CKD and CKD-related mineral and bone disorders (CKD-MBDs) are associated with high cardiovascular and mortality risks. In randomized clinical trials (RCTs), no single drug intervention has been shown to reduce the high mortality risk in dialysis patients, but several robust secondary analyses point toward important potential beneficial effects of controlling CKD-MBD-related factors and secondary hyperparathyroidism. The advent of cinacalcet, which has a unique mode of action at the calcium-sensing receptor, represented an important step forward in controlling CKD-MBD. In addition, new RCTs have conclusively shown that cinacalcet improves achievement of target levels for all of the metabolic abnormalities associated with CKD-MBD and may also attenuate the progression of vascular and valvular calcifications in dialysis patients. However, a final conclusion on the effect of cinacalcet on hard outcomes remains elusive. Tolerance of cinacalcet is limited by frequent secondary side effects such as nausea, vomiting, hypocalcemia and oversuppression of parathyroid hormone, which may cause some management difficulties, especially for those lacking experience with the drug. Against this background, this review aims to summarize the results of studies on cinacalcet, up to and including the publication of the recent ADVANCE and EVOLVE RCTs, as well as recent post hoc analyses, and to offer practical guidance on how to improve the clinical management of the most frequent adverse events associated with cinacalcet, based on both currently available information and personal experience. In addition, attention is drawn to less common secondary effects of cinacalcet treatment and advisable precautions.
Background Models developed to predict hospital-acquired AKI (HA-AKI) in non-critically ill patients have a low sensitivity, do not include dynamic changes of risk factors and do not allow to establish a time relationship between exposure to risk factors and AKI. We developed and externally validated a predictive model of HA-AKI, integrating electronic health databases and recording the exposure to risk factors prior to the detection of AKI. Methods Study set: 36,852 non-critically ill hospitalized patients admitted from January to December 2017. Using stepwise logistic analyses, including demography, chronic comorbidities, and exposure to risk factors prior to AKI detection, we developed a multivariate model to predict HA-AKI. This model was then externally validated in 21,545 non-critical patients admitted to the validation center in the period from June 2017 to December 2018. Results The incidence of AKI in the study set was 3.9%. Among chronic comorbidities, the highest odds ratios, were conferred by chronic kidney disease, urologic and liver disease. Among acute complications, the highest odd ratios were associated with acute respiratory failure, anemia, systemic inflammatory response syndrome (SIRS), circulatory shock and major surgery. The model showed an AUC of 0.907(95% CI 0.902 to 0.908), a sensitivity of 82.7 (95% CI 80.7–84.6) and a specificity of 84.2 (95% CI 83.9-84.6) to predict HA-AKI, with an adequate goodness-of-fit for all risk categories (Chi2:6.02, p:0.64). In the validation set, prevalence of AKI was 3.2%. The model showed an AUC of 0.905 (95% CI 0.904-0.910) a sensitivity of 81.2 (95% CI 79.2–83.1) and a specificity of 82.5 (95% CI 82.2- 83) to predict HA-AKI and had an adequate goodness-of-fit for all risk categories (Chi2:4.2, p:0.83). An online tool predaki.amalfianalytics.com is available to calculate the risk of AKI in other hospital environments. Conclusions By using electronic health data records, our study provides a model that can be used in clinical practice to obtain an accurate dynamic and updated assessment of the individual risk of HA-AKI along the hospital admission period in non-critically ill patients.
Background. The current models developed to predict hospital-acquired AKI (HA-AKI) in non-critically ill fail to identify the patients at risk of severe HA-AKI stage 3. Objective. To develop and externally validate a model to predict the individual probability of developing HA-AKI stage 3 through the integration of electronic health databases. Methods. Study set: 165,893 non-critically ill hospitalized patients. Using stepwise logistic regression analyses, including demography, chronic comorbidities, and exposure to risk factors prior to AKI detection, we developed a multivariate model to predict HA-AKI stage 3. This model was then externally validated in 43,569 non-critical patients admitted to the validation center. Results. The incidence of HA-AKI stage 3 in the study set was 0.6%. Among chronic comorbidities, the highest odds ratios were conferred by ischemic heart disease, ischemic cerebrovascular disease, chronic congestive heart failure, chronic obstructive pulmonary disease, chronic kidney disease and liver disease. Among acute complications, the highest odd ratios were associated with acute respiratory failure, major surgery and exposure to nephrotoxic drugs. The model showed an AUC of 0.906 (95% CI 0.904 to 0.908), a sensitivity of 89.1 (95% CI 87.0–91.0) and a specificity of 80.5 (95% CI 80.2–80.7) to predict HA-AKI stage 3, but tended to overestimate the risk at low-risk categories with an adequate goodness-of-fit for all risk categories (Chi2:16.4, p: 0.034). In the validation set, incidence of HA-AKI stage 3 was 0.62%. The model showed an AUC of 0.861 (95% CI 0.859–0.863), a sensitivity of 83.0 (95% CI 80.5–85.3) and a specificity of 76.5 (95% CI 76.2–76.8) to predict HA-AKI stage 3 with an adequate goodness of fit for all risk categories (Chi2:15.42, p: 0.052). Conclusions. Our study provides a model that can be used in clinical practice to obtain an accurate dynamic assessment of the individual risk of HA-AKI stage 3 along the hospital stay period in non-critically ill patients.
Background The Madrid Acute Kidney Injury Prediction Score (MAKIPS) is a recently described tool able to do an automatic calculation of the risk of hospital-acquired AKI (HA-AKI) from electronic clinical records and could easily be implemented in clinical practice but, to date, it has not been externally validated. The objective of our study was to perform an external validation of the MAKIPS score in a hospital with different characteristics and case-mix. Methods External validation cohort study of the MAKIPS score, performed on patients hospitalized at a single tertiary hospital between April 2018 and September 2019. Performance was assessed by discrimination using area under the receiver operating characteristic curves (AUCROC) and calibration plots. Results HA-AKI in the external validation cohort was 5.3%. When compared to the MAKIPS cohort, the validation cohort showed a higher prevalence of men, diabetes, hypertension, cardiovascular disease, cerebrovascular disease, anaemia, congestive heart failure, chronic pulmonary disease, connective tissue diseases and renal disease, whereas, the prevalences of peptic ulcer disease, liver disease, malignancy, metastatic solid tumours and AIDS were significantly lower. In the validation cohort, the MAKIPS score showed an AUC of 0.798 (95% CI 0.788 -0.809). Calibration plots showed that at probability rates below 0.19, the score tended to overestimate, and at probability rates between 0.22 and 0.67, rates the score tended to underestimate the risk of HA-AKI. Conclusions The MAKIPS score can be a useful tool, easily obtainable from electronic records, to predict HA-AKI in hospitals with different case-mix characteristics.
Rhabdomyolysis is a major cause of acute kidney failure. The etiology is diverse, from full-blown crush syndrome to less frequent causes, such as metabolic myopathy. We describe the case of a 35-year-old male with a history of intermittent myalgias who was admitted to hospital with acute renal failure secondary to rhabdomyolysis. Moderate to intense diffuse uptake of technetium-99m was seen in soft tissues at scintigraphy. The diagnosis of metabolic myopathy was confirmed after careful workup and genetic testing.
No abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.