Impact of postoperative pancreatic fistula on outcomes in pancreatoduodenectomy: a comprehensive analysis of American College of Surgeons National Surgical Quality Improvement Program data

BACKGROUND

Pancreatoduodenectomy (PD) is a major surgical procedure associated with significant risks, particularly postoperative pancreatic fistula (POPF). Studies have highlighted the importance of certain risk factors for POPF, which are crucial for surgical decision-making and the management of high-risk patients undergoing PD. This study aimed to assess the surgical outcomes of patients undergoing PD who met the International Study Group of Pancreatic Surgery - Class D (ISGPS-D) criteria.

METHODS

This study analyzed American College of Surgeons National Surgical Quality Improvement Program data (2014-2021) for patients undergoing ISGPS-D PD, classified as having a soft pancreatic texture and a pancreatic duct of ≤3 mm. This study focused on mortality rates and the correlation between several factors and POPF (ISGPS grade B/C).

RESULTS

From 5964 patients who underwent PD and met the ISGPS-D criteria, the 30-day mortality rate was 1.98%. Males had a higher incidence of POPF than females (57.42% vs 47.35%, respectively; P < .001). Patients with POPF experienced significantly higher rates of major postoperative complications (Clavien-Dindo grade ≥ IIIa), including thrombosis, pneumonia, sepsis, delayed gastric emptying, wound disruption, infections, and acute renal failure. There was a marked increase in the 30-day readmission and mortality rates in patients with POPF (30.0% vs 17.6% and 3.2% vs 1.4%, respectively; all P < .001). Multivariate analysis highlighted female sex as a protective factor against mortality (odds ratio [OR], 0.47; P < .001) and extended hospital stay (>10 days) as a predictor of increased mortality risk (OR, 2.37; P < .001).

CONCLUSION

This study underscored the significant association between POPF and increased postoperative morbidity and mortality rates. Future efforts should concentrate on refining surgical techniques and improving preoperative assessments to mitigate the risks associated with POPF in patients undergoing PD.

Computed tomography and guidelines-based human-machine fusion model for predicting resectability of the pancreatic cancer

BACKGROUND AND AIM

The study aims to develop a hybrid machine learning model for predicting resectability of the pancreatic cancer, which is based on computed tomography (CT) and National Comprehensive Cancer Network (NCCN) guidelines.

METHOD

We retrospectively studied 349 patients. One hundred seventy-one cases from Center 1 and 92 cases from Center 2 were used as the primary training cohort, and 66 cases from Center 3 and 20 cases from Center 4 were used as the independent test dataset. Semi-automatic module of ITK-SNAP software was used to assist CT image segmentation to obtain three-dimensional (3D) imaging region of interest (ROI). There were 788 handcrafted features extracted for 3D ROI using PyRadiomics. The optimal feature subset consists of three features screened by three feature selection methods as the input of the SVM to construct the conventional radiomics-based predictive model (cRad). 3D ROI was used to unify the resolution by 3D spline interpolation method for constructing the 3D tumor imaging tensor. Using 3D tumor image tensor as input, 3D kernelled support tensor machine-based predictive model (KSTM), and 3D ResNet-based deep learning predictive model (ResNet) were constructed. Multi-classifier fusion ML model is constructed by fusing cRad, KSTM, and ResNet using multi-classifier fusion strategy. Two experts with more than 10 years of clinical experience were invited to reevaluate each patient based on their CECT following the NCCN guidelines to obtain resectable, unresectable, and borderline resectable diagnoses. The three results were converted into probability values of 0.25, 0.75, and 0.50, respectively, according to the traditional empirical method. Then it is used as an independent classifier and integrated with multi-classifier fusion machine learning (ML) model to obtain the human-machine fusion ML model (HMfML).

RESULTS

Multi-classifier fusion ML model's area under receiver operating characteristic curve (AUC; 0.8610), predictive accuracy (ACC: 80.23%), sensitivity (SEN: 78.95%), and specificity (SPE: 80.60%) is better than cRad, KSTM, and ResNet-based single-classifier models and their two-classifier fusion models. This means that three different models have mined complementary CECT feature expression from different perspectives and can be integrated through CFS-ER, so that the fusion model has better performance. HMfML's AUC (0.8845), ACC (82.56%), SEN (84.21%), SPE (82.09%). This means that ML models might learn extra information from CECT that experts cannot distinguish, thus complementing expert experience and improving the performance of hybrid ML models.

CONCLUSION

HMfML can predict PC resectability with high accuracy.

Impact of gender on outcomes following single-level anterior lumbar interbody fusion

Background

There have been a multitude of studies attempting to identify the relationship between gender and postoperative outcomes; however, few studies have examined how this relationship may affect outcomes after anterior lumbar interbody fusion (ALIF) surgery. We aim to better characterize the impact that self-reported gender may have on patient reported outcome measures (PROMs) and achievement rates of minimum clinically important difference (MCID) after ALIF.

Methods

A retrospective database of a single spine surgeon was searched for patients who had undergone single-level ALIF. Indications for surgery including acute trauma, infection, or malignancy were excluded. The population was separated into cohorts by self-reported gender, female or male. PROMs were recorded and compared within groups to their preoperative baselines and between groups. MCID achievement rate was compared between groups.

Results

140 patients were identified for this study, with 68 patients self-identifying as female gender. The male gender cohort was found to have a significantly greater prevalence of hypertension (p = 0.018). Both cohorts showed significant improvement during at least one or more postoperative time points for each evaluated outcome measure (p ≤ 0.048, all). No significant difference in mean PROM scores was noted between cohorts at any time point for any measured outcome. The female gender cohort had significantly greater MCID achievement rates for visual acuity scale (VAS) back pain overall and at the 6-month time point (p ≤ 0.043, both). The female gender cohort also had significantly greater achievement of MCID at the 1-year time point for VAS leg pain (p = 0.017).

Conclusion

Both female and male gender cohorts demonstrated significant improvement in all outcomes measured at one or more postoperative time points. Postoperative outcomes did not differ by gender. MCID achievement was more common in female patients. Female patients may experience more tangible clinical improvement after ALIF compared to male patients.