Predicting the risk of acute kidney injury after cardiopulmonary bypass: development and assessment of a new predictive nomogram

Jing, Huan; Liao, Mengyang; Tang, Simin; Lin, Sen; Li, Ye; Zhong, Jiying; Wang, Hanbin; Zhou, Jun

doi:10.1186/s12871-022-01925-w

Cited by 4 publications

(1 citation statement)

References 54 publications

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“…Age emerged as a significant risk factor for postoperative AKI following CPB surgery, especially in individuals aged over 70 [3][6], [15], [19] , as supported by various studies. Comorbidities that accumulate with age, such as renovascular disease and congestive heart failure, may contribute to the higher incidence of AKI in elderly patients.…”

Section: Discussionmentioning

confidence: 99%

A model for predicting AKI after cardiopulmonary bypass surgery in Chinese patients with normal preoperative renal function.

Lin,

Xiao,

Lin

et al. 2024

Preprint

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Objective The objective of this study is to establish and validate a predictive model for the occurrence of acute kidney injury (AKI) following cardiopulmonary bypass (CPB) surgery in Chinese patients with preoperative renal function within normal range. Method From January 2015 to September 2022, a total of 1003 patients were added into the analysis. We used the ratio of 7:3 to divide the patients into a training group (n = 700) and a testing group (n = 303). Independent risk factors for postoperative AKI were identified through the least absolute shrinkage and selection operator (LASSO) regression and multifactor logistic regression analysis. A nomogram predictive model was then established. Various metrics such as the Area Under the ROC curve (AUC), calibration curve, and decision curve were used for validation of the nomogram predictive model in the training and testing groups. Additionally, the nomogram model was compared with three conventional models (Cleveland Clinic score, Mehta score, and Simplified Renal Index (SRI) score) using the AUC, calibration curve, and decision curve. Results The AKI group had a worse prognosis. Age, Body mass index (BMI), emergent surgery, CPB time, intraoperative use of adrenaline, and postoperative procalcitonin (PCT) were identified as important risk factors for AKI after CPB surgery. The nomogram predictive model demonstrated good discrimination (AUC: 0.772 (95%CI: 0.735 − 0.809) and 0.780 (95% CI: 0.724 − 0.835) ), calibration (Hosmer and Lemeshow goodness of fit test: P-value 0.6941 and 0.9539) and clinical utility in both the training and testing groups. Moreover, our model exhibited better discrimination, calibration capacity, and superior net benefit compared to the other three conventional models. Conclusion The nomogram predictive model, which established in patients with normal preoperative renal function, has high accuracy, calibration and clinical utility. The model's performance is superior to the other three conventional models (Cleveland Clinic score, Mehta score, and SRI score).

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Section: Discussionmentioning

confidence: 99%

A model for predicting AKI after cardiopulmonary bypass surgery in Chinese patients with normal preoperative renal function.

Lin,

Xiao,

Lin

et al. 2024

Preprint

View full text Add to dashboard Cite

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Development and Validation of a Nomogram for Predicting Acute Kidney Injury in Pediatric Patients Undergoing Cardiac Surgery

Shi,

Xiong,

Bie

et al. 2024

Pediatr Cardiol

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A systematic review of cardiac surgery clinical prediction models that include intra-operative variables

Jones,

Taylor,

Sperrin

et al. 2024

Perfusion

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Background Most cardiac surgery clinical prediction models (CPMs) are developed using pre-operative variables to predict post-operative outcomes. Some CPMs are developed with intra-operative variables, but none are widely used. The objective of this systematic review was to identify CPMs with intra-operative variables that predict short-term outcomes following adult cardiac surgery. Methods Ovid MEDLINE and EMBASE databases were searched from inception to December 2022, for studies developing a CPM with at least one intra-operative variable. Data were extracted using a critical appraisal framework and bias assessment tool. Model performance was analysed using discrimination and calibration measures. Results A total of 24 models were identified. Frequent predicted outcomes were acute kidney injury (9/24 studies) and peri-operative mortality (6/24 studies). Frequent pre-operative variables were age (18/24 studies) and creatinine/eGFR (18/24 studies). Common intra-operative variables were cardiopulmonary bypass time (16/24 studies) and transfusion (13/24 studies). Model discrimination was acceptable for all internally validated models (AUC 0.69-0.91). Calibration was poor (15/24 studies) or unreported (8/24 studies). Most CPMs were at a high or indeterminate risk of bias (23/24 models). The added value of intra-operative variables was assessed in six studies with statistically significantly improved discrimination demonstrated in two. Conclusion Weak reporting and methodological limitations may restrict wider applicability and adoption of existing CPMs that include intra-operative variables. There is some evidence that CPM discrimination is improved with the addition of intra-operative variables. Further work is required to understand the role of intra-operative CPMs in the management of cardiac surgery patients.

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Predicting the risk of acute kidney injury after cardiopulmonary bypass: development and assessment of a new predictive nomogram

Cited by 4 publications

References 54 publications

A model for predicting AKI after cardiopulmonary bypass surgery in Chinese patients with normal preoperative renal function.

A model for predicting AKI after cardiopulmonary bypass surgery in Chinese patients with normal preoperative renal function.

Development and Validation of a Nomogram for Predicting Acute Kidney Injury in Pediatric Patients Undergoing Cardiac Surgery

A systematic review of cardiac surgery clinical prediction models that include intra-operative variables

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