Ghaneh Fananapazir1, John P McGahan1, Michael T Corwin1, Susan L Stewart2, Catherine T Vu1, Luke Wright1, Christoph Troppmann3. 1. 1 Department of Radiology, University of California Davis Medical Center, 4860 Y St, Ste 3100, Sacramento, CA 95817. 2. 2 Department of Public Health Sciences, University of California Davis Medical Center, Sacramento, CA. 3. 3 Department of Surgery, University of California Davis Medical Center, Sacramento, CA.
Abstract
OBJECTIVE: The objective of our study was to evaluate which spectral Doppler ultrasound parameters are useful in patients with clinical concern for transplant renal artery stenosis (TRAS) and create mathematically derived prediction models that are based on these parameters. MATERIALS AND METHODS: The study subjects included 120 patients with clinical signs of renal dysfunction who had undergone ultrasound followed by angiography (either digital subtraction angiography or MR angiography) between January 2005 and December 2015. Five ultrasound variables were evaluated: ratio of highest renal artery velocity to iliac artery velocity, highest renal artery velocity, spectral broadening, resistive indexes, and acceleration time. Angiographic studies were categorized as either showing no stenosis or showing stenosis. Reviewers assessed the ultrasound examinations for TRAS using all five variables, which we refer to as the full model, and using a reduced number of variables, which we refer to as the reduced-variable model; sensitivities and specificities were generated. RESULTS: Ninety-seven patients had stenosis and 23 had no stenosis. The full model had a sensitivity and specificity of 97% and 91%, respectively. The reduced-variable model excluded the ratio and resistive index variables without affecting sensitivity and specificity. We applied cutoff values to the variables in the reduced-variable model, which we refer to as the simple model. Using these cutoff values, the simple model showed a sensitivity and specificity of 96% and 83%. The simple model was able to categorize patients into four risk categories for TRAS: low, intermediate, high, and very high risk. CONCLUSION: We propose a simple model that is based on highest renal artery velocity, distal spectral broadening, and acceleration time to classify patients into risk categories for TRAS.
OBJECTIVE: The objective of our study was to evaluate which spectral Doppler ultrasound parameters are useful in patients with clinical concern for transplant renal artery stenosis (TRAS) and create mathematically derived prediction models that are based on these parameters. MATERIALS AND METHODS: The study subjects included 120 patients with clinical signs of renal dysfunction who had undergone ultrasound followed by angiography (either digital subtraction angiography or MR angiography) between January 2005 and December 2015. Five ultrasound variables were evaluated: ratio of highest renal artery velocity to iliac artery velocity, highest renal artery velocity, spectral broadening, resistive indexes, and acceleration time. Angiographic studies were categorized as either showing no stenosis or showing stenosis. Reviewers assessed the ultrasound examinations for TRAS using all five variables, which we refer to as the full model, and using a reduced number of variables, which we refer to as the reduced-variable model; sensitivities and specificities were generated. RESULTS: Ninety-seven patients had stenosis and 23 had no stenosis. The full model had a sensitivity and specificity of 97% and 91%, respectively. The reduced-variable model excluded the ratio and resistive index variables without affecting sensitivity and specificity. We applied cutoff values to the variables in the reduced-variable model, which we refer to as the simple model. Using these cutoff values, the simple model showed a sensitivity and specificity of 96% and 83%. The simple model was able to categorize patients into four risk categories for TRAS: low, intermediate, high, and very high risk. CONCLUSION: We propose a simple model that is based on highest renal artery velocity, distal spectral broadening, and acceleration time to classify patients into risk categories for TRAS.
Authors: Long Jiang Zhang; Jin Peng; Jiqiu Wen; U Joseph Schoepf; Akos Varga-Szemes; L Parkwood Griffith; Yuan Meng Yu; Shu Min Tao; Yan Jun Li; Xue Feng Ni; Jian Xu; Dong Hong Shi; Guang Ming Lu Journal: Eur Radiol Date: 2018-04-17 Impact factor: 5.315
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Authors: Emanuele David; Giovanni Del Gaudio; Francesco Maria Drudi; Vincenzo Dolcetti; Patrizia Pacini; Antonio Granata; Renzo Pretagostini; Manuela Garofalo; Antonio Basile; Maria Irene Bellini; Vito D'Andrea; Mariano Scaglione; Richard Barr; Vito Cantisani Journal: Tomography Date: 2022-06-28