Prognostic implications of elevated pulmonary artery systolic pressure on 6-month mortality in elderly patients with acute myocardial infarction

Wang, Shengji; Lian, Yong-Gang; Wang, Hongfei; Fan, Xiaoran; Zhao, Haiying

doi:10.1097/ec9.0000000000000035

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…Notably, pulmonary arterial hypertension (PAH) is ranked as the fourth signi cant variable. PAH is an important risk factor for AKI after transcatheter aortic valve implantation [39] and is closely related to right heart function [40,41]. Reportedly, right ventricular failure is associated with severe postoperative AKI of cardiac surgery [42] and PAH is one of the top ve risk factors, providing a new viewpoint on clinical decision making.…”

Section: Discussionmentioning

confidence: 99%

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

Jia

Bai

et al. 2023

Preprint

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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 Totally 2780 patients from two medical centers in East China who underwent primary isolated CABG were enrolled. Then 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 on Python. A total of 2051 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 24h 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. This ML 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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Section: Discussionmentioning

confidence: 99%

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

Jia

Bai

et al. 2023

Preprint

View full text Add to dashboard Cite

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

Jia,

Xu,

Bai

et al. 2023

BMC Med Inform Decis Mak

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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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Prognostic implications of elevated pulmonary artery systolic pressure on 6-month mortality in elderly patients with acute myocardial infarction

Cited by 2 publications

References 30 publications

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

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

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

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