{"id":{"repo_id":"auckland-ms","oai_identifier":"oai:researchspace.auckland.ac.nz:2292/72945"},"canonical_url":"https://search.dev.ndltd.org/etd/auckland-ms/oai:researchspace.auckland.ac.nz:2292/72945","repository":{"repo_id":"auckland-ms","name":"University of Auckland","base_url":"https://researchspace.auckland.ac.nz/server/oai/request"},"display":{"title":"Development of a Risk Prediction Tool for Emergency Laparotomy","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Barazanchi, Ahmed Waleed Habib"],"institution":"ResearchSpace@Auckland","degree_name":"PhD","degree_level":"Doctoral","degree_discipline":"Surgery","degree_department":null,"school":null,"contributors":[],"advisors":["Hill, Andrew G"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024","date_published":"2024","updated_at":"2026-07-24T01:06:29Z","subjects":["Laparotomy","Acute General Surgery","Risk Prediction"],"languages":[],"rights":["Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated."],"rights_urls":["https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2292/72945","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Hill, Andrew G"]},{"key":"dc:creator","label":"Author","values":["Barazanchi, Ahmed Waleed Habib"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-07-17T21:44:49Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-07-17T21:44:49Z"]},{"key":"dc:date.issued","label":"Date","values":["2024"]},{"key":"dc:publisher","label":"Institution","values":["ResearchSpace@Auckland"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Surgery"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["PhD"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["The University of Auckland"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Laparotomy","Acute General Surgery","Risk Prediction"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated."]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/2292/72945"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Development of a Risk Prediction Tool for Emergency Laparotomy"]}]}],"canonical_facts":{"dc:contributor.advisor":["Hill, Andrew G"],"dc:creator":["Barazanchi, Ahmed Waleed Habib"],"dc:date.accessioned":["2025-07-17T21:44:49Z"],"dc:date.available":["2025-07-17T21:44:49Z"],"dc:date.issued":["2024"],"dc:description.abstract":["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."],"dc:identifier.uri":["https://hdl.handle.net/2292/72945"],"dc:publisher":["ResearchSpace@Auckland"],"dc:rights":["Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated."],"dc:rights.uri":["https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm"],"dc:subject":["Laparotomy","Acute General Surgery","Risk Prediction"],"dc:title":["Development of a Risk Prediction Tool for Emergency Laparotomy"],"dc:type":["Thesis"],"thesis:degree_discipline":["Surgery"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["PhD"],"thesis:institution_name":["The University of Auckland"]},"updated_at":"2026-07-24T01:06:29Z"}