Predictive nomogram for postoperative pancreatic fistula following pancreaticoduodenectomy: a retrospective study

Risk stratification of postoperative pancreatic fistula and other complications following pancreatoduodenectomy. How far are we? A scoping review

PURPOSE

Pancreatoduodenectomy (PD) is a challenging procedure which is associated with high morbidity rates. This study was performed to make an overview of risk factors included in risk stratification methods both logistic regression models and models based on artificial intelligence algorithms to predict postoperative pancreatic fistula (POPF) and other complications following PD and to provide insight in the extent to which these tools were validated.

METHODS

Five databases were searched to identify relevant studies. Calculators, equations, nomograms, and artificial intelligence models that addressed POPF and other complications were included. Only PD resections were considered eligible. There was no exclusion of the minimally invasive techniques reporting PD resections. All other pancreatic resections were excluded.

RESULTS

90 studies were included. Thirty-five studies were related to POPF, thirty-five studies were related to other complications following PD and twenty studies were related to artificial intelligence predication models after PD. Among the identified risk factors, the most used factors for POPF risk stratification were the main pancreatic duct diameter (MPD) (80%) followed by pancreatic texture (51%), whereas for other complications the most used factors were age (34%) and ASA score (29.4%). Only 26% of the evaluated risk stratification tools for POPF and other complications were externally validated. This percentage was even lower for the risk models using artificial intelligence which was 20%.

CONCLUSION

The MPD was the most used factor when stratifying the risk of POPF followed by pancreatic texture. Age and ASA score were the most used factors for the stratification of other complications. Insight in clinically relevant risk factors could help surgeons in adapting their surgical strategy and shared decision-making. This study revealed that the focus of research still lies on developing new risk models rather than model validation, hampering clinical implementation of these tools for decision support.

A novel model based on clinical and computed tomography (CT) indices to predict the risk factors of postoperative major complications in patients undergoing pancreaticoduodenectomy

Background

Postoperative complications are prone to occur in patients after radical pancreaticoduodenectomy (PD). This study aimed to construct and validate a model for predicting postoperative major complications in patients after PD.

Methods

The clinical data of 360 patients who underwent PD were retrospectively collected from two centers between January 2019 and December 2023. Visceral adipose volume (VAV) and subcutaneous adipose volume (SAV) were measured using three-dimensional (3D) computed tomography (CT) reconstruction. According to the Clavien-Dindo classification system, the postoperative complications were graded. Subsequently, a predictive model was constructed based on the results of least absolute shrinkage and selection operator (LASSO) multivariate logistic regression analysis and stepwise (stepAIC) selection. The nomogram was internally validated by the training and test cohort. The discriminatory ability and clinical utility of the nomogram were evaluated by area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA).

Results

The major complications occurred in 13.3% (n = 48) of patients after PD. The nomogram revealed that high VAV/SAV, high system inflammation response index (SIRI), high triglyceride glucose-body mass index (TyG-BMI), low prognostic nutritional index (PNI) and CA199 ≥ 37 were independent risk factors for major complications. The C-index of this model was 0.854 (95%CI [0.800-0.907]), showing excellent discrimination. The calibration curve demonstrated satisfactory concordance between nomogram predictions and actual observations. The DCA curve indicated the substantial clinical utility of the nomogram.

Conclusion

The model based on clinical and CT indices demonstrates good predictive performance and clinical benefit for major complications in patients undergoing PD.

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.

A nomogram for predicting in-hospital overall survival of hypertriglyceridemia-induced severe acute pancreatitis: A single center, cross-sectional study

Background

Hypertriglyceridemia-induced severe acute pancreatitis (HTG-SAP) is a type of pancreatitis characterized by an abnormal elevation of plasma triglyceride. HTG-SAP has been associated with various complications and a high mortality rate. In this study, we established a nomogram for predicting the overall survival (OS) of HTG-SAP patients during hospitalization.

Methods

128 HTG-SAP cases hospitalized at the Affiliated Huadu Hospital, Southern Medical University, from 2019 to 2022 were analyzed retrospectively. A nomogram including prognostic factors correlated with OS during hospitalization was established by multivariate Cox regression analysis. We internally validated the nomogram using time-dependent (at 1-, 2-, and 3- months) survival receiver operating characteristic (SROC) and calibration curve with 500 iterations of bootstrap resampling. Time-dependent decision curve analysis (DCA) was employed to validate the clinical value of the nomogram.

Results

Multivariate Cox regression indicated that serum triglyceride, red blood cell distribution width (RDW), lactic acid, and interleukin-6 (IL6) were independent prognostic factors for OS of HTG-SAP patients during hospitalization and were used to construct a nomogram. The time-dependent area under the curve (AUC) values at 1-, 2-, and 3- months were 0.946, 0.913, and 0.929, respectively, and the Concordance index (C-index) of the nomogram was 0.916 (95%CI 0.871-0.961). The time-dependent calibration curves indicated good consistency between the observed and predicted outcomes. The time-dependent DCAs also revealed that the nomogram yielded a high clinical net benefit. After stratifying the included cases into two risk groups based on the risk score obtained from the nomogram, the high-risk group exhibited a significantly inferior overall survival (OS) compared to the low-risk group (p < 0.0001).

Conclusions

Our nomogram exhibited good performance in predicting the overall survival of HTG-SAP patients during hospitalization.

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.

Development and validation of a network calculator model for safety and efficacy after pancreaticoduodenectomy in the elderly patients with pancreatic head cancer

BACKGROUND

Benefiting from increased life expectancy and improved perioperative management, more elderly patients with pancreatic head cancer (PHC) underwent pancreaticoduodenectomy (PD). However, individualized predictive models for the safety and efficacy of PD is still lacking. this study aimed to developed three safety- and efficacy-related risk calculators for elderly (> = 65 years) PHC patients.

