A Scoring System for Surgical Site Infection after Pancreaticoduodenectomy Using Clinical Data

Prevalence of and risk factors for surgical site infections after pancreaticoduodenectomy: a systematic review and meta-analysis

Background

Surgical site infections (SSIs) are one of the most common complications after pancreaticoduodenectomy (PD); however, the global prevalence and risk factors for SSIs after PD remain unknown.

Objectives

To investigate the prevalence of and risk factors for SSIs after PD.

Methods

The PubMed, Embase, Cochrane Library, Web of Science, and Science Direct databases were systematically searched from inception to 1 December 2022. Observational studies reporting adjusted odds ratios (ORs) and 95% confidence intervals (CIs) of risk factors for SSIs in patients undergoing PD were included. Two independent reviewers in teams performed data extraction, risk of bias assessment, and level of evidence analysis. The pooled results were estimated using a random-effects model. The I 2 statistic and Q χ 2 statistic were used to assess heterogeneity. Funnel plots, Egger's regression test, and the trim-and-fill method were used to determine publication bias. The primary outcomes were identifying risk factors for SSIs after PD. The secondary outcomes were the pooled prevalence rates of SSIs.

Results

A total of 98 704 patients from 45 studies were included, and 80% of the studies were considered high quality. The estimated pooled prevalence of SSIs was 23% (0.19-0.27, I 2=97%). The prevalence of SSIs was found to be higher in Japan and lower in USA. Preoperative biliary stenting, higher body mass index (BMI), longer operation time, postoperative pancreatic fistula, soft pancreatic texture, perioperative blood transfusion, and cardiac disease were identified as significant risk factors for the development of SSIs after PD. Additionally, broad-spectrum antibiotics were a significant protective factor against SSIs. Subgroup analysis and sensitivity analysis showed that the results were robust.

Conclusion and relevance

The prevalence of SSIs remains high and varies widely among regions. It is necessary to take effective preventive measures and carry out more prospective studies to further verify these results.

Derivation and validation of a novel preoperative risk prediction model for surgical site infection in pancreaticoduodenectomy and comparison of preoperative antibiotics with different risk stratifications in retrospective cohort

BACKGROUND

Surgical site infections (SSIs) are common postoperative complications of pancreaticoduodenectomy.

AIM

To develop a model for preoperative identification of the risk of SSI that may improve outcomes and guide preoperative antibiotics.

METHODS

The prediction model was built by meta-analysis. After literature search and inclusion, data extraction, and quantitative synthesis, the prediction model was established based on the pooled odds ratio of predictors. A single-centre retrospective cohort was the validation cohort. Receiver operating characteristic curves and area under the curve were used to assess the model's ability. We also created a decision curve and a calibration plot to assess the nomogram. The effects of prophylactic antibiotics on SSI were compared between groups by multivariable logistic regression with different risk stratifications.

FINDINGS

Twenty-eight studies were included in the meta-analysis, 17 studies in the derivation cohort. Age, male gender, body mass index, pancreatic duct diameter, high-risk diagnosis, and preoperative biliary drainage were selected to build the prediction model. The model was validated in an external cohort. The cut-off value was 3.5 and area under the curve (AUC) was 0.76 in open pancreaticoduodenectomy (OPD). In laparoscopic pancreaticoduodenectomy, the cut-off value was 4.5 and AUC was 0.69. Decision curve and calibration plot showed good usability of the model, especially in OPD. Multivariable logistic regression did not indicate differences between broad- and narrow-spectrum antibiotics for SSI in different risk stratifications.

CONCLUSION

The model can identify patients with a high risk of SSI preoperatively. The choice of prophylactic antibiotics under different risk stratifications should be investigated further.

Artificial Intelligence and Big Data Technologies in the Construction of Surgical Risk Prediction Model for Patients with Coronary Artery Bypass Grafting

The objective of this work was to predict the risk of mortality rate in patients with coronary artery bypass grafting (CABG) based on the risk prediction model of CABG using artificial intelligence (AI) and big data technologies. The clinical data of 2,364 patients undergoing CABG in our hospital from January 2019 to August 2021 were collected in this work. Based on AI and big data technology, business requirement analysis, system requirement analysis, complication prediction module, big data mining technology, and model building are carried out, respectively; the successful CABG risk prediction system includes case feature analysis service, risk warning service, and case retrieval service. The commonly used precision, recall, and F1-score were adopted to evaluate the quality of the gradient-boosted tree (GBT) model. The analysis proved that the GBT model was the best in terms of precision, F1-score, and area under the receiver operating characteristic curve (ROC). According to the CABG risk prediction model, 1,382 patients had a score of <0, 463 patients had a score of 0 ≤ score ≤ 2, 252 patients had a score of 2 < score ≤ 5, and 267 patients had a score of >5, which were stratified into four groups: A, B, C, and D. The actual number of in-hospital deaths was 25, and the in-hospital mortality rate was 1.05%. The mortality rate predicted by the CABG risk prediction model was 2.67 ± 1.82% (95% confidential interval (CI) (2.87-2.98)), which was higher than the actual value. The CABG risk prediction model showed the credible results only in group B with AUC = 0.763 > 0.7. In group B, 3 patients actually died, the actual mortality rate was 0.33%, and the predicted mortality rate was 0.96 ± 0.78 (95% CI (0.82-0.87)), which overestimated the mortality rate of patients in group B. It successfully constructed a CABG risk prediction model based on the AI and big data technologies, which would overestimate the mortality of patients with intermediate risk, and it is suitable for different types of heart diseases through continuous research and development and innovation, and provides clinical guidance value.

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.

Association of gravity drainage and complications following Whipple: an analysis of the ACS-NSQIP targeted database

Background

The optimal type of operative drainage following pancreaticoduodenectomy (PD) remains unclear. Our objective is to investigate risk associated with closed drainage techniques (passive [gravity] vs. suction) after PD.

Methods

We assessed operative drainage techniques utilized in patients undergoing PD in the ACS-NSQIP pancreas-targeted database from 2016 to 2018. Using multivariable logistic regression to adjust for characteristics of the patient, procedure, and pancreas, we examined the association between use of gravity drainage and postoperative outcomes.

Results

We identified 9665 patients with drains following PD from 2016 to 2018, of which 12.7% received gravity drainage. 61.0% had a diagnosis of adenocarcinoma or pancreatitis, 26.5% had a duct <3 mm, and 43.5% had a soft or intermediate gland. After multivariable adjustment, gravity drainage was associated with decreased rates of postoperative pancreatic fistula (odds ratio [OR] 0.779, 95% confidence interval [CI] 0.653–0.930, p=0.006), delayed gastric emptying (OR 0.830, 95% CI 0.693–0.988, p=0.036), superficial SSI (OR 0.741, 95% CI 0.572–0.959, p=0.023), organ space SSI (OR 0.791, 95% CI 0.658–0.951, p=0.012), and readmission (OR 0.807, 95% CI 0.679–0.958, p=0.014) following PD.

Conclusions

Gravity drainage is independently associated with decreased rates of CR-POPF, DGE, SSI, and readmission following PD. Additional prospective research is necessary to better understand the preferred drainage technique following PD.