Background

Historically, surgeons have emphasised the in-hospital or 30-day mortality rate after elective AAA repair, and there is a considerable amount of literature published on the topic.^63–66 As the incidence of AAA repair increases with age, with most patients in their eighth decade, often with significant comorbidity,²⁸ understanding long-term survival is increasingly important. As most patients with AAA are asymptomatic, the principal indication of repair is to prevent rupture and increase survival.

Objective

To identify preoperative risk factors that predict long-term survival following elective AAA repair.

Modelling survival following elective abdominal aortic aneurysm repair: methods

Data from the VGNW programme for all patients who underwent elective AAA repair from January 2000 to April 2013 were included in this analysis. Over this study period there were 87 contributing surgeons across 24 hospitals. Emergency repairs for AAA rupture or repairs of thoracoabdominal aneurysms were excluded. The demographic batch service was used to determine mortality status for all patients up to and including 31 May 2013.

The data were cleaned by first resolving transcriptional discrepancies and clinical conflicts. Aberrant and extreme values were removed or transformed if the cause was inconsistent measurement units. Database variables with significant missing data (15%) were excluded from the analysis. For the remaining variables, any missing patient factor was imputed to the median value for continuous variables and assumed to be absent for categorical variables. The following preoperative measurements were defined as abnormal: serum creatinine concentration > 120 µmol/l; haemoglobin level < 11 g/dl for women and < 13 g/dl for men; WCC < 3.0 × 10⁹/l or > 11.0 × 10⁹/l; serum urea concentration > 7.5 mmol/l; serum sodium level < 135 mmol/l or > 145 mmol/l; potassium level < 3.5 mmol/l or > 5.5 mmol/l. Ischaemic heart disease included a history of previous MI, angina or both. All analyses and cleaning were performed using R version 3.0.1 (R Foundation for Statistical Computing, Vienna, Austria).

Survival analyses were performed using the Kaplan–Meier method, and differences in survival were compared using the log-rank test.^67,68 Age and AAA diameter were retained as continuous variables as they did not violate the linearity assumption (checked using standard diagnostics).⁵⁴ Individual Cox proportional hazards models were used for univariate analysis of preoperative variables. Variables of clinical significance and those with a p-value < 0.2 by univariate analysis were included in a multivariate Cox proportional hazards model to identify significant preoperative prognostic indicators of long-term survival. Backward stepwise selection using AIC was used to optimise this model. Scaled Schoenfield residuals were analysed to ensure the proportional hazards assumptions were not violated for variables associated with long-term survival.^69,70

Modelling survival following elective abdominal aortic aneurysm repair: results

In the 13-year period, 4070 patients underwent AAA repair in the North West region. Of these, 2317 (57%) were by open surgical repair and 1753 (43%) were by EVAR. The mean age was 73.5 years and the majority of patients, 3398 (84%), were men. Patient characteristics are shown in Table 12. Overall cohort survival was 70.2% at 5 years and 41% at 10 years, with a median survival of 8.1 years (Figure 9). The in-hospital mortality was 5.2% overall, which improved over the study period to a mean of 3.0% during the last 5 years (2009–13). For those patients that survived the perioperative period following open surgical repair or EVAR, the apparent early benefit of EVAR (1-year survival of 88.3% and 91.2% respectively; Figure 10) was lost by year 2 of follow-up and at 5 years, 71.3% of patients following open repair were alive compared with 65.4% following EVAR. Table 12 shows that the main reason for this improved survival following open surgery may be the younger age and lower comorbidities in patients selected for open surgery by the surgeon.

TABLE 12

Patient demographics of the VGNW survival cohort

FIGURE 9

Overall survival following AAA repair of the cohort from day of operation using Kaplan–Meier.

FIGURE 10

Overall survival in patients undergoing open repair or EVAR from date of operation using Kaplan–Meier. No significant survival advantage was noted between the groups (p = 0.441).

On univariate analysis, increasing patient age, female sex and ischaemic heart disease were all significantly associated with reduced survival. Preoperative investigations associated with reduced survival included abnormal ECG, raised serum concentrations of creatinine and urea, an abnormal serum sodium and anaemia. Larger AAA diameter was also a risk factor for poor survival following repair. Preoperative statin therapy was associated with improved survival long term.

Multivariate Cox proportional hazards model of prognostic factors for overall survival following AAA repair are shown in Table 13. This model was stratified on repair type because of the violation of the proportional hazards assumption demonstrated in Figure 10. The patient characteristics significantly associated with reduced survival included increasing age [hazard ratio (HR) 1.05, 95% CI 1.04 to 1.06], female sex (HR 1.32, 95% CI 1.14 to 1.53) and ischaemic heart disease (HR 1.16, 95% CI 1.02 to 1.32). Preoperative statin therapy was associated with improved survival (HR 0.76, 95% CI 0.67 to 0.87). Antiplatelet therapy and diabetes, although included in the model, were not statistically significant predictors of survival (p = 0.11 and p = 0.09 respectively).

