Development of a Risk Prediction Tool for Emergency Laparotomy

Aim: This thesis aims to develop a risk prediction tool for emergency laparotomy surgery based on easily obtainable preoperative variables that predict useful outcomes for clinicians and patients. Methods: A systematic review was used to establish individual risk factors for emergency laparotomy. Clinicians were asked about their current use of risk prediction tools and how an ideal tool should be. A mixed methods qualitative study using interviews was conducted to explore the patient's experience of emergency laparotomy, to clarify the important aspects of recovery, and to enquire on what ideal risk prediction would be. A retrospective cohort was used to explore the utility of sarcopenia in risk prediction and the accuracy of current risk prediction models attempting to improve them using frailty and nutrition. The same cohort was reviewed for long-term mortality with an in-depth analysis of causes of death. A model for ethics application on acutely unwell delirious patients was developed to allow for the inclusion of a representative cohort. A five-hospital multicentre study using the obtained risk factor was used to develop and internally validate a novel risk prediction model using sequential logistic regressions. Findings: A list of predictive factors based on five broad categories was developed: physiology, comorbidities, frailty, nutrition, and social factors. Survival was the most important factor to predict for patients with quality of life followed closely by mortality for clinicians. Sarcopenia in the retrospective cohort was not a useful predictor of mortality. Adding frailty and nutrition improved the prediction of the most accurate model developed by the National Emergency Laparotomy Audit in the UK. The same risk prediction model showed consistent accuracy up to five years postoperatively but that excess mortality could not be explained based on comorbidities and cancer status. The model achieved an area under receiver operating characteristic of 0.90, indicating excellent discrimination, and McFadden’s R-square of 0.32, indicating excellent calibration. Conclusion: This thesis presents a novel risk prediction tool with excellent discrimination and calibration. By focusing on a parsimonious selection of variables, the tool offers practical preoperative risk assessment in emergency laparotomy patients.

Name thesis:degree_name

PhD

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Surgery

Grantor dc:publisher

ResearchSpace@Auckland

Year dc:date.issued

2024

Author dc:creator

• Barazanchi, Ahmed Waleed Habib

Advisor dc:contributor.advisor

• Hill, Andrew G

• Laparotomy
• Acute General Surgery
• Risk Prediction

Statement dc:rights

• Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Licence dc:rights.uri

• https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm

Handle dc:identifier.uri

https://hdl.handle.net/2292/72945

OAI identifier oai:identifier

oai:researchspace.auckland.ac.nz:2292/72945