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Trajectory Modelling to Assess Trends in Long-Term Readmission Rate among Abdominal Aortic Aneurysm Patients
INTRODUCTION: The aim of the study was to use trajectory analysis to categorise high-impact users based on their long-term readmission rate and identify their predictors following AAA (abdominal aortic aneurysm) repair. Methods. In this retrospective cohort study, group-based trajectory modelling (G...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6215543/ https://www.ncbi.nlm.nih.gov/pubmed/30420971 http://dx.doi.org/10.1155/2018/4321986 |
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author | Rao, Ahsan Bottle, Alex Bicknell, Collin Darzi, Ara Aylin, Paul |
author_facet | Rao, Ahsan Bottle, Alex Bicknell, Collin Darzi, Ara Aylin, Paul |
author_sort | Rao, Ahsan |
collection | PubMed |
description | INTRODUCTION: The aim of the study was to use trajectory analysis to categorise high-impact users based on their long-term readmission rate and identify their predictors following AAA (abdominal aortic aneurysm) repair. Methods. In this retrospective cohort study, group-based trajectory modelling (GBTM) was performed on the patient cohort (2006-2009) identified through national administrative data from all NHS English hospitals. Proc Traj software was used in SAS program to conduct GBTM, which classified patient population into groups based on their annual readmission rates during a 5-year period following primary AAA repair. Based on the trends of readmission rates, patients were classified into low- and high-impact users. The high-impact group had a higher annual readmission rate throughout 5-year follow-up. Short-term high-impact users had initial high readmission rate followed by rapid decline, whereas chronic high-impact users continued to have high readmission rate. RESULTS: Based on the trends in readmission rates, GBTM classified elective AAA repair (n=16,973) patients into 2 groups: low impact (82.0%) and high impact (18.0%). High-impact users were significantly associated with female sex (P=0.001) undergoing other vascular procedures (P=0.003), poor socioeconomic status index (P < 0.001), older age (P < 0.001), and higher comorbidity score (P < 0.001). The AUC for c-statistics was 0.84. Patients with ruptured AAA repair (n=4144) had 3 groups: low impact (82.7%), short-term high impact (7.2%), and chronic high impact (10.1%). Chronic high impact users were significantly associated with renal failure (P < 0.001), heart failure (P = 0.01), peripheral vascular disease (P < 0.001), female sex (P = 0.02), open repair (P < 0.001), and undergoing other related procedures (P=0.05). The AUC for c-statistics was 0.71. CONCLUSION: Patients with persistent high readmission rates exist among AAA population; however, their readmissions and mortality are not related to AAA repair. They may benefit from optimization of their medical management of comorbidities perioperatively and during their follow-up. |
format | Online Article Text |
id | pubmed-6215543 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-62155432018-11-12 Trajectory Modelling to Assess Trends in Long-Term Readmission Rate among Abdominal Aortic Aneurysm Patients Rao, Ahsan Bottle, Alex Bicknell, Collin Darzi, Ara Aylin, Paul Surg Res Pract Research Article INTRODUCTION: The aim of the study was to use trajectory analysis to categorise high-impact users based on their long-term readmission rate and identify their predictors following AAA (abdominal aortic aneurysm) repair. Methods. In this retrospective cohort study, group-based trajectory modelling (GBTM) was performed on the patient cohort (2006-2009) identified through national administrative data from all NHS English hospitals. Proc Traj software was used in SAS program to conduct GBTM, which classified patient population into groups based on their annual readmission rates during a 5-year period following primary AAA repair. Based on the trends of readmission rates, patients were classified into low- and high-impact users. The high-impact group had a higher annual readmission rate throughout 5-year follow-up. Short-term high-impact users had initial high readmission rate followed by rapid decline, whereas chronic high-impact users continued to have high readmission rate. RESULTS: Based on the trends in readmission rates, GBTM classified elective AAA repair (n=16,973) patients into 2 groups: low impact (82.0%) and high impact (18.0%). High-impact users were significantly associated with female sex (P=0.001) undergoing other vascular procedures (P=0.003), poor socioeconomic status index (P < 0.001), older age (P < 0.001), and higher comorbidity score (P < 0.001). The AUC for c-statistics was 0.84. Patients with ruptured AAA repair (n=4144) had 3 groups: low impact (82.7%), short-term high impact (7.2%), and chronic high impact (10.1%). Chronic high impact users were significantly associated with renal failure (P < 0.001), heart failure (P = 0.01), peripheral vascular disease (P < 0.001), female sex (P = 0.02), open repair (P < 0.001), and undergoing other related procedures (P=0.05). The AUC for c-statistics was 0.71. CONCLUSION: Patients with persistent high readmission rates exist among AAA population; however, their readmissions and mortality are not related to AAA repair. They may benefit from optimization of their medical management of comorbidities perioperatively and during their follow-up. Hindawi 2018-10-21 /pmc/articles/PMC6215543/ /pubmed/30420971 http://dx.doi.org/10.1155/2018/4321986 Text en Copyright © 2018 Ahsan Rao et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Rao, Ahsan Bottle, Alex Bicknell, Collin Darzi, Ara Aylin, Paul Trajectory Modelling to Assess Trends in Long-Term Readmission Rate among Abdominal Aortic Aneurysm Patients |
title | Trajectory Modelling to Assess Trends in Long-Term Readmission Rate among Abdominal Aortic Aneurysm Patients |
title_full | Trajectory Modelling to Assess Trends in Long-Term Readmission Rate among Abdominal Aortic Aneurysm Patients |
title_fullStr | Trajectory Modelling to Assess Trends in Long-Term Readmission Rate among Abdominal Aortic Aneurysm Patients |
title_full_unstemmed | Trajectory Modelling to Assess Trends in Long-Term Readmission Rate among Abdominal Aortic Aneurysm Patients |
title_short | Trajectory Modelling to Assess Trends in Long-Term Readmission Rate among Abdominal Aortic Aneurysm Patients |
title_sort | trajectory modelling to assess trends in long-term readmission rate among abdominal aortic aneurysm patients |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6215543/ https://www.ncbi.nlm.nih.gov/pubmed/30420971 http://dx.doi.org/10.1155/2018/4321986 |
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