Machine-learning predictions for acute kidney injuries after coronary artery bypass grafting: a real-life muticenter retrospective cohort study

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Abstract

Background

Acute kidney injury (AKI) after coronary artery bypass grafting (CABG) surgery is associated with poor outcomes. The objective of this study was to apply a new machine learning (ML) method to establish prediction models of AKI after CABG.

Methods

A total of 2,780 patients from two medical centers in East China who underwent primary isolated CABG were enrolled. The dataset was randomly divided for model training (80%) and model testing (20%). Four ML models based on LightGBM, Support vector machine (SVM), Softmax and random forest (RF) algorithms respectively were established in Python. A total of 2,051 patients from two other medical centers were assigned to an external validation group to verify the performances of the ML prediction models. The models were evaluated using the area under the receiver operating characteristics curve (AUC), Hosmer-Lemeshow goodness-of-fit statistic, Bland-Altman plots, and decision curve analysis. The outcome of the LightGBM model was interpreted using SHapley Additive exPlanations (SHAP).

Results

The incidence of postoperative AKI in the modeling group was 13.4%. Similarly, the incidence of postoperative AKI of the two medical centers in the external validation group was 8.2% and 13.6% respectively. LightGBM performed the best in predicting, with an AUC of 0.8027 in internal validation group and 0.8798 and 0.7801 in the external validation group. The SHAP revealed the top 20 predictors of postoperative AKI ranked according to the importance, and the top three features on prediction were the serum creatinine in the first 24 h after operation, the last preoperative Scr level, and body surface area.

Conclusion

This study provides a LightGBM predictive model that can make accurate predictions for AKI after CABG surgery. The LightGBM model shows good predictive ability in both internal and external validation. It can help cardiac surgeons identify high-risk patients who may experience AKI after CABG surgery.

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Most cited references 38

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The STROBE guidelines

Sarah Cuschieri (2019)

An observational study is a type of epidemiological study design, which can take the form of a cohort, a case–control, or a cross-sectional study. When presenting observational studies in manuscripts, an author needs to ascertain a clear presentation of the work and provide the reader with appropriate information to enable critical appraisal of the research. The Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guidelines were created to aid the author in ensuring high-quality presentation of the conducted observational study. The original articles publishing the STROBE guidelines together with their bibliographies were identified and thoroughly reviewed. These guidelines consist of 22 checklist items that the author needs to fulfil before submitting the manuscript to a journal. The STROBE guidelines were created to aid the authors in presenting their work and not to act as a validation tool for the conducted study or as a framework to conduct an observational study on. The authors complying with these guidelines are more likely to succeed in publishing their observational study work in a journal.

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A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group.

A S Levey, J P Bosch, J B Lewis … (1999)

Serum creatinine concentration is widely used as an index of renal function, but this concentration is affected by factors other than glomerular filtration rate (GFR). To develop an equation to predict GFR from serum creatinine concentration and other factors. Cross-sectional study of GFR, creatinine clearance, serum creatinine concentration, and demographic and clinical characteristics in patients with chronic renal disease. 1628 patients enrolled in the baseline period of the Modification of Diet in Renal Disease (MDRD) Study, of whom 1070 were randomly selected as the training sample; the remaining 558 patients constituted the validation sample. The prediction equation was developed by stepwise regression applied to the training sample. The equation was then tested and compared with other prediction equations in the validation sample. To simplify prediction of GFR, the equation included only demographic and serum variables. Independent factors associated with a lower GFR included a higher serum creatinine concentration, older age, female sex, nonblack ethnicity, higher serum urea nitrogen levels, and lower serum albumin levels (P < 0.001 for all factors). The multiple regression model explained 90.3% of the variance in the logarithm of GFR in the validation sample. Measured creatinine clearance overestimated GFR by 19%, and creatinine clearance predicted by the Cockcroft-Gault formula overestimated GFR by 16%. After adjustment for this overestimation, the percentage of variance of the logarithm of GFR predicted by measured creatinine clearance or the Cockcroft-Gault formula was 86.6% and 84.2%, respectively. The equation developed from the MDRD Study provided a more accurate estimate of GFR in our study group than measured creatinine clearance or other commonly used equations.

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Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

Wei Luo, Dinh Phung, Truyen Tran … (2016)

Background As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. Objective To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. Methods A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. Results The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. Conclusions A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community.

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Author and article information

Contributors

Xin Zhao: zhaoxin@email.sdu.edu.cn

Mingliang Li: 31526259@qq.com

Yangyang Zhang: zhangyangyang_wy@vip.sina.com

Journal

Journal ID (nlm-ta): BMC Med Inform Decis Mak

Journal ID (iso-abbrev): BMC Med Inform Decis Mak

Title: BMC Medical Informatics and Decision Making

Publisher: BioMed Central (London )

ISSN (Electronic): 1472-6947

Publication date (Electronic): 23 November 2023

Publication date PMC-release: 23 November 2023

Publication date Collection: 2023

Volume: 23

Electronic Location Identifier: 270

Affiliations

[1 ]College of Information Science, Shanghai Ocean University, ( https://ror.org/04n40zv07) Shanghai, P.R. China

[2 ]Department of Cardiovascular Surgery, Qilu Hospital of Shandong University, ( https://ror.org/056ef9489) Jinan, Shandong P.R. China

[3 ]Department of Thoracic Surgery, Xuzhou Cancer Hospital, ( https://ror.org/01g9gaq76) Xuzhou, P.R. China

[4 ]Department of Thoracic Surgery, Sheyang County People’s Hospital, ( https://ror.org/030a08k25) Yancheng, P.R. China

[5 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, Department of Cardiovascular Surgery, Shanghai Chest Hospital, , Shanghai Jiao Tong University School of Medicine, ; 241 Huaihai West Road, Shanghai, 200120 China

[6 ]Department of Cardiovascular Surgery, Jiangsu Province Hospital, the First Affiliated Hospital of Nanjing Medical University, ( https://ror.org/04py1g812) Nanjing, P.R. China

[7 ]Department of Cardiovascular Surgery, The General Hospital of Ningxia Medical University, ( https://ror.org/02h8a1848) Yinchuan, Ningxia P.R. China

Article

Publisher ID: 2376

DOI: 10.1186/s12911-023-02376-0

PMC ID: 10668365

PubMed ID: 37996844

SO-VID: eef309b1-0284-4fd2-91b0-4c40caa1bbe8

License:

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