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      Predictors of Contrast Volume in Transcatheter Aortic Valve Replacement


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          Background: Contrast-induced acute kidney injury (CIAKI) is a frequent and serious complication of transcatheter aortic valve replacement (TAVR). The most important procedural risk factor for CIAKI is contrast volume. Objectives: Because contrast volume is a modifiable factor that directly predicts CIAKI, we sought to identify predictors of increased contrast volume in TAVR patients. Identification of such predictors may allow both prediction and mitigation of CIAKI risk following TAVR. Method: We retrospectively analyzed data from consecutive patients not on hemodialysis who underwent successful TAVR at a single US center from 2013 to 2018. Using multivariable linear regression modelling, we assessed the relationships between contrast volumes and 49 patient and procedural factors hypothesized to be potential predictors. Results: In 295 patients, we identified 17 factors that independently predicted contrast volume, 10 of which contributed 90% of the complete model’s r<sup>2</sup> value. Procedure year (suggesting a learning curve), aortic insufficiency, radiation dose, prior AVR, and previous pacemaker placement were statistically the most significant predictors of CIAKI. TAVR device and diabetes were notably not predictors. Conclusions: To predict and reduce contrast use in TAVR, patients at risk for increased contrast volume may be identified using the predictors elucidated in this study. For such patients, strategies for contrast reduction and renal protection may be employed.

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          Renal function-based contrast dosing predicts acute kidney injury following transcatheter aortic valve implantation.

          This study sought to assess whether the volume of contrast media (CM) influences the occurrence of acute kidney injury (AKI) following transcatheter aortic valve implantation (TAVI).
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            Derivation and External Validation of Prediction Models for Advanced Chronic Kidney Disease Following Acute Kidney Injury

            Question Can the risk of progression to advanced chronic kidney disease be accurately predicted when patients who had acute kidney injury during their hospital stay are discharged from the hospital? Findings A multivariable model was developed with 9973 participants and was externally validated with 2761 participants. In the external validation cohort, a model that included age, sex, acute kidney injury stage, prehospitalization serum creatinine level, albuminuria, and discharge serum creatinine achieved a C statistic of 0.81 for predicting advanced chronic kidney disease after hospital discharge. Meaning This model was able to predict advanced chronic kidney disease following hospitalization with acute kidney injury but requires evaluation of its utility in a clinical setting. Importance Some patients will develop chronic kidney disease after a hospitalization with acute kidney injury; however, no risk-prediction tools have been developed to identify high-risk patients requiring follow-up. Objective To derive and validate predictive models for progression of acute kidney injury to advanced chronic kidney disease. Design, Setting, and Participants Data from 2 population-based cohorts of patients with a prehospitalization estimated glomerular filtration rate (eGFR) of more than 45 mL/min/1.73 m 2 and who had survived hospitalization with acute kidney injury (defined by a serum creatinine increase during hospitalization > 0.3 mg/dL or > 50% of their prehospitalization baseline), were used to derive and validate multivariable prediction models. The risk models were derived from 9973 patients hospitalized in Alberta, Canada (April 2004-March 2014, with follow-up to March 2015). The risk models were externally validated with data from a cohort of 2761 patients hospitalized in Ontario, Canada (June 2004-March 2012, with follow-up to March 2013). Exposures Demographic, laboratory, and comorbidity variables measured prior to discharge. Main Outcomes and Measures Advanced chronic kidney disease was defined by a sustained reduction in eGFR less than 30 mL/min/1.73 m 2 for at least 3 months during the year after discharge. All participants were followed up for up to 1 year. Results The participants (mean [SD] age, 66 [15] years in the derivation and internal validation cohorts and 69 [11] years in the external validation cohort; 40%-43% women per cohort) had a mean (SD) baseline serum creatinine level of 1.0 (0.2) mg/dL and more than 20% had stage 2 or 3 acute kidney injury. Advanced chronic kidney disease developed in 408 (2.7%) of 9973 patients in the derivation cohort and 62 (2.2%) of 2761 patients in the external validation cohort. In the derivation cohort, 6 variables were independently associated with the outcome: older age, female sex, higher baseline serum creatinine value, albuminuria, greater severity of acute kidney injury, and higher serum creatinine value at discharge. In the external validation cohort, a multivariable model including these 6 variables had a C statistic of 0.81 (95% CI, 0.75-0.86) and improved discrimination and reclassification compared with reduced models that included age, sex, and discharge serum creatinine value alone (integrated discrimination improvement, 2.6%; 95% CI, 1.1%-4.0%; categorical net reclassification index, 13.5%; 95% CI, 1.9%-25.1%) or included age, sex, and acute kidney injury stage alone (integrated discrimination improvement, 8.0%; 95% CI, 5.1%-11.0%; categorical net reclassification index, 79.9%; 95% CI, 60.9%-98.9%). Conclusions and Relevance A multivariable model using routine laboratory data was able to predict advanced chronic kidney disease following hospitalization with acute kidney injury. The utility of this model in clinical care requires further research. This study uses data from Canadian administrative and clinical ambulatory databases to derive and validate risk prediction models for advanced chronic kidney disease in patients hosptialized with acute kidney injury.
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              Acute kidney injury after transcatheter aortic valve implantation: incidence, predictors and impact on mortality.

              There is a paucity of data describing acute kidney injury (AKI) following transcatheter aortic valve implantation and its impact on mortality remains unknown. We therefore evaluate the incidence, predictors and impact of AKI following transcatheter aortic valve implantation. We searched MEDLINE for studies from 2008 to 2013, evaluating AKI after transcatheter aortic valve implantation. All studies were compared according to the incidence, predictors and impact of AKI following transcatheter aortic valve implantation. AKI was diagnosed according to the Valve Academic Research Consortium definition using the RIFLE criteria. Thirteen studies with more than 1900 patients were included. AKI occurred in 8.3-57% of the patients. The following factors were associated with AKI: blood transfusion; transapical access; preoperative creatinine concentration; peripheral vascular disease; hypertension; and procedural bleeding events. The 30-day mortality rate in patients with AKI ranged from 13.3% to 44.4% and was 2-6-fold higher than in patients without AKI. The amount of contrast agent used was not associated with the occurrence of AKI. AKI is a common complication, with an incidence of 8.3-57% following transcatheter aortic valve implantation. Patients with AKI had higher 30-day and late mortality rates. However, AKI was related to the amount of contrast volume used in only one study.

                Author and article information

                S. Karger AG
                September 2020
                02 July 2020
                : 145
                : 9
                : 608-610
                aDivision of Cardiovascular Medicine, University of Nebraska Medical Center and Nebraska Medicine, Omaha, Nebraska, USA
                bSchool of Public Health, University of Nebraska Medical Center, Omaha, Nebraska, USA
                cDivision of Cardiovascular Medicine, Warren Alpert Medical School of Brown University and Lifespan Cardiovascular Institute, Providence, Rhode Island, USA
                dDivision of Cardiovascular Medicine, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts, USA
                eDivision of Cardiovascular Medicine, Richard M. Ross Heart Hospital, The Ohio State University, Columbus, Ohio, USA
                Author notes
                *Andrew M. Goldsweig, Division of Cardiovascular Medicine, University of Nebraska Medical Center, 982265 Nebraska Medical Center, Omaha, NE 68198 (USA), andrew.goldsweig@unmc.edu
                507506 Cardiology 2020;145:608–610
                © 2020 S. Karger AG, Basel

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                Valvular Heart Disease: Short Communication


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