Machine learning prediction model of acute kidney injury after percutaneous coronary intervention

Kuno, Toshiki; Mikami, Takahisa; Sahashi, Yuki; Numasawa, Yohei; Suzuki, Masahiro; Noma, Shigetaka; Fukuda, Keiichi; Kohsaka, Shun

doi:10.1038/s41598-021-04372-8

Cited by 14 publications

(9 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nowadays, numerous studies have constructed prediction model for AKI in patients undergoing various conditions, and the model were constructed based on perioperative laboratory test and history of disease status [22][23][24][25][26][27]. However, the prediction model for AKI were not contained the parameters from ultrasound.…”

Section: Discussionmentioning

confidence: 99%

Performance of resistive index and semi-quantitative power doppler ultrasound score in predicting acute kidney injury: A meta-analysis of prospective studies

et al. 2022

View full text Add to dashboard Cite

This study aimed to assess the predictive value of the renal resistive index (RRI) and power Doppler ultrasound (PDU) on subsequent acute kidney injury (AKI) risk using a meta-analytic approach. We searched eligible studies in PubMed, EmBase, and the Cochrane library from inception until August 2021. The parameters included the sensitivity, specificity, positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), and area under the receiver operating characteristic curves (AUC). Twenty-three prospective studies involving 2,400 patients were selected. The pooled sensitivity and specificity of the RRI and PDU were 0.76 and 0.79, and 0.64 and 0.90, respectively. The pooled PLR and NLR were 3.64 and 0.31, and 6.58 and 0.40 for the RRI and PDU, respectively. The DORs of the RRI and PDU for predicting AKI were 11.76, and 16.32, respectively. The AUCs of the RRI and PDU for predicting AKI were 0.83, and 0.86, respectively. There were no significant differences between the RRI and PDU for predicting AKI in terms of sensitivity, PLR, NLR, DOR, and AUC. The specificity of the RRI was lower than that of the PDU for predicting AKI. This study found that the predictive performance of the RRI and PDU from the Doppler ultrasound for AKI was similar, which need to be further verified based on the direct comparison results.

show abstract

Section: Discussionmentioning

confidence: 99%

Performance of resistive index and semi-quantitative power doppler ultrasound score in predicting acute kidney injury: A meta-analysis of prospective studies

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Through this purpose, ML models are being developed rapidly regarding risk stratification of AKI for further safety considerations before, during, and after PCI. The computational discipline of ML-based methods allows the algorithm formulation into models capable of recognizing complex patterns or interactions when utilizing extensive data [ 8 , 21 ]. By incorporating ML into the model development process, there is the potential to enhance the accuracy of the AKI risk stratification [ 6 , 22 ].…”

Section: Discussionmentioning

confidence: 99%

“…Prediction models, such as the NCDR-AKI risk model, have been developed to assess the risk of CI-AKI prior to performing PCI with a c-statistics of 0.71 [ 7 ]. Traditional statistical models may not include all possible interactions when there are numerous candidate variables, resulting in a decrease in the model’s accuracy when these interactions are ignored [ 1 , 8 ]. Machine Learning (ML)-based models do not depend on assumptions about the variables involved or their relationship with the outcome.…”

Section: Introductionmentioning

confidence: 99%

Predictive modeling for acute kidney injury after percutaneous coronary intervention in patients with acute coronary syndrome: a machine learning approach

