Clinical Outcome and Morphologic Analysis after Endovascular Aneurysm Repair Using the Excluder Endograft

OBJECTIVE: Long-term follow-up after endovascular aneurysm repair (EVAR) is very scarce, and doubt remains regarding the durability of these procedures. We designed a retrospective cohort study to assess long-term clinical outcome and morphologic changes in patients with abdominal aortic aneurysms (AAAs) treated by EVAR using the Excluder endoprosthesis (W. L. Gore and Associates, Flagstaff, Ariz). METHODS: From 2000 to 2007, 179 patients underwent EVAR in a tertiary institution. Clinical data were retrieved from a prospective database. All patients treated with the Excluder endoprosthesis were included. Computed tomography angiography (CTA) scans were retrospectively analyzed preoperatively, at 30 days, and at the last follow-up using dedicated tridimensional reconstruction software. For patients with complications, all remaining CTAs were also analyzed. The primary end point was clinical success. Secondary end points were freedom from reintervention, sac growth, types I and III endoleak, migration, conversion to open repair, and AAA-related death or rupture. Neck dilatation, renal function, and overall survival were also analyzed. RESULTS: Included were 144 patients (88.2% men; mean age, 71.6 years). Aneurysms were ruptured in 4.9%. American Society of Anesthesiologists classification was III/IV in 61.8%. No patients were lost during a median follow-up of 5.0 years (interquartile range, 3.1-6.4; maximum, 11.2 years). Two patients died of medical complications ≤ 30 days after EVAR. The estimated primary clinical success rates at 5 and 10 years were 63.5% and 41.1%, and secondary clinical success rates were 78.3% and 58.3%, respectively. Sac growth was observed in 37 of 142 patients (26.1%). Cox regression showed type I endoleak during follow-up (hazard ratio, 3.74; P = .008), original design model (hazard ratio, 3.85; P = .001), and preoperative neck diameter (1.27 per mm increase, P = .006) were determinants of sac growth. Secondary interventions were required in 32 patients (22.5%). The estimated 10-year rate of AAA-related death or rupture was 2.1%. Overall life expectancy after AAA repair was 6.8 years. CONCLUSIONS: EVAR using the Excluder endoprosthesis provides a safe and lasting treatment for AAA, despite the need for maintained surveillance and secondary interventions. At up to 11 years, the risk of AAA-related death or postimplantation rupture is remarkably low. The incidences of postimplantation sac growth and secondary intervention were greatly reduced after the introduction of the low-permeability design in 2004.

Modeling in-Hospital Patient Survival During the First 28 Days After Intensive Care Unit Admission: a Prognostic Model for Clinical Trials in General Critically Ill Patients

OBJECTIVE: The objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches. DESIGN: The study was designed to develop an outcome prediction model for 28-day in-hospital mortality using (a) logistic regression with random effects and (b) a multilevel Cox proportional hazards model. SETTING: The study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort. PATIENTS AND PARTICIPANTS: Patients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The database was divided randomly into 5 roughly equal-sized parts (at the ICU level). It was thus possible to run the model-building procedure 5 times, each time taking four fifths of the sample as a development set and the remaining fifth as the validation set. At 28 days after ICU admission, 19.98% of the patients were still in the hospital. Because of the different sampling space and outcome variables, both models presented a better fit in this sample than did the SAPS 3 admission score calibrated to vital status at hospital discharge, both on the general population and in major subgroups. CONCLUSIONS: Both statistical methods can be used to model the 28-day in-hospital mortality better than the SAPS 3 admission model. However, because the logistic regression approach is specifically designed to forecast 28-day mortality, and given the high uncertainty associated with the assumption of the proportionality of risks in the Cox model, the logistic regression approach proved to be superior.