Annals of surgery最新文献

Objective: To assess rates of surgical complications and postoperative readmission in diabetic patients with and without active perioperative prescriptions for glucagon-like peptide-1 receptor agonist (GLP-1 RA) medications.

Background: With the rapid growth of GLP-1 RA use in the United States, it is important to understand the potential effect of these drugs on surgical outcomes broadly.

Methods: In this retrospective, observational cohort analysis, patients with a diagnosis of type 1 or type 2 diabetes undergoing a surgical procedure at a multicenter quaternary-care health care system between February 2020 and July 2023 were included. Propensity score matching was performed between procedures in patients with and without an active GLP-1 RA prescription. The primary outcome was 30-day readmission, and secondary outcomes were documented dehiscence, infection, hematoma, and bleeding within 180 days after surgery.

Results: Among 74,425 surgical procedures in 21,772 patients included in the analysis, 27.2% were performed in the setting of an active GLP-1 RA prescription. In 13,129 patients [48.0% men, 52.0% women; median (interquartile range) age, 67 (57, 75)], 35,020 procedures were propensity score matched. After matching, the active GLP-1 RA prescription group had a significantly reduced risk of 30-day readmission [relative risk (RR): 0.883; 95% CI: 0.789-0.987; P = 0.028; number needed to treat (NNT): 219; 95% CI: 191-257], postoperative wound dehiscence (RR: 0.711; 95% CI: 0.577-0.877; P = 0.001; NNT: 266; 95% CI: 202-391), and postoperative hematoma (RR: 0.440; 95% CI: 0.216-0.894; P = 0.023; NNT: 1786; 95% CI: 652-2416). No significant differences were seen in rates of infection and bleeding.

Conclusions: An active perioperative GLP-1 RA prescription in patients with diabetes was associated with significant reductions in risk-adjusted readmission, wound dehiscence, and hematoma, and no difference in infection and bleeding rates. Further study is warranted to elucidate any causal association.

Objective: To develop an artificial intelligence (AI) system for the early prediction of residual cancer burden (RCB) scores during neoadjuvant chemotherapy (NAC) in breast cancer.

Background: RCB III indicates drug resistance in breast cancer, and early detection methods are lacking.

Methods: This study enrolled 1048 patients with breast cancer from 4 institutions, who were all receiving NAC. Magnetic resonance images were collected at the pre-NAC and mid-NAC stages, and radiomics and deep learning features were extracted. A multitask AI system was developed to classify patients into 3 groups (RCB 0 to I, II, and III ) in the primary cohort (PC, n=335). Feature selection was conducted using the Mann-Whitney U test, Spearman analysis, least absolute shrinkage and selection operator regression, and the Boruta algorithm. Single-modality models were developed, followed by model integration. The AI system was validated in 3 external validation cohorts (EVCs, n=713).

Results: Among the patients, 442 (42.18%) were RCB 0 to I, 462 (44.08%) were RCB II, and 144 (13.74%) were RCB III. Model I achieved an area under the curve of 0.975 in the PC and 0.923 in the EVCs for differentiating RCB III from RCB 0 to II. Model II distinguished RCB 0 to I from RCB II-III, with an area under the curve of 0.976 in the PC and 0.910 in the EVCs. Subgroup analysis confirmed that the AI system was consistent across different clinical T stages and molecular subtypes.

Conclusions: The multitask AI system offers a noninvasive tool for the early prediction of RCB scores in breast cancer, supporting clinical decision-making during NAC.

Objective: To develop and validate a signature to precisely predict prognosis in pancreatic ductal adenocarcinoma (PDAC) undergoing resection and adjuvant chemotherapy.

Background: PDAC is largely heterogeneous and responds discrepantly to treatment.

Methods: A total of 551 consecutive patients with PDAC from 3 different cohorts of tertiary centers were initially enrolled. Genetic events of the 4 most commonly mutated genes in PDAC and expressions of 12 PI3K/AKT/mammalian target of rapamycin (mTOR) pathway markers were examined. A 9-feature signature for the prediction of chemotherapy benefits was constructed in the training cohort using the least absolute shrinkage and selection operator Cox regression model and validated in 2 independent cohorts.

