Dataset Information

Development and validation of a model for predicting acute kidney injury after cardiac surgery in patients of advanced age.

ABSTRACT:

Objective

To develop a clinical model for predicting postoperative acute kidney injury (AKI) in patients of advanced age undergoing cardiac surgery.

Methods

A total of 848 patients (aged ≥ 60 years) undergoing cardiac surgery were consecutively enrolled. Among them, 597 were randomly selected for the development set and the remaining 251 for the validation set. AKI was the primary outcome. To develop a model for predicting AKI, visualized as a nomogram, we performed logistic regression with variables selected by Lasso regression analysis. The discrimination, calibration, and clinical usefulness of the new model were assessed and compared with those of Cleveland Clinic score and Simplified Renal Index (SRI) score in the validation set.

Results

The incidence of AKI was 61.8% in the development set. The new model included seven variables including preoperative serum creatinine, hypertension, preoperative uric acid, New York Heart Association classification ≥ 3, cardiopulmonary bypass time > 120 min, intraoperative red blood cell transfusion, and postoperative prolonged mechanical ventilation. In the validation set, the areas under the receiver operating characteristic curves for assessing discrimination of the new model, Cleveland Clinic score, and SRI score were 0.801, 0.670, and 0.627, respectively. Compared with the other two scores, the new model presented excellent calibration according to the calibration curves. Decision curve analysis presented the new model was more clinically useful than the other two scores.

Conclusions

We developed and validated a new model for predicting AKI after cardiac surgery in patients of advanced age, which may help clinicians assess patients' risk for AKI.

SUBMITTER: Hu P

PROVIDER: S-EPMC7898501 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Publications

Development and validation of a model for predicting acute kidney injury after cardiac surgery in patients of advanced age.

Hu Penghua P Chen Yuanhan Y Wu Yanhua Y Song Li L Zhang Li L Li Zhilian Z Fu Lei L Liu Shuangxin S Ye Zhiming Z Shi Wei W Liang Xinling X

Journal of cardiac surgery 20201212 3

<h4>Objective</h4>To develop a clinical model for predicting postoperative acute kidney injury (AKI) in patients of advanced age undergoing cardiac surgery.<h4>Methods</h4>A total of 848 patients (aged ≥ 60 years) undergoing cardiac surgery were consecutively enrolled. Among them, 597 were randomly selected for the development set and the remaining 251 for the validation set. AKI was the primary outcome. To develop a model for predicting AKI, visualized as a nomogram, we performed logistic regre ...[more]

PMID: 33314365

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Project description:BackgroundAcute kidney injury (AKI) often accompanies cardiac valve surgery, and worsens patient outcome. The aim of our study is to identify preoperative and intraoperative independent risk factors for AKI in patients undergoing cardiac valve surgery. Using these factors, we developed a risk prediction model for AKI after cardiac valve surgery and conducted external validation.MethodsOur retrospective study recruited 497 adult patients undergoing cardiac valve surgery as a derivation cohort between February and August 2023. Patient demographics, including medical history and perioperative clinical information, were acquired, and patients were classified into one of two cohorts, AKI and non-AKI, according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. Using binary logistic stepwise regression analysis, we identified independent AKI risk factors after cardiac valve surgery. Lastly, we constructed a nomogram and conducted external validation in a validation cohort comprising 200 patients. The performance of the nomogram was evaluated based on the area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCA).ResultsIn the derivation cohort, 172 developed AKI (34.6%). Relative to non-AKI patients, the AKI patients exhibited elevated postoperative complication incidences and worse outcome. Based on multivariate analysis, advanced age (OR: 1.855; p = 0.011), preoperative hypertension (OR: 1.91; p = 0.017), coronary heart disease (OR: 6.773; p < 0.001), preoperative albumin (OR: 0.924; p = 0.015), D-Dimer (OR: 1.001; p = 0.038), plasma creatinine (OR: 1.025; p = 0.001), cardiopulmonary bypass (CPB) duration (OR: 1.011; p = 0.001), repeat CPB (OR: 6.195; p = 0.010), intraoperative red blood cell transfusion (OR: 2.560; p < 0.001), urine volume (OR: 0.406 p < 0.001) and vasoactive-inotropic score (OR: 1.135; p = 0.009) were independent risk factors for AKI. The AUC of the nomogram in the derivation and validation cohorts were 0.814 (95%CI: 0.775-0.854) and 0.798 (95%CI: 0.726-0.871), respectively. Furthermore, the calibration curve revealed that the predicted outcome was in agreement with the actual observations. Finally, the DCA curves showed that the nomogram had a good clinical applicability value.ConclusionSeveral perioperative factors modulate AKI development following cardiac valve surgery, resulting in poor patient prognosis. The proposed AKI predictive model is both sensitive and precise, and can assist in high-risk patient screening in the clinics.

Project description:ObjectiveWe aimed to develop and validate a predictive machine learning (ML) model for cardiac surgery associated with acute kidney injury (CSA-AKI) based on a multicenter randomized control trial (RCT) and a Medical Information Mart for Intensive Care-IV (MIMIC-IV) dataset.MethodsThis was a subanalysis from a completed RCT approved by the Ethics Committee of Fuwai Hospital in Beijing, China (NCT03782350). Data from Fuwai Hospital were randomly assigned, with 80% for the training dataset and 20% for the testing dataset. The data from three other centers were used for the external validation dataset. Furthermore, the MIMIC-IV dataset was also utilized to validate the performance of the predictive model. The area under the receiver operating characteristic curve (ROC-AUC), the precision-recall curve (PR-AUC), and the calibration brier score were applied to evaluate the performance of the traditional logistic regression (LR) and eleven ML algorithms. Additionally, the Shapley Additive Explanations (SHAP) interpreter was used to explain the potential risk factors for CSA-AKI.ResultA total of 6495 eligible patients undergoing cardiopulmonary bypass (CPB) were eventually included in this study, 2416 of whom were from Fuwai Hospital (Beijing), for model development, 562 from three other cardiac centers in China, and 3517 from the MIMICIV dataset, were used, respectively, for external validation. The CatBoostClassifier algorithms outperformed other models, with excellent discrimination and calibration performance for the development, as well as the MIMIC-IV, datasets. In addition, the CatBoostClassifier achieved ROC-AUCs of 0.85, 0.67, and 0.77 and brier scores of 0.14, 0.19, and 0.16 in the testing, external, and MIMIC-IV datasets, respectively. Moreover, the utmost important risk factor, the N-terminal brain sodium peptide (NT-proBNP), was confirmed by the LASSO method in the feature section process. Notably, the SHAP explainer identified that the preoperative blood urea nitrogen level, prothrombin time, serum creatinine level, total bilirubin level, and age were positively correlated with CSA-AKI; preoperative platelets level, systolic and diastolic blood pressure, albumin level, and body weight were negatively associated with CSA-AKI.ConclusionsThe CatBoostClassifier algorithms outperformed other ML models in the discrimination and calibration of CSA-AKI prediction cardiac surgery with CPB, based on a multicenter RCT and MIMIC-IV dataset. Moreover, the preoperative NT-proBNP level was confirmed to be strongly related to CSA-AKI.

Dataset Information

Development and validation of a model for predicting acute kidney injury after cardiac surgery in patients of advanced age.

Objective

Methods

Results

Conclusions

Publications

Development and validation of a model for predicting acute kidney injury after cardiac surgery in patients of advanced age.

Similar Datasets

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