Behnoush,

Shariatnia,

Khalaji

et al. 2024

Eur J Med Res

View full text Add to dashboard Cite

Background Acute kidney injury (AKI) is one of the preventable complications of percutaneous coronary intervention (PCI). This study aimed to develop machine learning (ML) models to predict AKI after PCI in patients with acute coronary syndrome (ACS). Methods This study was conducted at Tehran Heart Center from 2015 to 2020. Several variables were used to design five ML models: Naïve Bayes (NB), Logistic Regression (LR), CatBoost (CB), Multi-layer Perception (MLP), and Random Forest (RF). Feature importance was evaluated with the RF model, CB model, and LR coefficients while SHAP beeswarm plots based on the CB model were also used for deriving the importance of variables in the population using pre-procedural variables and all variables. Sensitivity, specificity, and the area under the receiver operating characteristics curve (ROC-AUC) were used as the evaluation measures. Results A total of 4592 patients were included, and 646 (14.1%) experienced AKI. The train data consisted of 3672 and the test data included 920 cases. The patient population had a mean age of 65.6 ± 11.2 years and 73.1% male predominance. Notably, left ventricular ejection fraction (LVEF) and fasting plasma glucose (FPG) had the highest feature importance when training the RF model on only pre-procedural features. SHAP plots for all features demonstrated LVEF and age as the top features. With pre-procedural variables only, CB had the highest AUC for the prediction of AKI (AUC 0.755, 95% CI 0.713 to 0.797), while RF had the highest sensitivity (75.9%) and MLP had the highest specificity (64.35%). However, when considering pre-procedural, procedural, and post-procedural features, RF outperformed other models (AUC: 0.775). In this analysis, CB achieved the highest sensitivity (82.95%) and NB had the highest specificity (82.93%). Conclusion Our analyses showed that ML models can predict AKI with acceptable performance. This has potential clinical utility for assessing the individualized risk of AKI in ACS patients undergoing PCI. Additionally, the identified features in the models may aid in mitigating these risk factors. Graphical Abstract

show abstract

“…It primarily includes large tertiary care referral centers (≥ 200 beds; n = 13) and a few medium-sized satellite hospitals (< 200 beds; n = 2). The details of this registry have been published previously 2 , 9 – 13 . The participating hospitals were instructed to document and register patient data of consecutive hospital visits for PCI using an internet-based data collection system.…”

Section: Methodsmentioning

confidence: 99%

Contrast volume and in-hospital outcomes of dialysis patients undergoing percutaneous coronary intervention

Kuno

Numasawa

Shoji

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Toxicity resulting from retained contrast media may cause adverse cardiovascular outcomes (e.g., heart failure and cardiogenic shock) for dialysis patients. However, the association between the administered contrast volume and outcomes of dialysis patients after percutaneous coronary intervention (PCI) has not been sufficiently investigated. We evaluated 953 consecutive dialysis patients (age, 67.9 ± 9.9 years; 30.1% with acute coronary syndrome) who underwent PCI between September 2008 and March 2019. Patients were divided into two groups: those with a contrast volume ≥ 200 ml and those with a contrast volume < 200 ml. The cutoff was 200 ml because 100 ml increment of contrast volume is known to raise the risk of acute kidney injury, and 200 ml is more than the average volume used at most PCI centers. The primary endpoint was a composite of in-hospital death, post-PCI cardiogenic shock and post-PCI heart failure. A multivariable logistic regression model and smooth spline curve were constructed to assess the association between contrast volume and the primary endpoint. The median contrast volume was 157 ml (interquartile range, 115–210 ml). The overall primary endpoint incidence was 6.8% (N = 65). A contrast volume ≥ 200 ml was associated with a higher risk of the primary endpoint (odds ratio 2.91; 95% confidence interval 1.42–6.05; P = 0.004). The smooth spline curve demonstrated a linear relationship between the contrast volume and primary endpoint. In conclusions, the contrast volume was associated with adverse in-hospital outcomes of dialysis patients undergoing PCI. Attention should be focused on the contrast volume used for dialysis patients undergoing PCI.

show abstract

Machine learning prediction model of acute kidney injury after percutaneous coronary intervention

Cited by 14 publications

References 45 publications

Performance of resistive index and semi-quantitative power doppler ultrasound score in predicting acute kidney injury: A meta-analysis of prospective studies

Performance of resistive index and semi-quantitative power doppler ultrasound score in predicting acute kidney injury: A meta-analysis of prospective studies

Predictive modeling for acute kidney injury after percutaneous coronary intervention in patients with acute coronary syndrome: a machine learning approach

Contrast volume and in-hospital outcomes of dialysis patients undergoing percutaneous coronary intervention

Contact Info

Product

Resources

About