Stroke Community Transitions Project

New York University School Of Medicine, New York NY

Investigators

Linked publications & trials

Abstract

We propose to assess the comparative effectiveness, relative to usual home care (UHC), of a novel nurse practitioner (NP) + community health worker (CHW) Community Transitions Intervention (CTI) adapted for post-stroke patients and culturally tailored, respectively, to Black and Hispanic patients to account for cultural differences that may influence their disease perceptions and adherence to recommended medical and/or lifestyle regimens. We would conduct a three-arm patient randomized controlled trial (RCT) of post-stroke home health patients assigned to: 1) usual home care (UHC) alone; 2) UHC+3-month NP-CTI; or 3) UHC+NP+CHW 3-month CTI. The intervention will focus on: The NP-only and the NP+CHW combined intervention will be assessed relative to usual care and to each other. All patients recruited to the study will have poorly controlled hypertension and, due to disability, will be homebound at study entry. Many also will be characterized by advanced age and other comorbid conditions. The primary outcome will be change in systolic blood pressure (SBP), while secondary outcomes will encompass a range of medical, behavioral, quality of life, and hospitalization outcomes. Measures of these dependent variables will be derived from a combination of primary and secondary data, including patient records, administrative files, patient assessment instruments, patient interviews, home BP measurement and laboratory tests (HbAlc and cholesterol measurement). Data collection will be conducted at baseline, 3 months and 12 months. The principal hypothesis is that the intervention arms will significantly reduce SBP in comparison to UHC. Secondary hypotheses also assume that the intervention arms will yield significant relative advantage compared to LJHC Data analyses will examine both endpoint differences and slopes, using all waves of data. The primary proposed analyses will use mixed random effects models, and a full information maximum likelihood (FIML) approach, with sensitivity analyses using generalized estimating equation (GEE) regression models. Our ultimate goal is to reduce stroke recurrence in this racially and culturally diverse high risk population.