Derivation and External Validation of Prediction Models for Advanced Chronic Kidney Disease Following Acute Kidney Injury

James, Matthew T.; Pannu, Neesh; Hemmelgarn, Brenda R.; Austin, Peter C.; Tan, Zhi; McArthur, Eric; Manns, Braden; Tonelli, Marcello; Wald, Ron; Quinn, Robert R.; Ravani, Pietro; Garg, Amit X.

doi:10.1001/jama.2017.16326

Cited by 123 publications

(112 citation statements)

References 37 publications

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“…The model 2 was derived from 60% of the AKD cohort and prospectively validated by the remaining 40%. review of the literature 14,24,[31][32][33] . All the candidate variables in the derivation cohort were included as potential covariates in multivariable logistic regression models.…”

Section: Identification and Classification Of Akdmentioning

confidence: 99%

Novel risk models to predict acute kidney disease and its outcomes in a Chinese hospitalized population with acute kidney injury

Xiao

Cheng

et al. 2020

Sci Rep

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Acute kidney disease (AKD) is a state between acute kidney injury (AKI) and chronic kidney disease (CKD), but the prognosis of AKD is unclear and there are no risk-prediction tools to identify high-risk patients. 2,556 AKI patients were selected from 277,898 inpatients of three affiliated hospitals of Central South University from January 2015 to December 2015. The primary point was whether AKI patients developed AKD. The endpoint was death or end stage renal disease (ESRD) 90 days after AKI diagnosis. Multivariable Cox regression was used for 90-day mortality and two prediction models were established by using multivariable logistic regression. Our study found that the incidence of AKD was 53.17% (1,359/2,556), while the mortality rate and incidence of ESRD in AKD cohort was 19.13% (260/1,359) and 3.02% (41/1,359), respectively. Furthermore, adjusted hazard ratio of mortality for AKD versus no AKD was 1.980 (95% CI 1.427–2.747). In scoring model 1, age, gender, hepatorenal syndromes, organic kidney diseases, oliguria or anuria, respiratory failure, blood urea nitrogen (BUN) and acute kidney injury stage were independently associated with AKI progression into AKD. In addition, oliguria or anuria, respiratory failure, shock, central nervous system failure, malignancy, RDW-CV ≥ 13.7% were independent risk factors for death or ESRD in AKD patients in scoring model 2 (goodness-of fit, P1 = 0.930, P2 = 0.105; AUROC1 = 0.879 (95% CI 0.862–0.896), AUROC2 = 0.845 (95% CI 0.813–0.877), respectively). Thus, our study demonstrated AKD was independently associated with increased 90-day mortality in hospitalized AKI patients. A new prediction model system was able to predict AKD following AKI and 90-day prognosis of AKD patients to identify high-risk patients.

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Section: Identification and Classification Of Akdmentioning

confidence: 99%

Novel risk models to predict acute kidney disease and its outcomes in a Chinese hospitalized population with acute kidney injury

Xiao

Cheng

et al. 2020

Sci Rep

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show abstract

“…This model using routine laboratory data was able to predict advanced chronic kidney disease following hospitalization with acute kidney injury. But requires evaluation of its utility in a clinical setting [97].…”

Section: Ckd After Akimentioning

confidence: 99%

Role of Artificial Intelligence in Kidney Disease

Yuan¹,

Zhang²,

Deng³

et al. 2020

Int. J. Med. Sci.

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Artificial intelligence (AI), as an advanced science technology, has been widely used in medical fields to promote medical development, mainly applied to early detections, disease diagnoses, and management. Owing to the huge number of patients, kidney disease remains a global health problem. Challenges remain in its diagnosis and treatment. AI could take individual conditions into account, produce suitable decisions and promise to make great strides in kidney disease management. Here, we review the current studies of AI applications in kidney disease in alerting systems, diagnostic assistance, guiding treatment and evaluating prognosis. Although the number of studies related to AI applications in kidney disease is small, the potential of AI in the management of kidney disease is well recognized by clinicians; AI will greatly enhance clinicians' capacity in their clinical practice in the future.

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“…130 In CKD, epidemiology methods have already enabled a more personalized approach to clinical decision making: there are well-validated tools for estimating the risk of ESKD, mortality, and cardiovascular disease developed in many different patient populations, which may be applied to the individual patient for risk stratification and management (Table 2). 13,[131][132][133][134][135][136][137] That said, there are >100 distinct causes of CKD. Different disease etiologies manifest in different ways with different levels of risk for poor outcomes and different recommended treatment.…”

Section: Epidemiology Studies To Enhance the Conduct Of Clinical Trialsmentioning

confidence: 99%

Epidemiology research to foster improvement in chronic kidney disease care

Luo

Grams

2020

Kidney International

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Derivation and External Validation of Prediction Models for Advanced Chronic Kidney Disease Following Acute Kidney Injury

Abstract: 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.

Cited by 123 publications

References 37 publications

Novel risk models to predict acute kidney disease and its outcomes in a Chinese hospitalized population with acute kidney injury

Novel risk models to predict acute kidney disease and its outcomes in a Chinese hospitalized population with acute kidney injury

Role of Artificial Intelligence in Kidney Disease

Epidemiology research to foster improvement in chronic kidney disease care

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