Multimodal Prognostic System for Heart Failure: A Multi-Center Study

Developing a Prognostic Warning System for Heart Failure Based on Multimodal Clinical Data: a Multi-center Retrospective Study

This study is a multicenter retrospective research aimed at developing a heart failure prognosis warning system based on multimodal clinical data to improve the diagnosis and treatment of heart failure.

This study aims to address the following main questions:

1. What are the clinical characteristics and current treatment status of heart failure patients?
2. What factors influence the onset of heart failure and the prognosis of patients?
3. How can real-world big data be utilized to construct an accurate heart failure prognosis assessment model?
4. How can this prognosis assessment model help improve the therapeutic effects and quality of life for heart failure patients?

Inclusion Criteria:

Age Requirement: We plan to include adult patients aged 18 and above to ensure the study covers a broad spectrum of the adult heart failure patient population.

Diagnostic Criteria: Patients should have a discharge diagnosis of heart failure according to the guidelines for heart failure diagnosis and treatment.

Time Frame: The discharge time of patients should be between January 2011 and December 2024 to collect and analyze data from the past several years.

Data Integrity: There is a preference for including medical records with high data integrity, but the specific criteria may be adjusted according to the actual situation of the dataset.

Exclusion Criteria:

Data Missing: Patients with more than 30% missing data in their medical records may be considered for exclusion to ensure the accuracy and reliability of the analysis.

Diagnostic Inconsistency: Patients with unclear diagnoses or potential for confusion with other diseases may be excluded to ensure the specificity of the study.

Non-standard Treatment: Patients who have not received treatment according to the established treatment protocol may be excluded to reduce the impact of treatment variability on the study results.