¿Es útil la puntuación de Guy para predecir resultados en la nefrolitotomía percutánea? / Is Guys stone score useful for predicting outcomes in percutaneous nephrolithotomy?

Introducción La nefrolitotomía percutánea (NLPC) es actualmente el tratamiento de elección para los cálculos renales de gran volumen. El sistema de clasificación de la litiasis de Guy se desarrolló para predecir los resultados del tratamiento. El objetivo de este estudio fue evaluar la relación entre la clasificación de Guy y los resultados en nuestra institución.Materiales y métodos Se evaluaron retrospectivamente las historias clínicas de los pacientes diagnosticados de urolitiasis y tratados mediante NLPC entre enero de 2017 y diciembre de 2018. Los datos analizados incluyeron: edad, sexo, clasificación de Guy según los hallazgos preoperatorios, transfusión, tasa de ausencia de cálculos y requerimiento de procedimientos adicionales. Para la comparación de las proporciones se utilizó la prueba de chi-cuadrado. Se calcularon las odds ratio (OR) con intervalos de confianza (IC) del 95% mediante regresión logística.Resultados Se analizaron 386 pacientes, 53,89% mujeres, con una media de edad de 45,59±15 años. Un total de 112 pacientes (29,04%) fueron clasificados como Guy I, 92 pacientes (23,82%) como Guy II, 94 pacientes (24,34%) como Guy III y 88 pacientes (22,80%) como Guy IV. No hubo diferencias entre los grupos en cuanto a la necesidad de transfusión de sangre. La tasa de ausencia de cálculos fue del 96,43% (n=108) para los pacientes Guy I; 84,78% (n=72) para Guy II; 76,59% (n=72) para Guy III y 50% (n=44) para las categorías Guy IV (p <0,0001). Tomando como referencia el grupo Guy I, las OR para requerir un nuevo tratamiento por cálculos residuales fueron: Guy II 4,85 (IC=0,95 a 24,60, p=0,05) a Guy III 8,25 (IC=1,73 a 39,44, p=0,008) y Guy IV 27 (IC=5,84 a 124,70; p <0,0001).Conclusión Se observó una asociación estadísticamente significativa entre la puntuación de Guy y las tasas de ausencia de cálculos tras la NLPC. La puntuación de Guy fue útil para predecir los resultados de la NLPC en nuestro grupo

Introduction Percutaneous Nephrolithotomy (PCNL) is currently the treatment of choice in large kidney stones. Guýs stone score was developed to predict treatment outcomes. The aim of this study was to evaluate the relationship between Guýs score and outcomes in our institution.Materials and Methods The medical records of patients diagnosed with urolithiasis and treated by PCNL were retrospectively evaluated between January 2017 and December 2018. Analyzed data included: age, sex, Guy's score based on preoperative findings, transfusion, stone-free rate and requirement of auxiliary procedures. Chi-square test was used for the comparison of proportions. Odds ratios (OR) with confidence intervals (CIs) of 95% were calculated by logistic regression.ResultsA total of 386 patients, 53.89% female, mean age of 45.59±15 years were analyzed. Guy's score was as follows: 112 patients (29.04%) were classified as Guy I, 92 patients (23.82%) as Guy II, 94 patients (24.34%) as Guy III, and 88 patients (22.80%) as Guy IV. There were no differences between groups in terms of blood transfusions. Stone-free rate was 96.43% (n=108) for patients Guy I; 84.78% (n=72) for Guy II; 76.59% (n=72) for Guy III and 50% (n=44) for Guy IV categories (p <0.0001). Taking as a reference Guy I group, the OR to require new treatment for residual stones were: Guy II 4.85 (CI=0.95 to 24.60, P=0.05) to Guy III 8.25 (CI=1.73 to 39.44, P=0.008) and Guy IV 27 (CI=5.84 to 124.70; p <0.0001).ConclusionThere was a statistically significant association between Guýs Score and stone-free rates following PCNL. Guy's score was useful to predict results of PCNL in our group (AU)

Is Guy's stone score useful for predicting outcomes in percutaneous nephrolithotomy?

INTRODUCTION: Percutaneous Nephrolithotomy (PCNL) is currently the treatment of choice in large kidney stones. Guy's stone score was developed to predict treatment outcomes. The aim of this study was to evaluate the relationship between Guy's score and outcomes in our institution. MATERIALS AND METHODS: The medical records of patients diagnosed with urolithiasis and treated by PCNL were retrospectively evaluated between January 2017 and December 2018. Analyzed data included: age, sex, Guy's score based on preoperative findings, transfusion, stone-free rate and requirement of auxiliary procedures. Chi-square test was used for the comparison of proportions. Odds ratios (OR) with confidence intervals (CIs) of 95% were calculated by logistic regression. RESULTS: A total of 386 patients, 53.89% female, mean age of 45.59 ±â€¯15 years were analyzed. Guy's score was as follows: 112 patients (29.04%) were classified as Guy I, 92 patients (23.82%) as Guy II, 94 patients (24.34%) as Guy III, and 88 patients (22.80%) as Guy IV. There were no differences between groups in terms of blood transfusions. Stone-free rate was 96.43% (n = 108) for patients Guy I; 84.78% (n = 72) for Guy II; 76.59% (n = 72) for Guy III and 50% (n = 44) for Guy IV categories (p < 0.0001). Taking as a reference Guy I group, the OR to require new treatment for residual stones were: Guy II 4.85 (CI = 0.95 to 24.60, P = 0.05) to Guy III 8.25 (CI = 1.73 to 39.44, P = 0.008) and Guy IV 27 (CI = 5.84 to 124.70; p < 0.0001). CONCLUSION: There was a statistically significant association between Guy's Score and stone-free rates following PCNL. Guy's score was useful to predict results of PCNL in our group.

Validation of a model-based inverse kinematics approach based on wearable inertial sensors.

Wearable inertial measurement units (IMUs) are a promising solution to human motion estimation. Using IMUs 3D orientations, a model-driven inverse kinematics methodology to estimate joint angles is presented. Estimated joint angles were validated against encoder-measured kinematics (robot) and against marker-based kinematics (passive mechanism). Results are promising, with RMS angular errors respectively lower than 3 and 6 deg over a minimum range of motion of 50 deg (robot) and 160 deg (passive mechanism). Moreover, a noise robustness analysis revealed that the model-driven approach reduces the effects of experimental noises, making the proposed technique particularly suitable for application in human motion analysis.