Title:Risk Prediction Models and Novel Prognostic Factors for Heart Failure with
Preserved Ejection Fraction: A Systematic and Comprehensive Review
Volume: 29
Issue: 25
Author(s): Shanshan Lin, Zhihua Yang, Yangxi Liu, Yingfei Bi, Yu Liu, Zeyu Zhang, Xuan Zhang, Zhuangzhuang Jia, Xianliang Wang*Jingyuan Mao*
Affiliation:
- Department of Cardiovascular, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine/National Clinical
Research Center for Chinese Medicine Acupuncture and Moxibustion, No. 88, Changling Road, Xiqing District, Tianjin 300381,
China
- Department of Cardiovascular, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine/National Clinical
Research Center for Chinese Medicine Acupuncture and Moxibustion, No. 88, Changling Road, Xiqing District, Tianjin 300381,
China
Keywords:
Heart failure with preserved ejection fraction, risk prediction model, prognostic factor, risk stratification, dynamic assessment, biomarkers.
Abstract:
Background: Patients with heart failure with preserved ejection fraction (HFpEF) have large individual
differences, unclear risk stratification, and imperfect treatment plans. Risk prediction models are helpful for the dynamic
assessment of patients' prognostic risk and early intensive therapy of high-risk patients. The purpose of this
study is to systematically summarize the existing risk prediction models and novel prognostic factors for HFpEF, to
provide a reference for the construction of convenient and efficient HFpEF risk prediction models.
Methods: Studies on risk prediction models and prognostic factors for HFpEF were systematically searched in
relevant databases including PubMed and Embase. The retrieval time was from inception to February 1, 2023.
The Quality in Prognosis Studies (QUIPS) tool was used to assess the risk of bias in included studies. The predictive
value of risk prediction models for end outcomes was evaluated by sensitivity, specificity, the area under
the curve, C-statistic, C-index, etc. In the literature screening process, potential novel prognostic factors
with high value were explored.
Results: A total of 21 eligible HFpEF risk prediction models and 22 relevant studies were included. Except for
2 studies with a high risk of bias and 2 studies with a moderate risk of bias, other studies that proposed risk prediction
models had a low risk of bias overall. Potential novel prognostic factors for HFpEF were classified and
described in terms of demographic characteristics (age, sex, and race), lifestyle (physical activity, body mass
index, weight change, and smoking history), laboratory tests (biomarkers), physical inspection (blood pressure,
electrocardiogram, imaging examination), and comorbidities.
Conclusion: It is of great significance to explore the potential novel prognostic factors of HFpEF and build a
more convenient and efficient risk prediction model for improving the overall prognosis of patients. This
review can provide a substantial reference for further research.