TABLE 13

Multivariable risk factors associated with long-term survival after elective AAA repair

As a sensitivity analysis, multivariate Cox proportional hazard models were prepared on open repair and EVAR patient cohorts separately. These models are shown in Tables 14 and 15. Both models include female sex, age, ischaemic heart disease, diabetes, serum creatinine > 120 µmol/l and anaemia. Statin use and abnormal sodium are included in the open repair model while inflammatory aneurysm, antiplatelet use and antihypertensive use are included in the EVAR model. All significant patient characteristics from the separate models are included in the combined model apart from inflammatory aneurysm type, which occurred in very low frequency (0.9% of patients) in the EVAR patient cohort.

TABLE 14

Multivariable risk factors for open repair patients associated with long-term survival after elective AAA repair

TABLE 15

Multivariable risk factors for EVAR patients associated with long-term survival after elective AAA repair

To address the possibility that specific preoperative factors may influence perioperative mortality more than long-term survival and given the extensive analyses already performed on perioperative mortality, a further Cox proportional hazards analysis from the date of discharge from hospital following AAA repair was performed.

In patients surviving to discharge, the median subsequent survival time improved to 8.7 years with 75.0% 5-year survival (Figure 11). Multivariate Cox proportional hazards model of prognostic factors for survival following discharge from hospital are shown in Table 16. This model did not require stratification on repair type, as the proportional hazards assumption was not violated (Figure 12). Interaction terms for type of repair (open versus EVAR) and year of operation were assessed against the variables in this model as EVAR became increasingly used in the later years of this study. In all cases p < 0.05 was considered statistically significant.

FIGURE 11

Overall survival of the cohort from date of discharge using Kaplan–Meier.

TABLE 16

Multivariable risk factors associated with survival after discharge from hospital following elective AAA repair

FIGURE 12

Overall survival in patients undergoing open repair or EVAR from date of discharge using Kaplan–Meier. A significant survival advantage was noted for the open repair group (p < 0.0001).

Increasing age (HR 1.05, 95% CI 1.04 to 1.06), female sex (HR 1.21, 95% CI 1.02 to 1.44), abnormal ECG (HR 1.18, 95% CI 1.02 to 1.36), abnormal sodium (HR 1.37, 95% CI 1.11 to 1.67), creatinine (HR 1.30, 95% CI 1.12 to 1.51) and anaemia (HR 1.35, 95% CI 1.17 to 1.56) were predictive of poor long-term survival. Open repair (HR 0.70, 95% CI 0.60 to 0.82), statin therapy (HR 0.80, 95% CI 0.69 to 0.92) and antiplatelet therapy (HR 0.85, 95% CI 0.73 to 0.98) were predictive of improved long-term survival in patients that survived AAA repair.

In order to include individual patient predicted survival in the DES model, a survival model with a baseline hazard function was required. A Weibull survival model was developed including risk factors identified as in the previous Cox proportional hazards survival models. Backward stepwise AIC model selection was again used to optimise the model.

The Weibull model of prognostic factors for survival following discharge from hospital is shown in Table 17. This model did not require stratification on repair type. Female sex (HR 1.02, 95% CI 1.00 to 1.04), abnormal ECG (HR 1.02, 95% CI 1.01 to 1.04), abnormal sodium (HR 1.03, 95% CI 1.01 to 1.05), creatinine (HR 1.03, 95% CI 1.01 to 1.05) and anaemia (HR 1.03, 95% CI 1.01 to 1.05) were predictive of poor long-term survival. Open repair (HR 0.97, 95% CI 0.96 to 0.99), increasing age (HR 0.99, 95% CI 0.99 to 1.00), previous aortic surgery or stent (HR 0.95, 95% CI 0.91 to 1.00), statin therapy (HR 0.97, 95% CI 0.96 to 0.99) and antiplatelet therapy (HR 0.98, 95% CI 0.97 to 1.00) were predictive of improved survival in those patients that survived to discharge following AAA repair.

TABLE 17

Weibull model for survival following discharge from hospital following elective AAA repair

Patient age is seen to be protective in this model, but this is accounted for, as the baseline hazard function is monotonically increasing with time which captures the increasing risk of death with increasing age. This increase in risk with time is effectively the patient ageing. Age in this model may represent the healthy-old-person effect where only the healthiest elderly people undergo repair. Alternatively, developing an AAA at an earlier age may indicate more aggressive disease and therefore a poor long-term prognosis.

Summary

Our model has identified preoperative factors such as advancing age, female sex, ischaemic heart disease, abnormal ECG, anaemia, abnormal serum sodium and creatinine as being associated with worse long-term survival following AAA repair. Statin and antiplatelet therapy confer improved survival. This survival model will be incorporated into the final decision aid calculating individual patient indications for AAA repair, as it represents the principal reason for repairing an AAA.