Clinical trials

This observational study evaluates whether a machine-learning algorithm, a computer program that learns patterns from data, can accurately diagnose cholestasis in newborns. Cholestasis refers to reduced or blocked bile flow from the liver, which can lead to liver damage. A severe form of cholestasis is biliary atresia, a condition where the bile ducts are damaged or absent, requiring early treatment to prevent long-term harm. The study involves infants from birth, both healthy and those potentially affected by cholestasis, recruited from four UK hospitals. It addresses two primary aims: * Accuracy of Diagnosis: Can the machine-learning algorithm accurately identify cholestasis and biliary atresia using parent-provided stool images? This will be assessed by measuring sensitivity (the ability to correctly detect true cases) and specificity (the ability to correctly identify infants without the condition). * Feasibility of Screening: Is using parent-provided images a feasible and acceptable screening method for early detection? To evaluate these aims, researchers will compare two groups: * Infants with abnormal stool images who are subsequently diagnosed with cholestasis or biliary atresia. * Infants with normal stool images who do not develop biliary atresia. This comparison will help determine the algorithm's ability to distinguish between infants with and without these conditions. Parents will: * Take smartphone photos of their baby's dirty diapers at 14, 21, and 28 days of age. * Upload the images for analysis by the algorithm. * Provide feedback on their experience with this screening process. The study seeks to determine if parent-submitted stool images can serve as a practical early screening tool for cholestasis, potentially enabling faster diagnosis and improved outcomes for affected infants.

This study aims to look at babies having a primary or delayed primary oesophageal repair for OA with dTOF to evaluate if using Indocyanine green (ICG) and near infrared fluorescence (NIRF) can decrease the rates of anastomotic leaks and/or predict which patients they will happen in. The latter evaluation would help counsel parents and mean that further research can evaluate if other tactics can prevent the leak being a moderate or severe problem. These may include, but not be limited to, extra anastomotic sutures, insertion of a chest drain at the time of surgery (if this had not previously been considered) delaying oral feeding or using medications to dry up the saliva prophylactically (these medications have been shown to reduce the length of time it takes leaks to seal). Any technique that can reduce leak rates in oesophageal atresia is to be welcomed. Additionally ICG may artifactually affect both peripheral oxygen readings (cause a transient decrease) and cerebral near infrared spectroscopy (NIRS) values (cause a transient increase). This is due to the temporary, dose dependent, interference of the dye with the mechanism of action of the monitoring rather than a physiological effect on oxygen levels. To date there has been no study investigating the effects of ICG on oxygen saturation and cerebral NIRS in neonates undergoing OA and/or dTOF repair. The theory is an extension from adult practice following oesophagectomy for cancer where there was a reduction in anastomotic leaks when using ICG/NIRF perfusion assessment. Another study in bariatric surgery using an enteral ICG/NIRF assessment was highly sensitive for anastomotic leaks allowing management of them intra-operatively. Objectives are to 1. Identify if the appearances of ICG/NIRF can predict anastomotic leaks 2. Identify if the ICG/NIRF images would engender a change in operative management leading to a reduced leak rate 3. Give a detailed report on the effects of ICG on oxygen readings This would be a cohort pilot study of 20 patients with the aim of informing a subsequent multi-centre Randomised controlled trial