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Immediate and strategic action is needed to improve environmental sustainability and reduce the detrimental effects of climate change. Climate change is already adversely affecting the health of Canadians related to worsening air pollution and wildfire smoke, increasing frequency and intensity of extreme weather events, and expansion of vector-borne and infectious illnesses. On one hand, radiology contributes to the climate crisis by generating greenhouse gas emissions and waste during the production, manufacture, transportation, and use of medical imaging equipment and supplies. On the other hand, radiology departments are also susceptible to equipment and infrastructure damage from flooding, extreme temperatures, and power failures, as well as workforce shortages due to injury and illness, potentially disrupting radiology services and increasing costs. The Canadian Association of Radiologists' (CAR) advocacy for environmentally sustainable radiology in Canada encompasses both minimizing the detrimental effects that delivery of radiology services has on the environment and optimizing the resilience of radiology departments to increasing health needs and changing patterns of disease on imaging related to climate change. This statement provides specific recommendations and pathways to help guide radiologists, medical imaging leadership teams, industry partners, governments, and other key stakeholders to transition to environmentally sustainable, net-zero, and climate-resilient radiology organizations. Specific consideration is given to unique aspects of medical imaging in Canada. Finally, environmentally sustainable radiology programs, policies, and achievements in Canada are highlighted.
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Purpose: Stone burden has been reported as an independent predictor of stone-free rate after percutaneous nephrolithotomy (PCNL); however no consensus exists on a standardized method for measuring stone burden. Recently, stone volume has been advocated as the most accurate means of measuring stone burden. We aimed to compare different measuring methods of stone burden and to identify the predictive value of each for outcomes after PCNL. Materials and Methods: We performed a retrospective review of a prospective database of patients who underwent PCNL between 2006 and 2013. A preoperative CT and postoperative imaging at discharge were necessary for eligibility. Stone burden was assessed through four different ways on CT images: (1) cumulative stone diameter; (2) estimated SA (surface area) calculated as longest × orthogonal diameter × π/4; (3) manual outline of stone and computer SA calculation; and (4) automated 3D volume calculation using specific software. Primary outcome was stone-free status (SFS) at discharge. Secondary outcomes included operative time and the need for an ancillary procedure. Regression analysis and receiver operating characteristic curve analysis were used to evaluate the predictive value of each method. Results: Of 313 included patients, 69.6% were stone free at discharge. All measures of stone burden were independent predictors of SFS [OR and 95% CI of 1.027 (1.014, 1.040), 1.481 (1.180, 1.858), 1.736 (1.266, 2.380), and 1.311 (1.127, 1.526), respectively] and demonstrated similar predictive accuracy (area under the curve = 0.630, 0.630, 0.627, and 0.638, respectively). Stone burden by any measure was an independent predictor of operative time and secondary procedure. Conclusions: We demonstrated that measuring stone burden by manual outline or automated 3D volume on reformatted CT images had no added value compared with orthogonal measurement for predicting outcomes after PCNL.
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Cálculos Renales , Nefrolitotomía Percutánea , Nefrostomía Percutánea , Humanos , Imagenología Tridimensional , Cálculos Renales/diagnóstico por imagen , Cálculos Renales/cirugía , Estudios Retrospectivos , Tomografía Computarizada por Rayos X , Resultado del TratamientoRESUMEN
BACKGROUND AND PURPOSE: Several scoring systems have recently emerged to predict stone-free rate (SFR) and complications after percutaneous nephrolithotomy (PCNL). We aimed to compare the most commonly used scoring systems (Guy's stone score, S.T.O.N.E. nephrolithometry, and CROES nomogram), assess their predictive accuracy for SFR and other postoperative variables, and develop a risk group stratification based on these scoring systems. MATERIALS AND METHODS: We performed a retrospective review of patients who have had a PCNL at four academic institutions between 2006 and 2013. Primary outcome was SFR within 3 weeks of the surgery and secondary outcomes were operative time (OT), complications, and length of stay (LOS). We performed chi-squared, t-test, logistic, linear, and Poisson regressions, as well as receiver operating characteristics curve with area under the curve (AUC) calculation. RESULTS: We identified 586 patients eligible for analysis. Of these, 67.4% were stone free. Guy's, S.T.O.N.E., and CROES score were predictive of SFR on multivariable logistic regression (odds ratio [OR]: 1.398, 95% confidence interval [CI]: 1.056, 1.852, p = 0.019; OR: 1.417, 85% CI: 1.231, 1.631, p < 0.001; OR: 0.993, 95% CI: 0.988, 0.998, p = 0.004) and have similar predictive accuracy with AUCs of 0.629, 0.671, and 0.646, respectively. On multivariable linear regression, only S.T.O.N.E. was an independent predictor of longer OT (ß = 14.556, 95% CI: 12.453, 16.660, p < 0.001). None of the scores were independent predictors of postoperative complications or a longer LOS. Poisson regression allowed for risk group stratification and showed the S.T.O.N.E. score and CROES nomogram to have the most distinct risk groups. CONCLUSIONS: The three evaluated scoring systems have similar predictive accuracy of SFR. S.T.O.N.E. has additional value in predicting OT. Risk group stratification can be used for patient counseling. Further research is needed to identify whether or not any is superior to the others with regard to clinical usefulness and predictive accuracy.
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Cálculos Renales/diagnóstico , Cálculos Renales/cirugía , Nefrostomía Percutánea/métodos , Índice de Severidad de la Enfermedad , Adulto , Anciano , Algoritmos , Área Bajo la Curva , Femenino , Humanos , Tiempo de Internación , Modelos Logísticos , Masculino , Persona de Mediana Edad , Análisis Multivariante , Nefrostomía Percutánea/efectos adversos , Nomogramas , Tempo Operativo , Complicaciones Posoperatorias , Periodo Posoperatorio , Pronóstico , Curva ROC , Estudios Retrospectivos , Riesgo , Resultado del TratamientoRESUMEN
INTRODUCTION AND OBJECTIVES: The mineral composition of a urinary stone may influence its surgical and medical treatment. Previous attempts at identifying stone composition based on mean Hounsfield Units (HUm) have had varied success. We aimed to evaluate the additional use of standard deviation of HU (HUsd) to more accurately predict stone composition. METHODS: We identified patients from two centers who had undergone urinary stone treatment between 2006 and 2013 and had mineral stone analysis and a computed tomography (CT) available. HUm and HUsd of the stones were compared with ANOVA. Receiver operative characteristic analysis with area under the curve (AUC), Youden index, and likelihood ratio calculations were performed. RESULTS: Data were available for 466 patients. The major components were calcium oxalate monohydrate (COM), uric acid, hydroxyapatite, struvite, brushite, cystine, and CO dihydrate (COD) in 41.4%, 19.3%, 12.4%, 7.5%, 5.8%, 5.4%, and 4.7% of patients, respectively. The HUm of UA and Br was significantly lower and higher than the HUm of any other stone type, respectively. HUm and HUsd were most accurate in predicting uric acid with an AUC of 0.969 and 0.851, respectively. The combined use of HUm and HUsd resulted in increased positive predictive value and higher likelihood ratios for identifying a stone's mineral composition for all stone types but COM. CONCLUSIONS: To the best of our knowledge, this is the first report of CT data aiding in the prediction of brushite stone composition. Both HUm and HUsd can help predict stone composition and their combined use results in higher likelihood ratios influencing probability.