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Manufacturing models may offer another way to compare clinical procedures
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Applying models more commonly found in manufacturing environments to peripheral interventions gives clinicians another way to assess comparative effectiveness of clinical procedures, study results showed.
Andrew J. Meltzer, MD, and researchers at Weill Cornell Medical College and the Cornell University School of Engineering adapted reliability engineering and life-cycle methodologies used in manufacturing to examine outcomes after peripheral endovascular intervention to assess whether they could better predict early failures.
There were 434 primary endovascular peripheral interventions done for claudication (51.8%), rest pain (16.8%) or tissue loss (31.3%) from 2005 to 2010. Failure was noted after 159 (36.6%) interventions during a mean follow-up of 18 months. Independent predictors of patency loss were rest pain (HR=2.5; 95% CI, 1.6-4.1) and tissue loss (HR=3.2; 95% CI, 2.0-5.2).
Interventions for claudication had an increasing failure rate, suggesting that failure is more likely to occur in a delayed fashion. Interventions for critical limb ischemia had a decreasing failure rate, suggesting that there were more early failures. The researchers quantified these variations in failure rate using engineering models and identified the characteristics associated with the poorest performance.
By 3.1 months, 10% of interventions failed. Independent predictors of failure before this time point were the following: tissue loss, long lesion length, chronic total occlusions, HF and end-stage renal disease.
Redefining benchmarks using empirically derived endpoints, such as 3.1 months as the threshold for “early failure,” could aid not only comparative effectiveness research, but also might help vascular specialists more appropriately choose patients for endovascular intervention, Meltzer and colleagues wrote.
“Patients with multiple risk factors for failed endovascular intervention before 3.1 months might be better served by surgical bypass,” they wrote.
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