D C Norvell1, M L Thompson2, E J Boyko3,4,5, G Landry6, A J Littman3,5,7, W G Henderson8, A P Turner9,10, C Maynard7, K P Moore5, J M Czerniecki9,10,11. 1. Spectrum Research, Tacoma, Washington, USA. 2. Department of Biostatistics, University of Washington, Seattle, Washington, USA. 3. Department of Epidemiology, University of Washington, Seattle, Washington, USA. 4. Division of Internal Medicine, University of Washington, Seattle, Washington, USA. 5. Epidemiologic Research and Information Center, VA Puget Sound Health Care System, Seattle, Washington, USA. 6. Department of Surgery, Division of Vascular Surgery, Oregon Health and Science University, Portland, Oregon, USA. 7. Health Services Research and Development, VA Puget Sound Health Care System, Seattle, Washington, USA. 8. Adult and Child Consortium for Outcomes Research and Delivery Science, University of Colorado, Denver, Colorado, USA. 9. Department of Rehabilitation Medicine, University of Washington, Seattle, Washington, USA. 10. Rehabilitation Care Services, VA Puget Sound Health Care System, Seattle, Washington, USA. 11. Veterans Affairs (VA) Center for Limb Loss and Mobility (CLiMB), VA Puget Sound Health Care System, Seattle, Washington, USA.
Abstract
BACKGROUND: Patients who undergo lower extremity amputation secondary to the complications of diabetes or peripheral artery disease have poor long-term survival. Providing patients and surgeons with individual-patient, rather than population, survival estimates provides them with important information to make individualized treatment decisions. METHODS: Patients with peripheral artery disease and/or diabetes undergoing their first unilateral transmetatarsal, transtibial or transfemoral amputation were identified in the Veterans Affairs Surgical Quality Improvement Program (VASQIP) database. Stepdown logistic regression was used to develop a 1-year mortality risk prediction model from a list of 33 candidate predictors using data from three of five Department of Veterans Affairs national geographical regions. External geographical validation was performed using data from the remaining two regions. Calibration and discrimination were assessed in the development and validation samples. RESULTS: The development sample included 5028 patients and the validation sample 2140. The final mortality prediction model (AMPREDICT-Mortality) included amputation level, age, BMI, race, functional status, congestive heart failure, dialysis, blood urea nitrogen level, and white blood cell and platelet counts. The model fit in the validation sample was good. The area under the receiver operating characteristic (ROC) curve for the validation sample was 0·76 and Cox calibration regression indicated excellent calibration (slope 0·96, 95 per cent c.i. 0·85 to 1·06; intercept 0·02, 95 per cent c.i. -0·12 to 0·17). Given the external validation characteristics, the development and validation samples were combined, giving a total sample of 7168. CONCLUSION: The AMPREDICT-Mortality prediction model is a validated parsimonious model that can be used to inform the 1-year mortality risk following non-traumatic lower extremity amputation of patients with peripheral artery disease or diabetes. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.
BACKGROUND:Patients who undergo lower extremity amputation secondary to the complications of diabetes or peripheral artery disease have poor long-term survival. Providing patients and surgeons with individual-patient, rather than population, survival estimates provides them with important information to make individualized treatment decisions. METHODS:Patients with peripheral artery disease and/or diabetes undergoing their first unilateral transmetatarsal, transtibial or transfemoral amputation were identified in the Veterans Affairs Surgical Quality Improvement Program (VASQIP) database. Stepdown logistic regression was used to develop a 1-year mortality risk prediction model from a list of 33 candidate predictors using data from three of five Department of Veterans Affairs national geographical regions. External geographical validation was performed using data from the remaining two regions. Calibration and discrimination were assessed in the development and validation samples. RESULTS: The development sample included 5028 patients and the validation sample 2140. The final mortality prediction model (AMPREDICT-Mortality) included amputation level, age, BMI, race, functional status, congestive heart failure, dialysis, blood ureanitrogen level, and white blood cell and platelet counts. The model fit in the validation sample was good. The area under the receiver operating characteristic (ROC) curve for the validation sample was 0·76 and Cox calibration regression indicated excellent calibration (slope 0·96, 95 per cent c.i. 0·85 to 1·06; intercept 0·02, 95 per cent c.i. -0·12 to 0·17). Given the external validation characteristics, the development and validation samples were combined, giving a total sample of 7168. CONCLUSION: The AMPREDICT-Mortality prediction model is a validated parsimonious model that can be used to inform the 1-year mortality risk following non-traumatic lower extremity amputation of patients with peripheral artery disease or diabetes. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.
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