Jiang H, Hu R, Wang YJ, Xie X. Predicting depression in patients with heart failure based on a stacking model. World J Clin Cases 2024; 12(21): 4661-4672 [DOI: 10.12998/wjcc.v12.i21.4661]
Corresponding Author of This Article
Xiang Xie, Doctor, Chief Physician, Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Shushan District, Hefei 230601, Anhui Province, China. sonographer@126.com
Research Domain of This Article
Psychology, Clinical
Article-Type of This Article
Clinical and Translational Research
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Clin Cases. Jul 26, 2024; 12(21): 4661-4672 Published online Jul 26, 2024. doi: 10.12998/wjcc.v12.i21.4661
Predicting depression in patients with heart failure based on a stacking model
Hui Jiang, Rui Hu, Yu-Jie Wang, Xiang Xie
Hui Jiang, Rui Hu, Xiang Xie, Department of Ultrasound Diagnosis, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
Yu-Jie Wang, Department of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui Province, China
Author contributions: Jiang H and Hu R designed the research study; Jiang H, Wang YJ and Xie X analyzed the data and wrote the manuscript. All of the authors read and approved the final manuscript.
Conflict-of-interest statement: The authors declare that they have no competing interests.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Xiang Xie, Doctor, Chief Physician, Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Shushan District, Hefei 230601, Anhui Province, China. sonographer@126.com
Received: April 27, 2024 Revised: May 27, 2024 Accepted: June 17, 2024 Published online: July 26, 2024 Processing time: 63 Days and 19.3 Hours
Abstract
BACKGROUND
There is a lack of literature discussing the utilization of the stacking ensemble algorithm for predicting depression in patients with heart failure (HF).
AIM
To create a stacking model for predicting depression in patients with HF.
METHODS
This study analyzed data on 1084 HF patients from the National Health and Nutrition Examination Survey database spanning from 2005 to 2018. Through univariate analysis and the use of an artificial neural network algorithm, predictors significantly linked to depression were identified. These predictors were utilized to create a stacking model employing tree-based learners. The performances of both the individual models and the stacking model were assessed by using the test dataset. Furthermore, the SHapley additive exPlanations (SHAP) model was applied to interpret the stacking model.
RESULTS
The models included five predictors. Among these models, the stacking model demonstrated the highest performance, achieving an area under the curve of 0.77 (95%CI: 0.71-0.84), a sensitivity of 0.71, and a specificity of 0.68. The calibration curve supported the reliability of the models, and decision curve analysis confirmed their clinical value. The SHAP plot demonstrated that age had the most significant impact on the stacking model's output.
CONCLUSION
The stacking model demonstrated strong predictive performance. Clinicians can utilize this model to identify high-risk depression patients with HF, thus enabling early provision of psychological interventions.
Core Tip: In this study, we utilized easily accessible demographic data and laboratory indicators to construct a stacking ensemble model for predicting depression in heart failure patients. We then compared the performance of this stacking model with that of a single machine learning (ML) model and discovered that the stacking model outperformed the single ML model.
