Methods
Search strategy
The research strategy, study selection and analysis method used in the study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA).13 Electronic databases (MEDLINE, EMBASE) were systematically searched for published studies reporting risk factors related to incident HF. Search key terms were: ‘incident heart failure’ and ‘risk factors’, ‘risk assessment’, ‘risk impact’, ‘risk prediction’, ‘risk score’, ‘risk prevention’. To ensure the identification of all relevant articles and publications, the reference lists of these articles were also reviewed to identify additional studies. The last search was performed on 7 October 2013.
Study inclusion
From these lists, studies were included if they met each of the following criteria: (1) studies of a full-length publication in a peer-reviewed English language journal; (2) studies carried out on human adults >18 years of age; (3) studies carried out on an unselected community population; (4) studies reporting risk factors relating to incident HF; (5) studies using Cox proportional hazard models reporting risk effect sizes in HR with 95% CIs and/or associated p value. This review incorporated mainly observational cohort studies.
Outcomes
The primary outcome of interest was incident HF. The criteria for identification of incident HF were described as one or more of the following: (1) medical diagnosis from physician's records; (2) evidence of treatment for HF (ie, diuretics and either digitalis or a vasodilator); (3) hospital or nursing home stays in which the participant had a discharge diagnosis with a code of International Statistical Classification of Diseases and related health problems (ICD-9 code of 428.0 to 428.9); (4) death certificate report in which the underlying cause of death was recorded using an ICD-9 code of 428.0 to 428.9.
Data extraction
Data were extracted independently by reviewers (HY, KN and PO). All discrepancies were reviewed and resolved by consensus. For the systematic review, the following data concerning the individual study populations were extracted: demographic and clinical characteristics and associated risk prevalence at baseline; study design; years of follow-up; statistical models; statistic models; risk effect sizes and their associated 95% CIs with p values; covariates included in the risk assessment models in relation to outcome. In situations in which multiple articles were published from a single cohort, data were included only if different risk variables were analysed and reported.
Statistical analysis
Reported risk effect sizes and the statistical models used in each study were reviewed. Crude measures of effect with 95% CIs were extracted for each risk variable. Multiple within-study effects stratified in subgroups were combined by weighting each group by its number of participants. Study risk estimates reported per categorical change were recalculated as continuous variables for body mass index (BMI).14 Risk estimates from the majority of studies were estimated using Cox proportional hazard models and pooled as HRs (although some incorrectly labelled these as relative risk/rate).15 ,16 Risk estimates reported as ‘Relative Risk’ using the Mantel-Haenszel17 or linear regression model18 or OR using the logistic regression model19 were excluded for further analysis. Consequently, pooled risk estimates were all from studies using Cox proportional hazard models and were suitable for providing summary risk estimates. Both unadjusted and maximally adjusted risk effects were pooled using random-effects models weighted by inverse variance.20 Further, a subset of studies reporting seven mutually adjusted risk effects (age, male gender, BMI, smoking, HTN, diabetes mellitus (DM) and CAD) were also pooled. When CIs were not reported, their associated p values were used to estimate variance of the risk estimate.21
The Cochrane Q statistic and I2 values index were used to assess the degree of heterogeneity across studies. Funnel plots were constructed and Egger's test was used to assess potential publication bias. Duval and Tweedie's trim and fill method was used to assess the potential effects of publication bias on risk estimates. Meta-regression was also performed for each risk factor to examine possible study factors associated with heterogeneity. The assessment of study quality was performed using the Newcastle-Ottawa Scale (NOS) for non-randomised studies in meta-analyses.22 Statistical analysis was performed using statistics package R V.3.1.1.