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

MRI in active surveillance: a critical review

INTRODUCTION: Recent technological advancements and the introduction of modern anatomical and functional sequences have led to a growing role for multiparametric magnetic resonance imaging (mpMRI) in the detection, risk assessment and monitoring of early prostate cancer. This includes men who have been diagnosed with lower-risk prostate cancer and are looking at the option of active surveillance (AS). The purpose of this paper is to review the recent evidence supporting the use of mpMRI at different time points in AS, as well as to discuss some of its potential pitfalls. METHODS: A combination of electronic and manual searching methods were used to identify recent, important papers investigating the role of mpMRI in AS. RESULTS: The high negative predictive value of mpMRI can be exploited for the selection of AS candidates. In addition, mpMRI can be efficiently used to detect higher risk disease in patients already on surveillance. CONCLUSION: Although there is an ongoing debate regarding the precise nature of its optimal implementation, mpMRI is a promising risk stratification tool and should be considered for men on AS

Development and validation of a novel risk score for the detection of insignificant prostate cancer in unscreened patient cohorts

Background: Active surveillance is recommended for insignificant prostate cancer (PCa). Tools exist to identify suitable candidates using clinical variables. We aimed to develop and validate a novel risk score (NRS) predicting which patients are harbouring insignificant PCa. / Methods: We used prospectively collected data from 8040 consecutive unscreened patients who underwent radical prostatectomy between 2006 and 2016. Of these, data from 2799 patients with Gleason 3 + 3 on biopsy were used to develop a multivariate model predicting the presence of insignificant PC at radical prostatectomy (ERSPC updated definition3: Gleason 3 + 3 only, index tumour volume < 1.3 cm3 and total tumour volume < 2.5 cm3). This was used to develop a novel risk score (NRS) which was validated in an equivalent independent cohort (n = 441). We compared the accuracy of existing predictive tools and the NRS in these cohorts. / Results: The NRS (incorporating PSA, prostate volume, age, clinical T Stage, percent and number of positive biopsy cores) outperformed pre-existing predictive tools in derivation and validation cohorts (AUC 0.755 and 0.76, respectively). Selection bias due to analysis of a surgical cohort is acknowledged. / Conclusions: The advantage of the NRS is that it can be tailored to patient characteristics and may prove to be valuable tool in clinical decision-making