Statistical Analysis
Continuous variables were summarized using mean ± SD. Categorical
variables were summarized using percentage. Variables were compared
across subjects with normal and diastolic dysfunction using t-test,
Kruskal Wallis, and chi-square test. Distinct clusters (heterogenous
groups) within a patient sample were evaluated using latent class
analysis (LCA) to classify subjects into multiple comorbidity groups
based on comorbidities present at admission. LCA uses the joint
distribution of observed responses across all individuals on a set of
items (i.e., types of comorbidities) to characterize an underlying
categorical latent variable that subdivides the given population into a
smaller number of groups using modal class assignment. LCAs were
conducted using the 12 baseline variables representing comorbidities
based on cardiovascular diseases, chronic lung diseases, obesity and
baseline ASA score. Since the number of clusters is unknown a priori,
statistical comparisons of model fit, based primarily on the log
likelihood value and Bayesian Information Criterion (BIC) were used to
compare models with an increasing number of clusters. An LCA is
particularly suitable because of its ability to specify unobserved
(latent) subgroups of individuals
[17]
Cox Proportional Hazard models, with the response variable being ‘time
to discharge’ and the event variable being ‘discharge’, were used to
evaluate the association between time to discharge and diastolic
dysfunction with and without adjustment for confounding variables.
Additional Cox models were used to determine the role of secondary
outcomes in mediating the relationship between diastolic dysfunction and
time to discharge. Survival analyses were performed using the R
programming environment. All estimates and confidence intervals (CIs)
were obtained using the “coxph” function.