Dysregulated Mineral Metabolism in AKI

Leaf, David E.; Christov, Marta

doi:10.1016/j.semnephrol.2018.10.004

Cited by 51 publications

(51 citation statements)

References 178 publications

(195 reference statements)

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“…Hyperphosphatemia is a common complication of RM with several proposed pathogenic factors, including the release of inorganic phosphorus into the plasma and reduced urinary phosphate excretion [26,27]. In the present study, increased serum phosphate was determined to be an independent predictor for AKI-associated RM, which is in accordance with previously reported findings [28,29].…”

Section: Discussionsupporting

confidence: 92%

Risk factors for acute kidney injury among patients with rhabdomyolysis

Yang

Zhou

Wang

et al. 2020

Preprint

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Background Acute kidney injury (AKI) is a life-threatening complication of rhabdomyolysis (RM). The aim of the present study was to assess patients at high risk for the occurrence of AKI defined by the Kidney Disease Improving Global Outcomes criteria and in-hospital mortality. Methods We performed a retrospective study of patients with creatine kinase levels >1000 U/L, who were admitted to the West China Hospital of Sichuan University between January 2011 and March 2019. The sociodemographic, clinical and laboratory data of these patients were obtained from an electronic medical records database, and univariate and multivariate regression analyses were subsequently conducted. Results For the 329 patients included in our study, the incidence of AKI was 61.4%, and the overall mortality rate was 19.8%; furthermore, patients with AKI tended to have higher mortality rates than those without AKI (24.8% vs. 11.8%; P<0.01). The clinical conditions most frequently associated with RM were trauma (28.3%), sepsis (14.6%), bee sting (12.8%), thoracic and abdominal surgery (11.2%) and exercise (7.0%). Furthermore, patients with RM resulting from sepsis, bee sting and acute alcoholism were more susceptible to AKI. The risk factors for the occurrence of AKI among RM patients included age ≥60 years (OR=3.070), chronic alcoholism (OR=3.256), hypertension (OR=4.252), multiple organ dysfunction syndrome (MODS; OR=7.244), high levels of white blood cell count (OR=1.047) and elevated serum phosphorus (OR=5.526). Age ≥60 years (OR=3.188), MODS (OR=2.262), diabetes (OR=2.746) and elevated prothrombin time (OR=1.079) were independent risk factors for in-hospital mortality in RM patients with AKI. Conclusions AKI is independently associated with mortality in patients with RM, and several risk factors were found to be associated with the occurrence of AKI and in-hospital mortality. These findings suggest that, to improve the quality of medical care, the early prevention of AKI should focus on high-risk patients and more effective management.

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Section: Discussionsupporting

confidence: 92%

Risk factors for acute kidney injury among patients with rhabdomyolysis

Yang

Zhou

Wang

et al. 2020

Preprint

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“…Hyperphosphatemia is a common complication of RM with several proposed pathogenic factors, including the release of inorganic phosphorus into the plasma and reduced urinary phosphate excretion [ 32 , 33 ]. In the present study, increased serum phosphate was determined to be an independent predictor for AKI-associated RM, which is in accordance with previously reported findings [ 11 , 34 , 35 ].…”

Section: Discussionmentioning

confidence: 99%

Risk factors for severe acute kidney injury among patients with rhabdomyolysis

et al. 2020

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Background Acute kidney injury (AKI) is a life-threatening complication of rhabdomyolysis (RM). The aim of the present study was to assess patients at high risk for the occurrence of severe AKI defined as stage II or III of KDIGO classification and in-hospital mortality of AKI following RM. Methods We performed a retrospective study of patients with creatine kinase levels > 1000 U/L, who were admitted to the West China Hospital of Sichuan University between January 2011 and March 2019. The sociodemographic, clinical and laboratory data of these patients were obtained from an electronic medical records database, and univariate and multivariate regression analyses were subsequently conducted. Results For the 329 patients included in our study, the incidence of AKI was 61.4% and the proportion of stage I, stage II, stage III were 18.8, 14.9 and 66.3%, respectively. The overall mortality rate was 19.8%; furthermore, patients with AKI tended to have higher mortality rates than those without AKI (24.8% vs. 11.8%; P < 0.01). The clinical conditions most frequently associated with RM were trauma (28.3%), sepsis (14.6%), bee sting (12.8%), thoracic and abdominal surgery (11.2%) and exercise (7.0%). Furthermore, patients with RM resulting from sepsis, bee sting and acute alcoholism were more susceptible to severe AKI. The risk factors for the occurrence of stage II-III AKI among RM patients included hypertension (OR = 2.702), high levels of white blood cell count (OR = 1.054), increased triglycerides (OR = 1.260), low level of high-density lipoprotein cholesterol (OR = 0.318), elevated serum phosphorus (OR = 5.727), 5000<CK ≤ 10,000 U/L (OR = 2.617) and CK>10,000 U/L (OR = 8.093). Age ≥ 60 years (OR = 2.946), sepsis (OR = 3.206) and elevated prothrombin time (OR = 1.079) were independent risk factors for in-hospital mortality in RM patients with AKI. Conclusions AKI is independently associated with mortality in patients with RM, and several risk factors were found to be associated with the occurrence of severe AKI and in-hospital mortality. These findings suggest that, to improve the quality of medical care, the early prevention of AKI should focus on high-risk patients and more effective management.