METHODS

This study was designed with two research cohorts, namely, the training cohort and the validation cohort, and comprises four general steps: (1) Risk factors were analyzed for the incidence of postoperative complications, cancer-specific survival (CSS), and overall survival (OS) in the training cohort (N = 271) using logistic and Cox-regression analysis. (2) Nomograms were then plotted based on the above results. (3) The accuracy of the developed nomogram models was then verified with the validation cohort (N = 134) data using consistency index (C-index) and calibration curves. (4) We then evaluated the efficacy of these nomograms using decision curve analysis (DCA) in both the training and validation cohorts, and ultimately constructed three online calculators based on these nomograms.

RESULTS

We identified ASA, diabetes, smoking, and lymph node invasion as predisposing risk factors for postoperative complications, and the predictive factors that affected both OS and CSS were ASA, diabetes, BMI, CA19-9 level, and tumor diameter. By integrating the above risk factors, we constructed three nomograms on postoperative complication, CSS, and OS. The C-index for complication, CSS, and OS were 0.824, 0.784, and 0.801 in the training cohort and 0.746, 0.718, and 0.708 in the validation cohort. Moreover, the validation curves and DCA demonstrated good calibration and robust compliance in both training and validation cohorts. We then developed three web calculators (https://caiming.shinyapps.io/CMCD/, https://caiming.shinyapps.io/CMCSS/, and https://caiming.shinyapps.io/CMOS/) to facilitate the use of the nomograms.

CONCLUSIONS

The calculators demonstrated promising performance as an tool for predicting the safety and efficacy of PD in elderly PHC patients.

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.

Incidence and risk factors of postoperative acute pancreatitis after pancreaticoduodenectomy: a systematic review and meta-analysis

Background

Postoperative acute pancreatitis (POAP) is a specific complication after pancreatectomy. The acute inflammatory response of the residual pancreas may affect the healing of pancreatoenteric anastomoses, leading to postoperative pancreatic fistulas (POPFs), abdominal infections, and even progressive systemic reactions, conditions that negatively affect patients' prognoses and can cause death. However, to the best of our knowledge, no systematic reviews or meta-analytic studies have assessed the incidence and risk factors of POAP after pancreaticoduodenectomy (PD).

Method

We searched PubMed, Web of Science, Embase, and Cochrane Library databases for relevant literature describing the outcomes of POAP after PD until November 25, 2022, and we used the Newcastle-Ottawa Scale to assess the quality of the studies. Next, we pooled the incidence of POAP and the odds ratios (ORs) and 95% confidence intervals (CIs) of the risk factors using a random-effect meta-analysis. I2 tests were used to assess heterogeneity between the studies.

Results

We analyzed data from 7,164 patients after PD from 23 articles that met the inclusion criteria for this study. The subgroup results of the meta-analysis by different POAP diagnostic criteria showed that the incidences of POAP were 15% (95% CI, 5-38) in the International Study Group for Pancreatic Surgery group, 51% (95% CI, 42-60) in the Connor group, 7% (95% CI, 2-24) in the Atlanta group, and 5% (95% CI, 2-14) in the unclear group. Being a woman [OR (1.37, 95% CI, 1.06-1.77)] or having a soft pancreatic texture [OR (2.56, 95% CI, 1.70-3.86)] were risk factors of POAP after PD.

Conclusion

The results showed that POAP was common after PD, and its incidence varied widely according to different definitions. Large-scale reports are still needed, and surgeons should remain aware of this complication.

Systematic Review Registration

identifier: CRD42022375124.

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.

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.

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.

Machine learning algorithms as early diagnostic tools for pancreatic fistula following pancreaticoduodenectomy and guide drain removal: A retrospective cohort study

OBJECTIVE

Clinically relevant postoperative pancreatic fistula (CR-POPF) remains the major cause of morbidity following pancreaticoduodenectomy (PD). Several model score systems such as the Fistula Risk Score (FRS) have been developed to predict CR-POPF using preoperative and intraoperative data. Machine learning (ML) algorithms are increasingly applied in the medical field and they could be used to assess the risk of CR-POPF, identify clinically meaningful data and guide drain removal.

METHODS

Data from consecutive patients who underwent PD between January 1, 2010 and March 31, 2021 at a single high-volume center was collected retrospectively in this study. Demographics, clinical features, intraoperative parameters, and laboratory values were used to conduct the ML model. Four different ML algorithms (CatBoost, lightGBM, XGBoost and Random Forest) were used to train this model with cross-validation.

RESULTS

A total of 2421 patients with 62 clinical parameters were enrolled in this ML model. The majority of patients (76.3%) underwent open PD while others underwent robot-assisted PD. CR-POPF occurred in 424 (17.5%) patients. The CatBoost algorithm outperformed other algorithms with a mean area under the receiver operating characteristic curve (AUC) of 0.81 (95% confidence interval: 0.80-0.82) from the 5-fold cross-validation procedure. In the test dataset, the CatBoost algorithm also achieved the best mean-AUC of 0.83. The most important value was mean drain fluid amylase (DFA) in the first seven postoperative days (POD). The performance of models that used only preoperative data and intraoperative data was marginally lower than that of models that used combined data.

CONCLUSION

Our ML algorithms could be applied as early diagnostic tools for CR-POPF in patients who underwent PD. Such real-time clinical decision support tools can identify patients with a high risk of CR-POPF, help in developing the perioperative management plan and guide the optimal timing of drain removal.