ABSTRACT
OBJECTIVE: To quantify the rate of low-yield surgery, defined as no high-grade dysplastic precursor lesions or T1N0M0 pancreatic cancer at pathology, during pancreatic cancer surveillance. BACKGROUND: Global efforts have been made in pancreatic cancer surveillance to anticipate the diagnosis of pancreatic cancer at an early stage and improve survival in high-risk individuals (HRIs) with a hereditary predisposition. The negative impact of pancreatic cancer surveillance when surgery is performed for low-grade dysplasia or a non-neoplastic condition is not well quantified. MATERIALS AND METHODS: A systematic search and prevalence meta-analysis was performed for studies reporting surgery with final diagnoses other than those defined by the Cancer of the Pancreas Screening (CAPS) goals from January 2000 to July 2023. The secondary outcome was the pooled proportion of final diagnoses matching the CAPS goals (PROSPERO: #CRD42022300408). RESULTS: Twenty-three articles with 5027 patients (median 109 patients/study, interquartile range 251) were included. The pooled prevalence of low-yield surgery was 2.1% (95% CI: 0.9-3.7, I2 : 83%). In the subgroup analysis, this prevalence was nonsignificantly higher in studies that only included familial pancreatic cancer subjects without known pathogenic variants, compared with those enrolling pathogenic variant carriers. No effect modifiers were found. Overall, the pooled prevalence of subjects under surveillance who had a pancreatic resection that contained target lesions was 0.8% (95% CI, 0.3-1.5, I2 : 24%]. The temporal analysis showed that the rate of low-yield surgeries decreased in the last decades and stabilized at around 1% (test for subgroup differences P <0.01). CONCLUSIONS: The risk of "low-yield" surgery during pancreatic cancer surveillance is relatively low but should be thoroughly discussed with individuals under surveillance.
Subject(s)
Pancreatic Neoplasms , Humans , Prevalence , Risk Factors , Pancreatic Neoplasms/diagnosis , Pancreatic Neoplasms/epidemiology , Pancreatic Neoplasms/surgery , Pancreas/pathology , Genetic Predisposition to DiseaseABSTRACT
The COVID-19 outbreak involved a spread of prediction efforts, especially in the early pandemic phase. A better understanding of the epidemiological implications of the different models seems crucial for tailoring prevention policies. This study aims to explore the concordance and discrepancies in outbreak prediction produced by models implemented and used in the first wave of the epidemic. To evaluate the performance of the model, an analysis was carried out on Italian pandemic data from February 24, 2020. The epidemic models were fitted to data collected at 20, 30, 40, 50, 60, 70, 80, 90, and 98 days (the entire time series). At each time step, we made predictions until May 31, 2020. The Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE) were calculated. The GAM model is the most suitable parameterization for predicting the number of new cases; exponential or Poisson models help predict the cumulative number of cases. When the goal is to predict the epidemic peak, GAM, ARIMA, or Bayesian models are preferable. However, the prediction of the pandemic peak could be made carefully during the early stages of the epidemic because the forecast is affected by high uncertainty and may very likely produce the wrong results.
ABSTRACT
[This corrects the article DOI: 10.3389/fnut.2023.1065294.].
ABSTRACT
Background and aims: Body composition parameters and immunonutritional indexes provide useful information on the nutritional and inflammatory status of patients. We sought to investigate whether they predict the postoperative outcome in patients with pancreatic cancer (PC) who received neoadjuvant therapy (NAT) and then pancreaticoduodenectomy. Methods: Data from locally advanced PC patients who underwent NAT followed by pancreaticoduodenectomy between January 2012 and December 2019 in four high-volume institutions were collected retrospectively. Only patients with two available CT scans (before and after NAT) and immunonutritional indexes (before surgery) available were included. Body composition was assessed and immunonutritional indexes collected were: VAT, SAT, SMI, SMA, PLR, NLR, LMR, and PNI. The postoperative outcomes evaluated were overall morbidity (any complication occurring), major complications (Clavien-Dindo ≥ 3), and length of stay. Results: One hundred twenty-one patients met the inclusion criteria and constituted the study population. The median age at the diagnosis was 64 years (IQR16), and the median BMI was 24 kg/m2 (IQR 4.1). The median time between the two CT-scan examined was 188 days (IQR 48). Skeletal muscle index (SMI) decreased after NAT, with a median delta of -7.8 cm2/m2 (p < 0.05). Major complications occurred more frequently in patients with a lower pre-NAT SMI (p = 0.035) and in those who gained in subcutaneous adipose tissue (SAT) compartment during NAT (p = 0.043). Patients with a gain in SMI experienced fewer major postoperative complications (p = 0.002). The presence of Low muscle mass after NAT was associated with a longer hospital stay [Beta 5.1, 95%CI (1.5, 8.7), p = 0.006]. An increase in SMI from 35 to 40 cm2/m2 was a protective factor with respect to overall postoperative complications [OR 0.43, 95% (CI 0.21, 0.86), p < 0.001]. None of the immunonutritional indexes investigated predicted the postoperative outcome. Conclusion: Body composition changes during NAT are associated with surgical outcome in PC patients who receive pancreaticoduodenectomy after NAT. An increase in SMI during NAT should be favored to ameliorate the postoperative outcome. Immunonutritional indexes did not show to be capable of predicting the surgical outcome.
