| Autor: |
Hui‐min Zhou, Rong‐jian Zhan, Xuanyu Chen, Yi‐fen Lin, Shao‐zhao Zhang, Huigan Zheng, Xueqin Wang, Meng‐ting Huang, Chao‐guang Xu, Xin‐xue Liao, Ting Tian, Xiao‐dong Zhuang |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
ESC heart failureReferences. |
| ISSN: |
2055-5822 |
| Popis: |
We aimed to explore the heterogeneous treatment effects (HTEs) for spironolactone treatment in patients with Heart failure with preserved ejection fraction (HFpEF) and examine the efficacy and safety of spironolactone medication, ensuring a better individualized therapy.We used the causal forest algorithm to discover the heterogeneous treatment effects (HTEs) from patients in the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trial. Cox regressions were performed to assess the hazard ratios (HRs) of spironolactone medication for cardiovascular death and drug discontinuation in each group. The causal forest model revealed three representative covariates and participants were partitioned into four subgroups which were Group 1 (baseline BMI ≤ 31.71 kg/mOur study manifested the HTEs of spironolactone in patients with HFpEF. Spironolactone treatment in HFpEF patients is feasible and effective in patients with high BMI and WBC while harmful in patients with low BMI and ALP. Machine learning model could be meaningful for improved categorization of patients with HFpEF, ensuring a better individualized therapy in the clinical setting. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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