Results: Utilizing the least absolute shrinkage and selection operator model, a predictive and prognostic signature, named ChemoResist, was established based on KRAS single nucleotide variant (SNV), phosphatase and tensin homologue (PTEN), and mTOR expressions, and 6 clinicopathologic features. Significant differences in survival were observed between high and low-ChemoResist patients receiving chemotherapy in both the training [median overall survival (OS), 17 vs 42 months, P < 0.001; median disease-free survival (DFS), 10 vs 23 months, P < 0.001] and validation cohorts (median OS, 18 vs 35 months, P = 0.034; median DFS, 11 vs 20 months, P = 0.028). The ChemoResist classifier also significantly differentiated patient survival in whole patients regardless of chemotherapy. Multivariable-adjusted analysis substantiated the ChemoResist signature as an independent predictive and prognostic factor. For predicting 2-year OS, the ChemoResist classifier had significantly higher areas under the curve than TNM stage (0.788 vs 0.636, P < 0.001), other clinicopathologic characteristics (0.505-0.668), and single molecular markers (0.507-0.591) in the training cohort. Furthermore, patients with low ChemoResist scores exhibited a more favorable response to adjuvant chemotherapy compared with those with high ChemoResist scores (hazard ratio for OS: training, 0.22 vs 0.57; validation, 0.26 vs 0.50; hazard ratio for DFS: training, 0.35 vs 0.54; validation, 0.18 vs 0.59). The ChemoResist signature was further validated in the total cohort undergoing R0 resection.

Conclusions: The ChemoResist signature could precisely predict survival in PDAC undergoing resection and chemotherapy, and its predictive value surpassed the TNM stage and other clinicopathologic factors. Moreover, the ChemoResist classifier could assist with identifying patients who would more likely benefit from adjuvant chemotherapy.

Objective: The DELORES trial investigated whether laparoscopic resection rectopexy (LRR) is superior to Delorme's procedure (DP) in full-thickness rectal prolapse.

Summary of background data: Multiple perineal and transabdominal procedures are current practice for rectal prolapse surgery. Evidence from adequately designed randomized studies addressing the question of which of these procedures are superior in terms of recurrence and bowel function is lacking.

Methods: DELORES was a randomized, observer-blinded, expertise-based multicenter trial. Patients with full-thickness rectal prolapse were eligible. Primary outcome was time to recurrence of full-thickness rectal prolapse within 24 months after primary surgery. Main secondary endpoints were morbidity, hospital stay, quality of life, constipation and fecal incontinence. (DRKS00000482).

Results: A total of 358 patients were screened between September 2010 and January 2016. Based on screening, 70 patients were randomized and 65 were included in the analysis (33 LRR and 32 DP procedures). Median follow-up was 23.9 months. Analysis of the primary outcome showed that LRR was superior to DP ( P =0.0012). During the 24-month follow-up, 8.2% of patients in the LRR group had a full-thickness prolapse recurrence versus 42.8% in the DP group. Median time to recurrence was 11.9 months for LRR and 8.2 months for DP. Median duration of surgery was 212 min (LRR) versus 77 min (DP). Overall postoperative morbidity was low. The reoperation rate was higher for DP (0% LRR vs. 33.3% DP). Quality of life (FIQL) and incontinence scores (Wexner) were more favorable for LRR at 24-month follow-up.

Conclusion: Laparoscopic resection rectopexy is superior to Delorme's procedure in terms of recurrence and has favorable functional results.

Objective: To determine the prevalence of ductal cancerization at the pancreatic neck margin and to investigate its prognostic significance.

Summary background data: Ductal cancerization (cancerization of ducts; COD) describes the growth of invasive cancer in pre-existing ducts, and is frequently seen in pancreatic cancer resection specimens. Although COD is a well-defined histological diagnosis, it is not routinely reported on final pathology. Therefore, the prevalence and oncologic significance of COD at the pancreatic neck margin is unknown.

Methods: We queried our institutional database for pancreatic cancer resections performed between 2014-2018. Diagnostic slides were reviewed by expert pathologists for the presence of COD at the final neck margin, and statistical analysis was performed to correlate these findings with outcomes.

Results: Of 767 pancreatic resections meeting our eligibility criteria, final pancreatic neck margin was histologically evaluated in 309 cases (40.3%). COD was present at the final neck margin of 16 cases (5.2%). Overall-survival (OS) and recurrence-free survival (RFS) were both significantly shorter when COD was present at the final neck margin (HR 1.86; 95%CI 1.09-3.17; P=0.024 and HR 2.01; 95%CI 1.16-3.49; P=0.013, respectively) when adjusted for cancer stage, margin status, and adjuvant therapy in multivariate analysis. In contrast, presence of high-grade pancreatic intraepithelial neoplasia (HG-PanIN) at the final neck margin did not impact oncologic outcome.

Conclusions: Although COD is uncommon at the final pancreatic neck margin, it is associated with poor survival and increased recurrence. Therefore, and to clearly distinguish it from its mimicker HG-PanIN, routine reporting in histopathological assessment may be advised.