Preoperative tumor marking with indocyanine green prior of robotic colorectal resections

This prospective case-series study aimed to assess the usefulness of preoperative colonoscopic marking of colorectal tumors using Indocyanine Green (ICG) fluorescence in patients that underwent robotic surgical colorectal resections. Consecutive patients that were eligible for colorectal resection with intent to cure in a single hospital (Athens Medical Center), from February 2022 to June 2022, were included. ICG solution was injected into the submucosal layer at 2 opposite sites (180 degrees apart) distal to the tumor, without submucosal elevation. Identification of the tumor marking was then performed after switching to near-infrared (NIR) fluorescence mode. During the robotic procedure, qualitative evaluation of fluorescence was performed by the surgical team (primary surgeon, first assistant, second assistant, research fellow). All 10 patients underwent robotic surgical approach and operations included right-sided colectomy (n = 1), left-sided colectomy (n = 6) and low anterior resection (n = 3). Visualisation of this dye with near-infrared light was very clear with bright intensity in all patients when the marking was performed one day prior of surgery. Preoperative tumor marking with ICG was identified intraoperatively in all cases and the techinque was easily reproducible

Systematic review of preoperative and intraoperative colorectal Anastomotic Leak Prediction Scores (ALPS)

Objective To systematically review preoperative and intraoperative Anastomotic Leak Prediction Scores (ALPS) and validation studies to evaluate performance and utility in surgical decision-making. Anastomotic leak (AL) is the most feared complication of colorectal surgery. Individualised leak risk could guide anastomosis and/or diverting stoma.Methods Systematic search of Ovid MEDLINE and Embase databases, 30 October 2020, identified existing ALPS and validation studies. All records including >1 risk factor, used to develop new, or to validate existing models for preoperative or intraoperative use to predict colorectal AL, were selected. Data extraction followed CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies guidelines. Models were assessed for applicability for surgical decision-making and risk of bias using Prediction model Risk Of Bias ASsessment Tool.Results 34 studies were identified containing 31 individual ALPS (12 colonic/colorectal, 19 rectal) and 6 papers with validation studies only. Development dataset patient populations were heterogeneous in terms of numbers, indication for surgery, urgency and stoma inclusion. Heterogeneity precluded meta-analysis. Definitions and timeframe for AL were available in only 22 and 11 ALPS, respectively. 26/31 studies used some form of multivariable logistic regression in their modelling. Models included 3–33 individual predictors. 27/31 studies reported model discrimination performance but just 18/31 reported calibration. 15/31 ALPS were reported with external validation, 9/31 with internal validation alone and 4 published without any validation. 27/31 ALPS and every validation study were scored high risk of bias in model analysis.Conclusions Poor reporting practices and methodological shortcomings limit wider adoption of published ALPS. Several models appear to perform well in discriminating patients at highest AL risk but all raise concerns over risk of bias, and nearly all over wider applicability. Large-scale, precisely reported external validation studies are required.PROSPERO registration number CRD42020164804