{"id":{"repo_id":"calgary","oai_identifier":"oai:ucalgary.scholaris.ca:1880/107133"},"canonical_url":"https://search.dev.ndltd.org/etd/calgary/oai:ucalgary.scholaris.ca:1880/107133","repository":{"repo_id":"calgary","name":"University of Calgary","base_url":"https://ucalgary.scholaris.ca/server/oai/request"},"display":{"title":"Development and Evaluation of Risk Models to Predict Readmission or Death Following Discharge from an Adult General Systems Intensive Care Unit","abstract":"Transitions of care from intensive care unit (ICU) to ward are high-risk periods of healthcare delivery associated with ICU readmission and post-ICU mortality. Evidence-based processes for transitions are crucial for improving outcomes. Validated prediction models that include consistently associated risk factors for ICU readmission or post-ICU mortality may help to improve these practices. This mixed-methods thesis was comprised of three distinct phases: 1) systematic review and meta-analysis; 2) development of prediction models for ICU Readmission and Post-ICU Mortality using two approaches (literature-derived coefficients, data-derived coefficients [Derivation Cohort]), 3) validation of the models in an external Validation Cohort. The models for ICU Readmission showed limited discriminative ability whereas the Post-ICU Mortality models were stronger. Developing prediction models using pooled measures of association is a feasible approach, producing similar results to more the traditional data-derived method. Additional investigation to further validate the findings is required.","abstract_html":"Transitions of care from intensive care unit (ICU) to ward are high-risk periods of healthcare delivery associated with ICU readmission and post-ICU mortality. Evidence-based processes for transitions are crucial for improving outcomes. Validated prediction models that include consistently associated risk factors for ICU readmission or post-ICU mortality may help to improve these practices. This mixed-methods thesis was comprised of three distinct phases: 1) systematic review and meta-analysis; 2) development of prediction models for ICU Readmission and Post-ICU Mortality using two approaches (literature-derived coefficients, data-derived coefficients [Derivation Cohort]), 3) validation of the models in an external Validation Cohort. The models for ICU Readmission showed limited discriminative ability whereas the Post-ICU Mortality models were stronger. Developing prediction models using pooled measures of association is a feasible approach, producing similar results to more the traditional data-derived method. Additional investigation to further validate the findings is required.","abstract_has_math":false,"creators":["Boyd, Jamie"],"institution":"Cumming School of Medicine","degree_name":"Master of Science (MSc)","degree_level":null,"degree_discipline":"Community Health Sciences","degree_department":null,"school":null,"contributors":[],"advisors":["Stelfox, Henry Thomas"],"committee_chairs":[],"committee_members":["James, Matthew T.","Zuege, Danny J."],"year":2018,"date_issued":"2018-07-06","date_published":"2018-07-06","updated_at":"2026-07-24T01:30:44Z","subjects":["critical care","intensive care unit","transitions of patient care","prediction models","readmission","in-hospital mortality","meta-analysis","logistic regression"],"languages":["eng"],"rights":["University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["http://dx.doi.org/10.11575/PRISM/32355"],"render_values":[{"text":"http://dx.doi.org/10.11575/PRISM/32355","href":"http://dx.doi.org/10.11575/PRISM/32355","code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/1880/107133","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Stelfox, Henry Thomas"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["James, Matthew T.","Zuege, Danny J."]},{"key":"dc:creator","label":"Author","values":["Boyd, Jamie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-11"]},{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-07-11T21:19:13Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-07-11T21:19:13Z"]},{"key":"dc:date.issued","label":"Date","values":["2018-07-06"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Calgary"]},{"key":"dc:type","label":"Dc Type","values":["master thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Community Health Sciences"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Calgary"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["critical care","intensive care unit","transitions of patient care","prediction models","readmission","in-hospital mortality","meta-analysis","logistic regression"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["http://dx.doi.org/10.11575/PRISM/32355"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1880/107133"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Transitions of care from intensive care unit (ICU) to ward are high-risk periods of healthcare delivery associated with ICU readmission and post-ICU mortality. Evidence-based processes for transitions are crucial for improving outcomes. Validated prediction models that include consistently associated risk factors for ICU readmission or post-ICU mortality may help to improve these practices. This mixed-methods thesis was comprised of three distinct phases: 1) systematic review and meta-analysis; 2) development of prediction models for ICU Readmission and Post-ICU Mortality using two approaches (literature-derived coefficients, data-derived coefficients [Derivation Cohort]), 3) validation of the models in an external Validation Cohort. The models for ICU Readmission showed limited discriminative ability whereas the Post-ICU Mortality models were stronger. Developing prediction models using pooled measures of association is a feasible approach, producing similar results to more the traditional data-derived method. Additional investigation to further validate the findings is required."]},{"key":"dc:title","label":"Title","values":["Development and Evaluation of Risk Models to Predict Readmission or Death Following Discharge from an Adult General Systems Intensive Care Unit"]}]}],"canonical_facts":{"dc:contributor.advisor":["Stelfox, Henry Thomas"],"dc:contributor.committeemember":["James, Matthew T.","Zuege, Danny J."],"dc:creator":["Boyd, Jamie"],"dc:date":["2018-11"],"dc:date.accessioned":["2018-07-11T21:19:13Z"],"dc:date.available":["2018-07-11T21:19:13Z"],"dc:date.issued":["2018-07-06"],"dc:description.abstract":["Transitions of care from intensive care unit (ICU) to ward are high-risk periods of healthcare delivery associated with ICU readmission and post-ICU mortality. Evidence-based processes for transitions are crucial for improving outcomes. Validated prediction models that include consistently associated risk factors for ICU readmission or post-ICU mortality may help to improve these practices. This mixed-methods thesis was comprised of three distinct phases: 1) systematic review and meta-analysis; 2) development of prediction models for ICU Readmission and Post-ICU Mortality using two approaches (literature-derived coefficients, data-derived coefficients [Derivation Cohort]), 3) validation of the models in an external Validation Cohort. The models for ICU Readmission showed limited discriminative ability whereas the Post-ICU Mortality models were stronger. Developing prediction models using pooled measures of association is a feasible approach, producing similar results to more the traditional data-derived method. Additional investigation to further validate the findings is required."],"dc:identifier.doi":["http://dx.doi.org/10.11575/PRISM/32355"],"dc:identifier.uri":["http://hdl.handle.net/1880/107133"],"dc:language.iso":["eng"],"dc:publisher.institution":["University of Calgary"],"dc:rights":["University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission."],"dc:subject":["critical care","intensive care unit","transitions of patient care","prediction models","readmission","in-hospital mortality","meta-analysis","logistic regression"],"dc:title":["Development and Evaluation of Risk Models to Predict Readmission or Death Following Discharge from an Adult General Systems Intensive Care Unit"],"dc:type":["master thesis"],"thesis:degree_discipline":["Community Health Sciences"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["University of Calgary"]},"updated_at":"2026-07-24T01:30:44Z"}