Novel risk factors for acute kidney injury

Varrier, Matt; Ostermann, Marlies

doi:10.1097/mnh.0000000000000061

Cited by 29 publications

(18 citation statements)

References 66 publications

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“…Obesity is generally accepted as a risk factor for the development of AKI;60 however, one study found that increased BMI is associated with decreased mortality from AKI in elderly surgical patients 61. The inverse relationship of obesity to AKI in our results is puzzling, though potentially explained by the fact that the addition of occupational characteristics attenuated the relationship of BMI to AKI.…”

Section: Discussioncontrasting

confidence: 66%

Heat strain, volume depletion and kidney function in California agricultural workers

et al. 2017

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Background Agricultural work can expose workers to increased risk of heat strain and volume depletion due to repeated exposures to high ambient temperatures, arduous physical exertion and limited rehydration. These risk factors may result in acute kidney injury (AKI). Methods We estimated AKI cumulative incidence in a convenience sample of 283 agricultural workers based on elevations of serum creatinine between preshift and postshift blood samples. Heat strain was assessed based on changes in core body temperature and heart rate. Volume depletion was assessed using changes in body mass over the work shift. Logistic regression models were used to estimate the associations of AKI with traditional risk factors (age, diabetes, hypertension and history of kidney disease) as well as with occupational risk factors (years in farm work, method of payment and farm task). Results 35 participants were characterised with incident AKI over the course of a work shift (12.3%). Workers who experienced heat strain had increased adjusted odds of AKI (1.34, 95% CI 1.04 to 1.74). Piece rate work was associated with 4.24 odds of AKI (95% CI 1.56 to 11.52). Females paid by the piece had 102.81 adjusted odds of AKI (95% CI 7.32 to 1443.20). Discussion Heat strain and piece rate work are associated with incident AKI after a single shift of agricultural work, though gender differences exist. Modifications to payment structures may help prevent AKI.

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Section: Discussioncontrasting

confidence: 66%

Heat strain, volume depletion and kidney function in California agricultural workers

et al. 2017

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“…In recent years, there have been several reports regarding novel and previously unknown risk factors for AKI, such as hyperuricemia 19 , hypoalbuminemia 20 , obesity 21 , obstructive sleep apnea 22 , hypochloremia and hyperchloremia 23 . In our study, BMI and obesity (COM2) were both selected in the top ten by several FS methods as shown in Table 4.…”

Section: Resultsmentioning

confidence: 99%

Feature Ranking in Predictive Models for Hospital-Acquired Acute Kidney Injury

Liu

et al. 2018

Sci Rep

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Acute Kidney Injury (AKI) is a common complication encountered among hospitalized patients, imposing significantly increased cost, morbidity, and mortality. Early prediction of AKI has profound clinical implications because currently no treatment exists for AKI once it develops. Feature selection (FS) is an essential process for building accurate and interpretable prediction models, but to our best knowledge no study has investigated the robustness and applicability of such selection process for AKI. In this study, we compared eight widely-applied FS methods for AKI prediction using nine-years of electronic medical records (EMR) and examined heterogeneity in feature rankings produced by the methods. FS methods were compared in terms of stability with respect to data sampling variation, similarity between selection results, and AKI prediction performance. Prediction accuracy did not intrinsically guarantee the feature ranking stability. Across different FS methods, the prediction performance did not change significantly, while the importance rankings of features were quite different. A positive correlation was observed between the complexity of suitable FS method and sample size. This study provides several practical implications, including recognizing the importance of feature stability as it is desirable for model reproducibility, identifying important AKI risk factors for further investigation, and facilitating early prediction of AKI.

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“…Most studies have consistently reported that the risk factors associated with a poor AKI prognosis include advancing age, cardiovascular disease, AKI stage, and so on [21]. In addition to examining these basic risk factors, we also systemically and comprehensively collected the laboratory indicators of patients with AKI, and examined the APACHE II and SOFA scores of these patients to better predict the mortality risk of AKI patients.…”

Section: Discussionmentioning

confidence: 99%

“…High SUA concentrations are significantly correlated with a decline in residual kidney function after adjusting for confounding factors [36]. Uric acid could have pro-inflammatory effects, increase oxidative stress, and damage the microcirculation [21], thereby increasing mortality risk and explaining why a high SUA level can serve as a marker of poor prognosis. Nevertheless, as high SUA might be a symptom of AKI, it is unclear whether the poor prognosis is due to uric acid itself or to AKI.…”

Section: Discussionmentioning

confidence: 99%

Risk Factor Analysis for AKI Including Laboratory Indicators: a Nationwide Multicenter Study of Hospitalized Patients

Nie

Feng

et al. 2017

Kidney Blood Press Res

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Background/Aims: Risk factor studies for acute kidney injury (AKI) in China are lacking, especially those regarding non-traditional risk factors, such as laboratory indicators. Methods: All adult patients admitted to 38 tertiary and 22 secondary hospitals in China in any one month between July and December 2014 were surveyed. AKI patients were screened according to the Kidney Disease: Improving Global Outcomes’ definition of AKI. Logistic regression was used to analyze the risk factors for AKI, and Cox regression was used to analyze the risk of in-hospital mortality for AKI patients; additionally, a propensity score analysis was used to reconfirm the risk factors among laboratory indicators for mortality. Results: The morbidity of AKI was 0.97%. Independent risk factors for AKI were advancing age, male gender, hypertension, and chronic kidney disease. All-cause mortality was 16.5%. The predictors of mortality in AKI patients were advancing age, tumor, higher uric acid level and increases in Acute Physiologic Assessment and Chronic Health Evaluation II and Sequential Organ Failure Assessment scores. The hazard ratio (HR) for mortality with uric acid levels > 9.1 mg/dl compared with ≤ 5.2 mg/dl was 1.78 (95% CI: 1.23 to 2.58) for the AKI patients as a group, and was 1.73 (95% CI: 1.24 to 2.42) for a propensity score-matched set. Conclusion: In addition to traditional risk factors, uric acid level is an independent predictor of all-cause mortality after AKI.

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Novel risk factors for acute kidney injury

Abstract: Novel risk factors for AKI have been identified but more work is necessary to investigate the nature of the association. There is no evidence that correction of hyperuricaemia or hypoalbuminaemia is beneficial but high-risk exposures should be avoided in patients at risk of AKI.

Cited by 29 publications

References 66 publications

Heat strain, volume depletion and kidney function in California agricultural workers

Heat strain, volume depletion and kidney function in California agricultural workers

Feature Ranking in Predictive Models for Hospital-Acquired Acute Kidney Injury

Risk Factor Analysis for AKI Including Laboratory Indicators: a Nationwide Multicenter Study of Hospitalized Patients

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