum (CUSUM) analyses.

Results

One-hundred and sixty patients were included. According to the CUSUM curve, the times of resection and reconstruction procedures were shortened significantly after 30 cases (median, 284 vs 195 min; P < 0.001) and 45 cases (median, 138 vs 120 min; P < 0.001), respectively. The estimated intraoperative blood loss (median, 100 vs 50 mL; P < 0.001) and the incidence of clinically relevant post-operative pancreatic fistula (29.2% vs 12.5%; P = 0.035) decreased significantly after 20 and 120 cases, respectively. There were no significant differences in the total number of lymph nodes examined, post-operative major complications, or post-operative length-of-stay between the two groups.

Conclusions

Optimization of the resection procedure and the acquisition of visual feedback facilitated the performance of RPD. RPD was a safe and feasible procedure in the selected patients.

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.

Development and validation of nomogram to predict lymph node metastasis preoperatively in patients with pancreatic neuroendocrine tumor

BACKGROUND

Routine lymphadenectomy in pancreatic neuroendocrine tumors (pNETs) is debated. There lacks accurate model to predict lymph node metastasis (LNM) preoperatively in pNETs. Therefore, this study aimed at developing a nomogram in predicting LNM in pNETs preoperatively.

METHODS

Patients undergoing surgery from Surveillance, Epidemiology, and End Results (SEER) database (design cohort, n = 2742) and First Affiliated Hospital of Sun Yat-sen University (validation cohort, n = 136) were enrolled. Nomogram was developed based on risk factors determined by logistic regression analyses. The performance of nomogram was evaluated by area under receiver operating characteristics curve (AUC), calibration curve, and decision curve analysis.

RESULTS

In design cohort, 915 of 2742 patients had LNM. Tumor in the pancreatic head, T stage, and tumor size were significantly associated with LNM (all p < 0.05). Prediction of nomogram was accurate with AUC of 0.776 in design cohort and 0.622 in validation cohort. The nomogram showed good agreement between prediction and observation in the design and validation cohort. Based on nomogram-predicted risk, patients with higher risk of LNM had worse overall survival over patients with lower risk of LNM (log-rank p < 0.001).

CONCLUSIONS

The novel nomogram could accurately predict LNM in pNET preoperatively. For patients with high risk of LNM, lymphadenectomy was recommended.

Differential Performance of Machine Learning Models in Prediction of Procedure-Specific Outcomes

BACKGROUND

Procedure-specific complications can have devastating consequences. Machine learning-based tools have the potential to outperform traditional statistical modeling in predicting their risk and guiding decision-making. We sought to develop and compare deep neural network (NN) models, a type of machine learning, to logistic regression (LR) for predicting anastomotic leak after colectomy, bile leak after hepatectomy, and pancreatic fistula after pancreaticoduodenectomy (PD).

METHODS

The colectomy, hepatectomy, and PD National Surgical Quality Improvement Program (NSQIP) databases were analyzed. Each dataset was split into training, validation, and testing sets in a 60/20/20 ratio, with fivefold cross-validation. Models were created using NN and LR for each outcome. Models were evaluated primarily with area under the receiver operating characteristic curve (AUROC).

RESULTS

A total of 197,488 patients were included for colectomy, 25,403 for hepatectomy, and 23,333 for PD. For anastomotic leak, AUROC for NN was 0.676 (95% 0.666-0.687), compared with 0.633 (95% CI 0.620-0.647) for LR. For bile leak, AUROC for NN was 0.750 (95% CI 0.739-0.761), compared with 0.722 (95% CI 0.698-0.746) for LR. For pancreatic fistula, AUROC for NN was 0.746 (95% CI 0.733-0.760), compared with 0.713 (95% CI 0.703-0.723) for LR. Variables related to intra-operative information, such as surgical approach, biliary reconstruction, and pancreatic gland texture were highly important for model predictions.

DISCUSSION

Machine learning showed a marginal advantage over traditional statistical techniques in predicting procedure-specific outcomes. However, models that included intra-operative information performed better than those that did not, suggesting that NSQIP procedure-targeted datasets may be strengthened by including relevant intra-operative information.

Development and validation of a nomogram for predicting post-operative abdominal infection in patients undergoing pancreaticoduodenectomy

AIM

The aim of this retrospective study is to develop and validate a predictive nomogram for predicting the risk of post-operative abdominal infection (PAI) in patients undergoing pancreaticoduodenectomy (PD).

METHODS

A total of 360 patients who underwent PD were enrolled into this research and randomly divided into the development and validation group. The clinical data of patients were statistically compared and the nomogram was constructed based on the results of multivariate logistic regression analysis and stepwise (stepAIC) selection. The nomogram was internally and crossly validated by the development and validation cohort. The discriminatory ability of the nomogram was estimated by AUC (Area Under the receiver operating characteristic Curve), calibration curve and decision curve analysis.

RESULTS

After PD, post-operative abdominal infection occurred in 33.89% (n = 122) of patients. The nomogram showed that preoperative biliary drainage and C-reactive protein (CRP), direct bilirubin (DB), alkaline phosphatase (AKP) levels on the 3rd postoperative day (POD3) were independent prognostic factors for abdominal infection after PD. The internal and cross validation of Receiver Operating Characteristic (ROC) curve was statistically significant (AUC = 0.723 and 0.786, respectively). The calibration curves showed good agreement between nomogram predictions and actual observations. The decision curves showed that the nomogram was of great clinical value.

CONCLUSION

A nomogram based on perioperative risk factors such as preoperative biliary drainage, CRP, DB and AKP could simply and accurately predict the risk degree of PAI in patients undergoing PD.

