Objectives
The objective of this systematic review was to identify risk factors and evaluate survival in patients with HF according to the 2016 ESC guidelines(17). Patient survival after being diagnosed with HF was the main focus of this study (primary outcome). Secondary outcomes included hospital admissions and factors that contribute to an elevated risk of mortality, such as age, left ventricular systolic dysfunction, treatment, and comorbidities.
Protocol and registration
The protocol was developed in accordance with the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) recommendations(17). The protocol was submitted for registration on the International Prospective Register of Systematic Reviews (PROSPERO) website on 21 August 2023 ahead of any data extraction. PROSPERO registration CRD42023456390. The protocol is available from:
https://www.crd.york.ac.uk/prospero/#recordDetails.ID= CRD42023456390
Eligibility criteria
Population
Our study included patients who were diagnosed with HF (as defined by ESC guidelines) and aged 15 years or older. The search will be limited to only studies published in English. However, there will be no restrictions based on the geographical location. Studies that discuss heart failure but do not report survival rates, studies with insufficient data or inaccessible full texts, or studies with under 1 year of follow-up will be excluded given the lack of information on long-term prognosis.
Intervention
In this study, no intervention was conducted, and we focused on investigating the survival time of patients with heart failure.
Information sources
A systematic literature search was carried out using the PubMed, Scopus, and Web of
Science databases. Three authors (SN, AM, SA) will independently perform two rounds of screening. The initial screening will be based on titles and abstracts, followed by a comprehensive review of the full text. In the case of disagreements, a third reviewer was consulted. Additionally, three authors will separately carry out duplicate data extraction. If needed, study investigators will be contacted for unreported data or additional details. The means of the recorded data were recorded in an Excel spreadsheet.
Search strategy
Our study included patients who were diagnosed with HF (as defined by ESC guidelines) and aged 15 years or older. The search will be limited to only studies published in English. However, there will be no restrictions based on the geographical location. Studies that discuss heart failure but do not report survival rates, studies with insufficient data or inaccessible full texts, or studies with under 1 year of follow-up will be excluded given the lack of information on long-term prognosis.
We aim to identify studies that are representative of the real world and furnish data that can be generalized to community populations. Consequently, we will eliminate interventional studies and instead focus on observational studies or studies that incorporate both interventional and observational phases.
We will exclude studies that focus on novel biomarkers, case reports, studies that involve selective subpopulations, and case series because they are not generalizable to community populations.
Conference abstracts were excluded from our analysis due to their limited provision of methodological details, hindering a thorough critical appraisal. Studies lacking original data, including review articles, were also excluded. However, we will conduct a thorough examination of their reference lists to identify original research studies that align with our inclusion criteria. For a list of terms that have been searched, see Supplementary File 1.
Data items and data collection process
The data to be extracted included the following:
- the first author's name, country of origin, year, study design, and methodology.
- study setting, sample size, mean duration of follow-up, and study dates.
- The participants’ age, sex, comorbidities, and echocardiography findings, including left ventricular ejection fraction.
- The outcome data included mortality rate, hospital admission cause of death, and summary statistics of any measure of morbidity, e.g., hazard ratio (HR).
- Number of person-years at risk and number of deaths by duration of follow-up, mean survival time, and other necessary information.
Outcome
Survival time was the primary outcome. Whenever feasible, the survival time was considered from the point of diagnosis. In cases where this information is unavailable, the survival time will be calculated from the time of enrollment in the study as a substitute.
Methodological appraisal and risk of bias
Two authors independently evaluated the risk of bias and methodological quality of each study using the Quality in Prognosis Studies (QUIPS) tool. This tool is specifically designed for prognostic studies and provides insights into the quality of each study. Additionally, the methodology of individual observational studies will be assessed against the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) criteria to further evaluate the reporting and methodological aspects of the included studies.
Data synthesis
We will utilize STATA version 17.0 (STATA Corp., College Station, TX) for all the statistical analyses. We plan to calculate effect sizes (hazard ratios) and 95% confidence intervals (CIs) for analysis using survival data for HF patients at various time points and Kaplan‒Meier plots. To assess heterogeneity among studies, we will employ the Q² and I² statistics and explore potential sources of heterogeneity via subgroup analysis. The findings will be discussed accordingly. In cases of low heterogeneity (I2< 50%), we used the fixed-effects model for the analysis. Conversely, the random-effects model was utilized when the heterogeneity was high (I2 ≥ 50%). Additionally, we will conduct exploratory subgroup analyses based on age, sex (male and female), and study setting. Subsequently, sensitivity analysis will be used to identify outlier effect sizes and missing data. We will employ statistical methods, such as the Begg test and Egger test, in the meta-analysis to assess publication bias and evaluate publication bias in the corresponding graphs. Forest plots were used to visualize the results of the meta-analysis, aiding in the identification of factors that may influence prognosis.