Trajectory classes of depression in a randomized depression trial of heart failure patients: a reanalysis of the SADHART-CHF trial.

OBJECTIVE

The objective of this article was to determine whether, in drug intervention trials, growth mixture modeling (GMM) is able to identify drug-responsive trajectory classes that are not evident in traditional growth modeling approaches.

METHODS

We reanalyzed acute phase (biweekly data up to 7 occasions) and longitudinal (12 months) data on the 469 patients in the SADHART-CHF study of the safety and efficacy of sertraline for depression in patients with heart failure. GMM was used to identify the trajectory classes present in the treatment and placebo groups, based on Hamilton Depression Rating Scale scores.

RESULTS

Two distinct trajectory classes were identified in the treatment group: (1) chronic depressives (12%), who remained depressed through the treatment phase; and (2) responders (88%), who had scores indicating nondepression at the conclusion of the acute phase. At baseline, chronic depressives were distinguished by higher Hamilton Depression Rating Scale scores, the presence of implantable cardioverter defibrillators, and a history of anxiety. During follow-up, they were more likely to have unstable angina. Only responders remitted (70%). Three distinct trajectories were identified in the placebo group: (1) moderating depressives (19%), (2) temporary improvers (13%), and (3) responders (68%). At baseline, the classes differed in mean Hamilton Depression Rating Scale scores, responders' scores falling between the other 2 classes, and the proportion with renal disease. Only remission differed at follow-up: responders (76%), moderating depressives (21%), and temporary improvers (3%). Where the traditional analytic approach found improvement from moderate to mild depression but no significant treatment effect, GMM found response in 20% more people in the treatment group than in the placebo group.

CONCLUSIONS

Unlike conventionally used, standard analytic approaches, which focus on intervention impact at study end or change from baseline to study end, GMM enables maximum use of repeated data to identify unique trajectories of latent classes that are responsive to the intervention.