Key result
A lasso logistics regression model based on perioperative variables best predicted all-stage AKI after valvular surgery (C statistic 0.7; 95% CI 0.66-0.73) compared to other machine learning models.
Why the study?
Existing AKI risk prediction tools have limitations and perform poorly in the Chinese population following valvular cardiac surgery.
Do perioperative parameters-based prediction models accurately predict acute kidney injury in Chinese patients following valvular surgery?
Cohort (n=3,392)
Do perioperative parameters-based prediction models accurately predict acute kidney injury in Chinese patients following valvular surgery?
Effect estimate: C statistic 0.7 (95% CI 0.66-0.73)
A lasso logistics regression model based on perioperative variables provides better prediction of acute kidney injury after valvular cardiac surgery in a Chinese population compared to machine learning models and the existing AKICS score.
May support perioperative AKI risk stratification in Chinese valvular surgery patients; leaves open external validation before clinical adoption.
Background: Acute kidney injury (AKI) is a relevant complication after cardiac surgery and is associated with significant morbidity and mortality. Existing risk prediction tools have certain limitations and perform poorly in the Chinese population. We aimed to develop prediction models for AKI after valvular cardiac surgery in the Chinese population. Methods: Models were developed from a retrospective cohort of patients undergoing valve surgery from December 2013 to November 2018. Three models were developed to predict all-stage, or moderate to severe AKI, as diagnosed according to Kidney Disease: Improving Global Outcomes (KDIGO) based on patient characteristics and perioperative variables. Models were developed based on lasso logistics regression (LLR), random forest (RF), and extreme gradient boosting (XGboost). The accuracy was compared among three models and against the previously published reference AKICS score. Results: A total of 3,392 patients (mean [SD] age, 50.1 [11.3] years; 1787 [52.7%] male) were identified during the study period. The development of AKI was recorded in 50.5% of patients undergoing valve surgery. In the internal validation testing set, the LLR model marginally improved discrimination (C statistic, 0.7; 95% CI, 0.66-0.73) compared with two machine learning models, RF (C statistic, 0.69; 95% CI, 0.65-0.72) and XGBoost (C statistic, 0.66; 95% CI, 0.63-0.70). A better calibration was also found in the LLR, with a greater net benefit, especially for the higher probabilities as indicated in the decision curve analysis. All three newly developed models outperformed the reference AKICS score. Conclusion: Among the Chinese population undergoing CPB-assisted valvular cardiac surgery, prediction models based on perioperative variables were developed. The LLR model demonstrated the best predictive performance was selected for predicting all-stage AKI after surgery. Clinical trial registration: Trial registration: Clinicaltrials.gov, NCT04237636.
No takes yet. Share an insight, caveat, or question.
Gong et al. (2023) conducted a cohort in Acute kidney injury after valvular cardiac surgery (n=3,392). Lasso logistics regression (LLR) model vs. Random forest (RF), extreme gradient boosting (XGBoost) models, and AKICS score was evaluated on Prediction of all-stage AKI (C statistic 0.7, 95% CI 0.66-0.73). A lasso logistics regression model based on perioperative variables best predicted all-stage AKI after valvular surgery (C statistic 0.7; 95% CI 0.66-0.73) compared to other machine learning models.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: