De Costa, Alan (2018) Predicting difficult cholecstectomy using a science based model. In: [Presented at the 47th Annual Academic Sessions of the Sri Lanka College of Surgeons]. From: 47th Annual Academic Sessions of the Sri Lanka College of Surgeons, 16-18 August 2018, Colombo, Sri Lanka.

Preview

PDF (Abstract Only) - Accepted Version Download (57kB) | Preview

Abstract

There has been considerable interest over the last decade in the development of models to predict conversion to open at laparoscopic cholecystectomy (CONV). This is of increasing relevance as trainees and younger surgeons have less exposure to Open Cholecystectomy.

The Cairns Prediction Model (CBM), and the model produced by the West Midlands Research Collaborative using the CholeS dataset in the UK are described. They are both validated models and have a role to play. Analysis of the CholeS dataset suggests that it provides evidence to relate the notion of "difficulty" to conversion risk. It also shows that as the cases become more difficult (as judged against the Nassar grade) conversion rates and complications increase in the chole S dataset, but not in the Reference dataset of an expert surgeon.

It is suggested that the models be used for ethical discussion with patients and the process of obtaining consent in the light of the notion of "material" risk. Further uses for the model are to arrange operation lists, to select day case procedures and those for registrar training. Importantly it allows selection of difficult cholecystectomy for referral to expert surgeons.

The term Selective Cholecystectomy is introduced to describe this approach.

Item ID:	55700
Item Type:	Conference Item (Abstract / Summary)
Related URLs:	Organisation
Date Deposited:	09 Oct 2018 23:18
FoR Codes:	11 MEDICAL AND HEALTH SCIENCES > 1103 Clinical Sciences > 110323 Surgery @ 100%
SEO Codes:	92 HEALTH > 9201 Clinical Health (Organs, Diseases and Abnormal Conditions) > 920105 Digestive System Disorders @ 60% 92 HEALTH > 9299 Other Health > 929999 Health not elsewhere classified @ 40%
Downloads:	Total: 56 Last 12 Months: 6
	More Statistics