Personal predictive model based on systemic inflammation markers for estimation of postoperative pancreatic fistula following pancreaticoduodenectomy

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.

Impact of perioperative immunonutrition on postoperative outcomes in pancreaticoduodenectomy: a systematic review and meta-analysis of randomized controlled trials

BACKGROUND

This systematic review and meta-analysis aimed to evaluate the impact of perioperative immunonutrition on postoperative outcomes in patients undergoing pancreaticoduodenectomy (PD).

METHODS

Conducted a comprehensive search in PubMed, Embase, Cochrane Library, Medline, and Web of Science databases to identify all randomized controlled trials (RCTs) on the topic of immunonutrition and PD. Subsequently screened literature, extracted data, and assessed the risk of bias in the included studies, and finally conducted a meta-analysis using RevMan 5.3 software.

RESULTS

The analysis included a total of 10 RCTs with 574 patients, among whom 288 were in the immunonutrition group and 283 in the control group. The meta-analysis revealed a significantly lower incidence of postoperative infection-related complications (OR = 0.45; 95% CI: 0.27-0.74; P = 0.002) and severe postoperative complications (OR = 0.61; 95% CI: 0.38-0.98; P = 0.04) in the immunonutrition group compared to the control group. Additionally, patients in the immunonutrition group had a significantly shorter length of hospital stay (MD= -1.87; 95%CI -3.29 - -0.44; P = 0.01). However, the analysis revealed no statistically significant difference in the overall complication rate between the two groups (P = 0.67). Furthermore, the incidence of specific complications and perioperative mortality rates also did not demonstrate any statistically significant differences (all P > 0.05).

CONCLUSIONS

Perioperative immunonutrition in PD patients can reduce postoperative infection-related complications, but more high-quality RCTs are needed for further validation.

Comparison of Controlling Nutritional Status Score with Bedside Index for Severity in Acute Pancreatitis Score and Atlanta Classification for Mortality in Patients with Acute Pancreatitis

Background/Objectives: Acute pancreatitis (AP) is characterized by pancreatic gland inflammation, and its clinical course ranges from mild to severe. Predicting the severity of AP early and reliably is important. In this study, we investigate the potential use of the Controlling Nutritional Status (CONUT) score as a prognostic marker in acute pancreatitis. Methods: We examined 336 patients who had been hospitalized with an AP diagnosis in the internal medicine clinic. The patients included in the study were followed up for 5 years. The study analyzed the specific variables of age, gender, and AP etiology as recorded biochemical parameters for all study participants and calculated the effects of age, sex, Bedside Index of Severity in AP (BISAP), the revised Atlanta classification, and the CONUT score on mortality. Results: When compared with surviving patients, non-surviving patients had higher scores for BISAP, CONUT, and the Atlanta Classification (p ˂ 0.001). In the non-surviving group, hemoglobin, lymphocyte, and albumin levels were significantly lower and creatinine, uric acid, and procalcitonin levels were significantly higher compared to the surviving group (p ˂ 0.001, 0.003, ˂0.001, ˂0.001, 0.005, ˂0.001, respectively). The multivariate analysis showed a significant association of mortality with age, CONUT, and BISAP scores (p ˂ 0.003, 0.001, 0.012 respectively). The CONUT score was separated into two groups based on the median value. The predicted survival time in the group with a CONUT score > 2 (53.8 months) was significantly lower than in the group with a CONUT score ≤ 2 (63.8 months). The cumulative incidence of all-cause mortality was significantly higher in the patients with higher CONUT scores. Conclusions: This study has assigned the CONUT score as an independent risk factor for mortality in AP.

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 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.