Exploring differential response to an emergency department-based care transition intervention.

OBJECTIVE: To identify multivariable subgroups of patients with differential responses to a nurse-delivered care transition intervention after an emergency department (ED) visit in a randomized controlled trial (RCT) using an emerging data-driven method. DESIGN: Secondary analysis of RCT. PARTICIPANTS: 512 individuals enrolled in an RCT of a nurse-delivered care transition intervention after an ED visit. All 512 participants were included in a pre-specified subgroup analysis, and 451 of these had sufficient complete case data to be included in a model-based recursive (MoB) partitioning analysis. METHODS: The primary outcome was having at least one ED visit in 30 days after the index ED visit. Two analytical methods explored heterogeneity of treatment effects: data driven model-based recursive partitioning (MoB) using 37 candidate baseline variables, and a contextual point of comparison with prespecified subgroups defined by ED super-user status (≥ 3 ED visits in previous 6 months or not), sex (male/female), and age, individually examined via treatment arm by subgroup interaction terms in logistic regression models. Internal validation of the MoB analysis via bootstrap resampling with an optimism corrected c-statistic was conducted to provide a bias-corrected estimate. RESULTS: MoB detected treatment effect heterogeneity in a single subgroup, marital status. Unmarried patients randomized to the intervention had a repeat ED use rate of 22% compared to 34% in the usual care group; married patients randomized to the intervention had a 27% ED return rate compared to 12% in the usual care group. Internal validation demonstrated an optimism corrected c-statistic of 0.54. No treatment-by-covariate subgroup interactions were identified among the 3 prespecified subgroups. CONCLUSION: Although exploratory, the results of the MoB analysis suggest that patient factors related to social relationships such as marital status may be important contributors to differential response to a care transition intervention after an ED visit. These were characteristics that the investigators had not anticipated or planned to examine in the individual prespecified subgroup analysis. Data-driven methods can yield unexpected findings and contribute to a more complete understanding of differential treatment effects in subgroup analysis, which can inform further work on development of effective care transition interventions in the ED setting.

Duke Scholars

Author Cynthia Jan Coffman Biostatistics & Bioinformatics, Division of Biostatistics

Author Susan Nicole Hastings Medicine, Geriatrics and Palliative Care