Diagnostic value of patterns of symptoms and signs of heart failure: application of latent class analysis with concomitant variables in a cross-sectional study.

OBJECTIVE

The diagnosis of heart failure (HF) requires a compatible clinical syndrome and demonstration of cardiac dysfunction by imaging or functional tests. Since individual symptoms and signs are generally unreliable and have limited value for diagnosing HF, the authors aimed to identify patterns of symptoms and signs, based on findings routinely collected in current clinical practice, and to evaluate their diagnostic value, taking into account the a priori likelihood of HF.

DESIGN

Cross-sectional evaluation.

PARTICIPANTS

1115 community participants aged ≥45 years from Porto, Portugal, in 2006-2008.

MAIN OUTCOMES MEASURES

Patterns were identified by latent class analysis, using concomitant variables to predict class membership. Patterns used 11 symptoms/signs, covering dimensions of congestion and hypoperfusion. Sex, age, education, obesity, diabetes and history of myocardial infarction or HF were included as concomitants.

RESULTS

Bayesian information criteria supported a solution with three patterns: 10.1% of participants followed a pattern with symptoms of troubled breathing and signs of congestion (pattern 1), 27.8% a pattern characterised mainly by signs of congestion (pattern 2) and 62.1% were essentially asymptomatic (pattern 3); model fit was best when including concomitant variables. The likelihood ratio of patterns 1, 2 and 3 for left ventricular systolic dysfunction was 3.4, 1.1 and 0.6, and for left ventricular diastolic dysfunction 3.5, 1.4 and 0.5, respectively.

CONCLUSIONS

The use of concomitant variables can improve the diagnostic value of the symptoms and signs patterns and, consequently, improve the usefulness of the symptoms and signs for diagnosis and as an outcome measure. The potential for application in other settings of complex diagnoses is very high. These models were shown to be useful to standardise and quantify the probabilistic reasoning in clinical diagnosis, upon which decisions of further investigation and even treatment need to be made.