Severe acute kidney disease is associated with worse kidney outcome among acute kidney injury patients

Chen, Yuwei; Wu, Mei‐Yi; Mao, Cheng-Hsien; Yeh, Yu-Ting; Chen, Tzu-Ting; Liao, Chia‐Te; Zheng, Cai Mei; Hsu, Yung‐Ho; Cherng, Yih-Giun; Wu, Mai‐Szu

doi:10.1038/s41598-022-09599-7

Cited by 6 publications

(2 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AKD is considered an important transition period in the AKI-to-CKD continuum (33). The severity of AKD has been reported to be significantly correlated to increased risk of adverse outcomes (14,15,22,(34)(35)(36). However, several previous studies have reported conflicting results (22,33,(37)(38)(39).…”

Section: Discussionmentioning

confidence: 99%

Risk factors and 180-day mortality of acute kidney disease in critically ill patients: A multi-institutional study

Pan

Chen

et al. 2023

Front. Med.

View full text Add to dashboard Cite

BackgroundCritically ill patients with acute kidney injury (AKI) have a poor prognosis. Recently, the Acute Disease Quality Initiative (ADQI) proposed to define acute kidney disease (AKD) as acute or subacute damage and/or loss of kidney function post AKI. We aimed to identify the risk factors for the occurrence of AKD and to determine the predictive value of AKD for 180-day mortality in critically ill patients.MethodsWe evaluated 11,045 AKI survivors and 5,178 AKD patients without AKI, who were admitted to the intensive care unit between 1 January 2001 and 31 May 2018, from the Chang Gung Research Database in Taiwan. The primary and secondary outcomes were the occurrence of AKD and 180-day mortality.ResultsThe incidence rate of AKD among AKI patients who did not receive dialysis or died within 90 days was 34.4% (3,797 of 11,045 patients). Multivariable logistic regression analysis indicated that AKI severity, underlying early CKD, chronic liver disease, malignancy, and use of emergency hemodialysis were independent risk factors of AKD, while male gender, higher lactate levels, use of ECMO, and admission to surgical ICU were negatively correlated with AKD. 180-day mortality was highest among AKD patients without AKI during hospitalization (4.4%, 227 of 5,178 patients), followed by AKI with AKD (2.3%, 88 of 3,797 patients) and AKI without AKD (1.6%, 115 of 7,133 patients). AKI with AKD had a borderline significantly increased risk of 180-day mortality (aOR 1.34, 95% CI 1.00–1.78; p = 0.047), while patients with AKD but no preceding AKI episodes had the highest risk (aOR 2.25, 95% CI 1.71–2.97; p < 0.001).ConclusionThe occurrence of AKD adds limited additional prognostic information for risk stratification of survivors among critically ill patients with AKI but could predict prognosis in survivors without prior AKI.

show abstract

Section: Discussionmentioning

confidence: 99%

Risk factors and 180-day mortality of acute kidney disease in critically ill patients: A multi-institutional study

Pan

Chen

et al. 2023

Front. Med.

View full text Add to dashboard Cite

show abstract

“…Nevertheless, an epidemiological study by Andonovic et al [ 10 ] found that patients with AKD in the ICU have a higher short-term risk of death but no statistically significant difference in long-term survival. Moreover, Chen et al [ 11 ] reported that patients with AKD are more likely to require long-term dialysis. Considering the high incidence and mortality rate of AKD, researchers have conducted exploratory early warning studies on AKD.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Predicting Risk and Prognosis of Acute Kidney Disease in Critically Ill Elderly Patients During Hospitalization: Internet-Based and Interpretable Model Study

Li,

Han,

Liang

et al. 2024

J Med Internet Res

View full text Add to dashboard Cite

Background Acute kidney disease (AKD) affects more than half of critically ill elderly patients with acute kidney injury (AKI), which leads to worse short-term outcomes. Objective We aimed to establish 2 machine learning models to predict the risk and prognosis of AKD in the elderly and to deploy the models as online apps. Methods Data on elderly patients with AKI (n=3542) and AKD (n=2661) from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were used to develop 2 models for predicting the AKD risk and in-hospital mortality, respectively. Data collected from Xiangya Hospital of Central South University were for external validation. A bootstrap method was used for internal validation to obtain relatively stable results. We extracted the indicators within 24 hours of the first diagnosis of AKI and the fluctuation range of some indicators, namely delta (day 3 after AKI minus day 1), as features. Six machine learning algorithms were used for modeling; the area under the receiver operating characteristic curve (AUROC), decision curve analysis, and calibration curve for evaluating; Shapley additive explanation (SHAP) analysis for visually interpreting; and the Heroku platform for deploying the best-performing models as web-based apps. Results For the model of predicting the risk of AKD in elderly patients with AKI during hospitalization, the Light Gradient Boosting Machine (LightGBM) showed the best overall performance in the training (AUROC=0.844, 95% CI 0.831-0.857), internal validation (AUROC=0.853, 95% CI 0.841-0.865), and external (AUROC=0.755, 95% CI 0.699–0.811) cohorts. In addition, LightGBM performed well for the AKD prognostic prediction in the training (AUROC=0.861, 95% CI 0.843-0.878), internal validation (AUROC=0.868, 95% CI 0.851-0.885), and external (AUROC=0.746, 95% CI 0.673-0.820) cohorts. The models deployed as online prediction apps allowed users to predict and provide feedback to submit new data for model iteration. In the importance ranking and correlation visualization of the model’s top 10 influencing factors conducted based on the SHAP value, partial dependence plots revealed the optimal cutoff of some interventionable indicators. The top 5 factors predicting the risk of AKD were creatinine on day 3, sepsis, delta blood urea nitrogen (BUN), diastolic blood pressure (DBP), and heart rate, while the top 5 factors determining in-hospital mortality were age, BUN on day 1, vasopressor use, BUN on day 3, and partial pressure of carbon dioxide (PaCO2). Conclusions We developed and validated 2 online apps for predicting the risk of AKD and its prognostic mortality in elderly patients, respectively. The top 10 factors that influenced the AKD risk and mortality during hospitalization were identified and explained visually, which might provide useful applications for intelligent management and suggestions for future prospective research.

show abstract

Subclinical and clinical acute kidney injury share similar urinary peptide signatures and prognosis

Boutin,

Latosinska,

Mischak

et al. 2023

Intensive Care Med

View full text Add to dashboard Cite

Severe acute kidney disease is associated with worse kidney outcome among acute kidney injury patients

Cited by 6 publications

References 43 publications

Risk factors and 180-day mortality of acute kidney disease in critically ill patients: A multi-institutional study

Risk factors and 180-day mortality of acute kidney disease in critically ill patients: A multi-institutional study

Machine Learning for Predicting Risk and Prognosis of Acute Kidney Disease in Critically Ill Elderly Patients During Hospitalization: Internet-Based and Interpretable Model Study

Subclinical and clinical acute kidney injury share similar urinary peptide signatures and prognosis

Contact Info

Product

Resources

About