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“…Similar to previous AKI investigations, underlying comorbidities (diabetes mellitus, severe liver diseases, chronic kidney disease) [ 28 , 29 ] and recent use of RAS inhibitors or potassium-sparing diuretics [ 19 ] were identified in the present CA-AKI hospitalization risk score. The risk model also indicated that low calcium levels and high phosphorus levels were potentially modifiable predictors and could be targeted for correction [ 30 ]. This feature has substantial clinical implications because it demonstrates that the model can be applied to prospectively support clinical decision systems in real time for rapid screening and recognition of patients with a predicted risk of CA-AKI in outpatient settings.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, patients with CA-AKI in the final cohort were older (65.26, SD 15.49 years) than those in the non-CA-AKI group; moreover, 54.4% of the patients were women and 50.67% had preexisting chronic kidney disease (eGFR<60 ml/min/1.73m 2 at baseline). In comparison, a study with a British population reported a mean age of 74.4 (SD 15.4) years, 50%-52% female patients, and 31.9%-34.6% of patients with preexisting chronic kidney disease [ 4 , 30 ], whereas another study with a US population reported a mean age of 67.8 (SD 12.2) years and 43.2% of patients with preexisting chronic kidney disease [ 3 ]; these populations were considered to be comparable with the CA-AKI patients identified using KDIGO SCr-based criteria in different populations. The present study used large-scale clinical data with a representative and adequate sample size to develop a diagnostic tool for CA-AKI risk evaluation.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Model for Risk Prediction of Community-Acquired Acute Kidney Injury Hospitalization From Electronic Health Records: Development and Validation Study

et al. 2020

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Background Community-acquired acute kidney injury (CA-AKI)-associated hospitalizations impose significant health care needs and contribute to in-hospital mortality. However, most risk prediction models developed to date have focused on AKI in a specific group of patients during hospitalization, and there is limited knowledge on the baseline risk in the general population for preventing CA-AKI-associated hospitalization. Objective To gain further insight into risk exploration, the aim of this study was to develop, validate, and establish a scoring system to facilitate health professionals in enabling early recognition and intervention of CA-AKI to prevent permanent kidney damage using different machine-learning techniques. Methods A nested case-control study design was employed using electronic health records derived from a group of Chang Gung Memorial Hospitals in Taiwan from 2010 to 2017 to identify 234,867 adults with at least two measures of serum creatinine at hospital admission. Patients were classified into a derivation cohort (2010-2016) and a temporal validation cohort (2017). Patients with the first episode of CA-AKI at hospital admission were classified into the case group and those without CA-AKI were classified in the control group. A total of 47 potential candidate variables, including age, gender, prior use of nephrotoxic medications, Charlson comorbid conditions, commonly measured laboratory results, and recent use of health services, were tested to develop a CA-AKI hospitalization risk model. Permutation-based selection with both the extreme gradient boost (XGBoost) and least absolute shrinkage and selection operator (LASSO) algorithms was performed to determine the top 10 important features for scoring function development. Results The discriminative ability of the risk model was assessed by the area under the receiver operating characteristic curve (AUC), and the predictive CA-AKI risk model derived by the logistic regression algorithm achieved an AUC of 0.767 (95% CI 0.764-0.770) on derivation and 0.761 on validation for any stage of AKI, with positive and negative predictive values of 19.2% and 96.1%, respectively. The risk model for prediction of CA-AKI stages 2 and 3 had an AUC value of 0.818 for the validation cohort with positive and negative predictive values of 13.3% and 98.4%, respectively. These metrics were evaluated at a cut-off value of 7.993, which was determined as the threshold to discriminate the risk of AKI. Conclusions A machine learning–generated risk score model can identify patients at risk of developing CA-AKI-related hospitalization through a routine care data-driven approach. The validated multivariate risk assessment tool could help clinicians to stratify patients in primary care, and to provide monitoring and early intervention for preventing AKI while improving the quality of AKI care in the general population.

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Dysregulated Mineral Metabolism in AKI

Cited by 51 publications

References 178 publications

Risk factors for acute kidney injury among patients with rhabdomyolysis

Risk factors for acute kidney injury among patients with rhabdomyolysis

Risk factors for severe acute kidney injury among patients with rhabdomyolysis

Machine Learning Model for Risk Prediction of Community-Acquired Acute Kidney Injury Hospitalization From Electronic Health Records: Development and Validation Study

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