ArticleBMC nephrology2020
Changing relative risk of clinical factors for hospital-acquired acute kidney injury across age groups: a retrospective cohort study.
Article in BMC nephrology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Research Progress on Risk Factors for Acute Kidney Injury After Antibiotic-Loaded Bone Cement Spacer Treatment for Periprosthetic Joint Infection.Orthopaedic surgery · 2026Review
- Association between albumin-corrected anion gap level and the risk of acute kidney injury in intensive care unit.International urology and nephrology · 2024Article
- Interpretable machine learning-based individual analysis of acute kidney injury in immune checkpoint inhibitor therapy.PloS one · 2024Article
- Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit.Advances in chronic kidney disease · 2022Review
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Authors and funding
8 authors.
Funding
Abstract
backgroundLikelihood of developing acute kidney injury (AKI) increases with age. We aimed to explore whether the predictability of AKI varies between age groups and assess the volatility of risk factors using electronic medical records (EMR).
methodsWe constructed a retrospective cohort of adult patients from all inpatient units of a tertiary care academic hospital and stratified it into four age groups: 18-35, 36-55, 56-65, and > 65. Potential risk factors collected from EMR for the study cohort included demographics, vital signs, medications, laboratory values, past medical diagnoses, and admission diagnoses. AKI was defined based on the Kidney Disease Improving Global Outcomes (KDIGO) serum creatinine criteria. We analyzed relative importance of the risk factors in predicting AKI using Gradient Boosting Machine algorithm and explored the predictability of AKI across age groups using multiple machine learning models.
resultsIn our cohort, older patients showed a significantly higher incidence of AKI than younger adults: 18-35 (7.29%), 36-55 (8.82%), 56-65 (10.53%), and > 65 (10.55%) (p < 0.001). However, the predictability of AKI decreased with age, where the best cross-validated area under the receiver operating characteristic curve (AUROC) achieved for age groups 18-35, 36-55, 56-65, and > 65 were 0.784 (95% CI, 0.769-0.800), 0.766 (95% CI, 0.754-0.777), 0.754 (95% CI, 0.741-0.768), and 0.725 (95% CI, 0.709-0.737), respectively. We also observed that the relative risk of AKI predictors fluctuated between age groups.
conclusionsAs complexity of the cases increases with age, it is more difficult to quantify AKI risk for older adults in inpatient population.
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