Correction: The ICE-AKI study: Impact analysis of a Clinical prediction rule and Electronic AKI alert in general medical patients

Hodgson, Luke; Roderick, Paul; Venn, Richard; Yao, Guiqing; Dimitrov, Borislav D.; Forni, Lui G.

doi:10.1371/journal.pone.0203183

Cited by 7 publications

(2 citation statements)

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“…One of the most significant barriers to delivering these recommended clinical actions is that most AKI resolves spontaneously or with initial clinical management, and it is quite difficult to know which patients will require discontinuation of nephrotoxic drugs or a more intensive assessment of hemodynamics/ volume status. Although biomarkers of early AKI and electronic alerts/care bundles have shown promise (25,26), unfortunately, not all trials have provided improved outcomes (24,27,28). It is our hypothesis that a tool such as CCL14 that can better predict persistent severe AKI can serve as a tool to determine which patients need additional kidney-focused care beyond early management because it stands to reason that those destined for persistent severe AKI are most likely to benefit from strict adherence to kidney care bundles.…”

Section: Discussionmentioning

confidence: 99%

Performance of a Standardized Clinical Assay for Urinary C–C Motif Chemokine Ligand 14 (CCL14) for Persistent Severe Acute Kidney Injury

et al. 2022

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Background: Clinical use of biomarkers requires the development of standardized assays and establishment of cutoffs. Urinary C-C-motif chemokine ligand 14 (CCL14) has been validated to predict persistent severe AKI in critically ill patients with established AKI. We now report on the performance of standardized cutoffs using a clinical assay. Methods: A second aim of the multicenter Ruby study was to establish two cutoffs for the prediction of persistent severe AKI (defined as KDIGO stage 3 AKI for at least 72 consecutive hours). Patients who received kidney replacement therapy (KRT) or died prior to achieving 72 hours in stage 3 AKI were also considered to have reached the endpoint. Results: A cutoff value for urinary CCL14 of 1.3 ng/ml was determined to achieve high sensitivity (91%, (95%CI 84%-96%)) and 13 ng/ml achieved high specificity (93% (89%-96%)). The cutoff of 1.3 ng/mL identifies the majority (91%) of patients who developed persistent severe AKI with a negative predictive value of 92%. The cutoff at 13 ng/ml had a positive predictive value of 72% (with a negative predictive value of 75%). In multivariable adjusted analyses, a CCL14 concentration between 1.3 and 13 ng/mL had an adjusted odds ratio (aOR) of 3.82 (95%CI 1.73-9.12) p=0.001 for the development of persistent severe AKI compared to those with CCL14 ≤ 1.3, while a CCL14 > 13 ng/ml had an aOR of 10.4 (3.89-29.9); p<0.001. Conclusions: Using a clinical assay, these standardized cutoffs (1.3 and 13 ng/mL) allow for the identification of patients at high risk for the development of severe persistent AKI. These results have immediate utility in helping to guide AKI patient care and may facilitate future clinical trials.

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Section: Discussionmentioning

confidence: 99%

Performance of a Standardized Clinical Assay for Urinary C–C Motif Chemokine Ligand 14 (CCL14) for Persistent Severe Acute Kidney Injury

et al. 2022

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“…Therefore, the ability to predict an AKI before surgery is crucial for efficiently allocating hospital resources to at-risk patients and directing medical resources to high-risk patients, ultimately leading to improved patient outcomes [ 8 , 9 , 22 ]. Previous studies have explored the use of ML algorithms to predict the occurrence of postoperative AKI [ 8 , 9 , 11 , 23 , 24 , 25 , 26 , 27 ]. Lei et al aimed to predict the risk of postoperative AKI in non-cardiac surgery by utilizing preoperative and intraoperative blood pressure data [ 9 ].…”

Section: Discussionmentioning

confidence: 99%

Internal and External Validation of Machine Learning Models for Predicting Acute Kidney Injury Following Non-Cardiac Surgery Using Open Datasets

Lee,

Jang,

Seo

et al. 2024

JPM

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This study developed and validated a machine learning model to accurately predict acute kidney injury (AKI) after non-cardiac surgery, aiming to improve patient outcomes by assessing its clinical feasibility and generalizability. We conducted a retrospective cohort study using data from 76,032 adults who underwent non-cardiac surgery at a single tertiary medical center between March 2019 and February 2021, and used data from 5512 patients from the VitalDB open dataset for external model validation. The predictive variables for model training consisted of demographic, preoperative laboratory, and intraoperative data, including calculated statistical values such as the minimum, maximum, and mean intraoperative blood pressure. When predicting postoperative AKI, our gradient boosting machine model incorporating all the variables achieved the best results, with AUROC values of 0.868 and 0.757 for the internal and external validations using the VitalDB dataset, respectively. The model using intraoperative data performed best in internal validation, while the model with preoperative data excelled in external validation. In this study, we developed a predictive model for postoperative AKI in adult patients undergoing non-cardiac surgery using preoperative and intraoperative data, and external validation demonstrated the efficacy of open datasets for generalization in medical artificial modeling research.

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Precision Delivery in Critical Care: Balancing Prediction and Personalization

Liu

Prescott

2019

Annual Update in Intensive Care and Emergency Medicine 2019

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Correction: The ICE-AKI study: Impact analysis of a Clinical prediction rule and Electronic AKI alert in general medical patients

Cited by 7 publications

References 1 publication

Performance of a Standardized Clinical Assay for Urinary C–C Motif Chemokine Ligand 14 (CCL14) for Persistent Severe Acute Kidney Injury

Performance of a Standardized Clinical Assay for Urinary C–C Motif Chemokine Ligand 14 (CCL14) for Persistent Severe Acute Kidney Injury

Internal and External Validation of Machine Learning Models for Predicting Acute Kidney Injury Following Non-Cardiac Surgery Using Open Datasets

Precision Delivery in Critical Care: Balancing Prediction and Personalization

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