Abstract

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.

Highlights

  • Introduction. e 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 repair

  • Proc Traj software was used in SAS program to conduct group-based trajectory modelling (GBTM), which classified patient population into groups based on their annual readmission rates during a 5-year period following primary AAA repair

  • Chronic high-impact users were significantly associated with renal failure, gastrointestinal complications, heart failure, peripheral vascular disease, number of hospital acquired complications, female sex, and undergoing other related procedures

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Summary

Research Article

Received 2 April 2018; Revised 27 July 2018; Accepted 27 August 2018; Published 21 October 2018. E 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. Based on the trends of readmission rates, patients were classified into low- and high-impact users. 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%). E aim of the study was to apply trajectory modelling on population-based data to visualise trends of readmission rate among high-impact users and other groups and to identify predictors associated with AAA surgical patients with high readmission rates

Methods
Mean readmission rate
Risk factors
Annual readmission rate
Discussion
Findings
Conflicts of Interest
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