2007 Abstracts: Prospective Decision Analysis for Peripheral Vascular Disease Predicts Future Quality of Life

Background: Decision-making for peripheral vascular disease can be quite complex due to issues with patient functional status, anatomic considerations, uncertainty of favorable outcome, medical co-morbidities, and limitations in life expectancy. The ability of a prospective decision analysis model to predict quality of life in patients with lower extremity arterial occlusive disease was tested.
Methods: A Markov model was compared to standard clinical decision-making in a prospective trial of 206 patients with symptomatic lower extremity arterial disease of any severity. Utility assessment and generalized (SF-36) and disease-specific (WIQ) quality of life were derived prior to treatment. Estimates of treatment outcome probabilities as well as intended and actual treatment plans were provided by attending vascular surgeons. Patient treatment was individualized, and quality of life was reassessed 4 and 12 months later.
Results: Primary intervention consisted of amputation for 9, bypass for 42, angioplasty for 8, and medical treatment for 146 patients. Considering all patients, no improvement in mean overall patient quality of life measured by SF-36 Physical Component Score (27+8 v 28+8, P=0.868) or WIQ (39+22 v 39+23, P=0.127) was noted 12 months after counseling and treatment by the vascular surgeons. Bodily Pain was the only individual SF-36 category to improve (40+23 v 49+25, P=0.026). Among patients with the most severe symptoms, significant improvements in Bodily Pain were observed among patients with the most severe pain (68+25 v 37+23, P=0.024) and among those undergoing bypass (60+29 v 31+22, P=0.020). It is noteworthy that when the surgeon's treatment was incongruent with the Markov model, patients were more likely to report a poorer quality of life at one year (PCS 25+8 v 29+8, P<0.001). The quality of life predicted at baseline by the Markov model correlated positively with the Physical Component Score (r=0.23), Bodily Pain (r=0.33), Fatigue (r=0.44) and negatively with WIQ (r=-0.08) observed one year later. Although the concept of patient "Utility" used in decision analysis has never been proven to be directly linked to standard quality of life measurements, the current study observed "Utility" to correlate positively with Physical Component Score(r=0.23), Bodily Pain (r=0.33), Fatigue (r=0.26), and WIQ (r=0.44).
Conclusions: Prospective application of an individualized decision Markov model in patients with vascular disease was predictive of poor patient quality of life at one year for patients among whom the treatment received did not follow the model's recommendation. This model may be useful to identify patients at risk for poor outcomes with standard clinical decision-making.