Risk factors of clinically relevant postoperative pancreatic fistula after pancreaticoduodenectomy: A systematic review and meta-analysis

BACKGROUND

Clinically relevant postoperative pancreatic fistula (CR-POPF) is a common and troublesome complication after pancreatoduodenectomy (PD). We conducted a systematic review and meta-analysis to identify the risk factors of CR-POPF after PD.

METHODS

We searched PubMed, EMBASE, and Cochrane Library databases for studies related to risk factors of CR-POPF after PD. Odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were extracted from the included studies, then a meta-analysis was conducted. If necessary, sensitivity analysis would be performed by changing the effect model or excluding 1 study at a time. Publication bias was assessed by funnel plot and Begg test and Egger test.

RESULTS

A total of 27 studies with 24,740 patients were included, and CR-POPF occurred in 3843 patients (incidence = 17%, 95% CI: 16%-19%). Male (OR = 1.56, 95% CI: 1.42-1.70), body mass index >25 kg/m2 (OR = 1.98, 95% CI: 1.23-3.18), pancreatic duct diameter <3 mm (OR = 1.87, 95% CI: 1.66-2.12), soft pancreatic texture (OR = 3.49, 95% CI: 2.61-4.67), and blood transfusion (OR = 3.10, 95% CI: 2.01-4.77) can significantly increase the risk of CR-POPF. Pancreatic adenocarcinoma (OR = 0.54, 95% CI: 0.47-0.61), vascular resection (OR = 0.57, 95% CI: 0.39-0.83), and preoperative chemoradiotherapy (OR = 0.68, 95% CI: 0.57-0.81) can significantly decrease the factor of CR-POPF. Diabetes mellitus was not statistically associated with CR-POPF (OR = 0.66, 95% CI: 0.40-1.08). However, the analysis of body mass index, pancreatic texture, and diabetes mellitus had a high heterogeneity, then sensitivity analysis was performed, and the result after sensitivity analysis showed diabetes mellitus can significantly decrease the risk of CR-POPF. There was no significant publication bias in this meta-analysis.

CONCLUSIONS

The current review assessed the effects of different factors on CR-POPF. This can provide a basis for the prevention and management of CR-POPF. Effective interventions targeting the above risk factors should be investigated in future studies for decreasing the occurrence of CR-POPF.

Establishment and Verification of Prognostic Nomograms for Young Women With Breast Cancer Bone Metastasis

Purpose

The prognosis of patients with metastatic breast cancer usually varies greatly among individuals. At present, the application of nomogram is very popular in metastatic tumors. The present study was conducted to identify independent survival predictors and construct nomograms among young women with breast cancer bone metastasis (BCBM).

Patients and Methods

We searched the Surveillance, Epidemiology, and End Results (SEER) database to identify young women diagnosed with BCBM between 2010 and 2016. We first analyzed the potential risk factors of overall survival (OS) and cancer-specific survival (CSS) by applying univariate Cox regression analysis. Then we conducted multivariate Cox analysis to identify independent survival predictors. Based on significant independent predictors, we developed and validated novel prognostic nomograms by using the R version 4.1.0 software.

Results

We finally identified 715 eligible young women with BCBM for survival analysis, of which 358 patients were in the training set, and 357 patients in the validation set. Approximately four-fifths of patients are between 31 and 40 years old. The 5-year OS and CSS rates of this research population were 41.9 and 43.3%, respectively. Multivariate analysis revealed seven independent predictors of both OS and CSS, including race, tumor subtype, tumor size, surgical treatment, brain metastasis, liver metastasis, and lung metastasis. Based on these predictors, we developed and validated OS and CSS nomograms. The C-index of the OS nomogram reached 0.728 and 0.73 in the training and validation sets, respectively. The C-index of the CSS nomogram reached 0.743 and 0.695 in the training and validation sets, respectively. Meanwhile, high quality calibration plots were revealed in both OS and CSS nomograms.

Conclusion

The current novel nomograms can provide an individualized survival evaluation of young women with BCBM and instruct clinicians to treat them appropriately.

Drain Lipase Levels and Decreased Rate of Drain Amylase Levels as Independent Predictors of Pancreatic Fistula with Nomogram After Pancreaticoduodenectomy

BACKGROUND

Pancreaticoduodenectomy (PD) has recently been improved due to its increased safety. However, postoperative pancreatic fistula (POPF) remains a lethal complication of PD. Identifying novel clinicophysiological risk factors for POPF during the early post-PD period would help improve patient morbidity and mortality. Therefore, this retrospective study aimed to evaluate possible risk factors during the early postoperative period after pancreaticoduodenectomy (PD).

METHODS

Data from 349 patients who underwent PD between 2007 and 2012 were examined retrospectively. All patients were classified into 2 groups: group A, patients without fistulae or biochemical leaks (288 patients), and group B, those with grade B or C POPF (61 patients). Data on various clinicophysiological parameters, including serum and drain laboratory data, were collected. Univariate and multivariate analyses were performed to evaluate POPF predictors. A predictive nomogram was established for these results.

RESULTS

Univariate analysis showed that various serum and drain-related factors, such as white blood cell count, C-reactive protein levels, drain amylase (DAMY) levels, and drain lipase (DLIP) levels, were possible POPF risk factors. Multivariate analysis confirmed that postoperative day (POD) 1 DLIP levels (hazard ratio, 15.393; p = 0.037) and decreased rate (POD3/1) of DAMY levels (hazard ratio, 4.415; p = 0.028) were independent risk factors. Further, POD1 DLIP levels and decreased rate of DAMY levels were significantly lower in group A than in group B. The accuracy of nomogram was 0.810.

CONCLUSIONS

POD1 DLIP levels (> 245 U/mL) and decreased rate of DAMY levels (> 0.44) were POPF risk factors, making them possible biomarkers for POPF.