Literature survey on epidemiology and pathology of gangliocytic paraganglioma

<p>Abstract</p> <p>Background</p> <p>Although gangliocytic paraganglioma (GP) has generally been regarded as a neuroendocrine tumor, its origin remains unclear. We therefore aimed to investigate the details of this disease by carefully analyzing and extracting common features of the disease as presented in selected publications.</p> <p>Methods</p> <p>We searched for English and Japanese cases of GP using the PubMed and IgakuChuoZasshi databases on August 2010. We then extracted and sampled raw data from the selected publications and performed appropriate statistical analyses. Additionally, we evaluated the expression of hormone receptors based on our previously reported case.</p> <p>Results</p> <p>192 patients with GP were retrieved from the databases. Patient ages ranged from 15 y to 84 y (mean: 52.3 y). The gender ratio was 114:76 (male to female, 2 not reported). Maximum diameter of the tumors ranged from 5.5 mm to 100 mm (mean: 25.0 mm). The duodenum (90.1%, 173/192) was found to be the most common site of the disease. In 173 patients with duodenal GP, gastrointestinal bleeding (45.1%, 78/173) was found to be the most common symptom of the disease, followed by abdominal pain (42.8%, 74/173), and anemia (14.5%, 25/173). Rate of lymph node metastasis was 6.9% (12/173). Our statistical analysis indicated that significant differences were found for gender between GP within the submucosal layer and exceeding the submucosal layer. Furthermore, our immunohistochemical evaluation showed that both epithelioid and pancreatic islet cells showed positive reactivity for progesterone receptors.</p> <p>Conclusions</p> <p>Our literature survey revealed that there were many more cases of GP exceeding the submucosal layer than were expected. Meanwhile, our statistical analyses and immunohistochemical evaluation supported the following two hypotheses. First, vertical growth of GP might be affected by progesterone exposure. Second, the origin of GP might be pancreatic islet cells. However, it is strongly suspected that our data have been affected by publication bias and to confirm these hypotheses, further investigation is required.</p

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability