Knowledge to action: Rationale and design of the Patient-Centered Care Transitions in Heart Failure (PACT-HF) stepped wedge cluster randomized trial

Accepted Manuscript Knowledge to action: Rationale and design of the patient-centered care transitions in heart failure (PACT-HF) stepped wedge cluster randomized trial

Harriette GC Van Spall, Shun Fu Lee, Feng Xie, Dennis T Ko, Lehana Thabane, Quazi Ibrahim, Peter R Mitoff, Michael Heffernan, Manish Maingi, Michael C Tjandrawidjaja, Mohammad I Zia, Mohamed Panju, Richard Perez, Kim D Simek, Liane Porepa, Ian D Graham, R. Brian Haynes, Dilys Haughton, Stuart J Connolly PII: DOI: Reference:

S0002-8703(17)30401-5 https://doi.org/10.1016/j.ahj.2017.12.013 YMHJ 5601

To appear in: Received date: Accepted date:

30 June 2017 17 December 2017

Please cite this article as: Harriette GC Van Spall, Shun Fu Lee, Feng Xie, Dennis T Ko, Lehana Thabane, Quazi Ibrahim, Peter R Mitoff, Michael Heffernan, Manish Maingi, Michael C Tjandrawidjaja, Mohammad I Zia, Mohamed Panju, Richard Perez, Kim D Simek, Liane Porepa, Ian D Graham, R. Brian Haynes, Dilys Haughton, Stuart J Connolly , Knowledge to action: Rationale and design of the patient-centered care transitions in heart failure (PACT-HF) stepped wedge cluster randomized trial. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Ymhj(2017), https://doi.org/10.1016/j.ahj.2017.12.013

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ACCEPTED MANUSCRIPT PACT-HF Rationale and Study Design

KNOWLEDGE TO ACTION: RATIONALE AND DESIGN OF THE PATIENT-CENTERED CARE TRANSITIONS IN HEART FAILURE (PACT-HF) STEPPED WEDGE CLUSTER RANDOMIZED TRIAL

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Harriette GC Van Spall, MD, MPH1,2,3; Shun Fu Lee, PhD ; Feng Xie, MSc, PhD2; Dennis T Ko, 4,5 2 MD, MSc ; Lehana Thabane, PhD ; Quazi Ibrahim, MSc3; Peter R Mitoff, MD5,6; Michael Heffernan, MD, PhD7; Manish Maingi, MD8; Michael C Tjandrawidjaja, MD9; Mohammad I Zia, MD5,10; Mohamed Panju, MSc, MD1; Richard Perez, MSc4; Kim D Simek, BSc3; Liane Porepa, MD11; Ian D Graham, MA, PhD12; R. Brian Haynes, MD, MSc, PhD2; Dilys Haughton, 1,3 BScN, MHSc13; Stuart J Connolly, MSc, MD

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Corresponding Author:

Harriette GC Van Spall MD MPH

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20 Copeland Avenue David Braley Research Institute Bldg, Suite C3-117 Hamilton ON L8L 0A3 Phone: (905) 521-2100 X40601 Fax: (905) 297-3785

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Department of Medicine, McMaster University, Hamilton, Ontario Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario 3 Population Health Research Institute, Hamilton, Ontario 4 Institute for Clinical Evaluative Sciences, Ontario 5 Department of Medicine, University of Toronto, Toronto, Ontario 6 Department of Medicine, St. Joseph’s Health Centre, Toronto 7 Department of Medicine, Halton Health Care Services, Oakville, Ontario 8 Cardiac Health Program, Trillium Health Partners, Mississauga, Ontario 9 Department of Medicine, William Osler Health System, Brampton, Ontario 10 Department of Medicine, Michael Garron Hospital, Toronto, Ontario 11 Department of Medicine, Southlake Regional Health Centre, Newmarket, Ontario 12 School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario 13 Hamilton Niagara Haldimand Brant Community Care Access Centre, Hamilton, Ontario

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ABSTRACT Introduction: Heart Failure (HF) is a common cause of hospitalization in older adults. The transition from hospital to home is high-risk, and gaps in transitional care can increase the risk of re-hospitalization and death. Combining health care services supported by meta-analyses, we designed the Patient-Centered Care Transitions in HF (PACT-HF) transitional care model.

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Methods: Adopting an integrated Knowledge Translation (iKT) approach in which decisionmakers and clinicians are partners in research, we implement and test the effectiveness of PACTHF among patients hospitalized for HF. We use a pragmatic stepped wedge cluster randomized trial design to introduce the complex health service intervention to 10 large hospitals in a randomized sequence until all hospitals initiate the intervention. The goal is for all patients hospitalized with HF to receive self-care education, multidisciplinary care, and early follow-up with their health care providers; and in addition, for high-risk patients to receive post-discharge nurse-led home visits and outpatient care in Heart Function clinics. This requires integration of care across hospitals, home care agencies, and outpatient clinics in our publicly funded health care system. While hospitals are the unit of recruitment and analysis, patients (estimated sample size of 3200) are the unit of analysis. Primary outcomes are hierarchically ordered as time to composite readmissions / emergency department (ED) visits / death at 3 months and time to composite readmissions / ED visits at 30 days. In a nested study of 8 hospitals, we measure the patient-centered outcomes of Discharge Preparedness, Care Transitions Quality, and Quality Adjusted Life Years (QALY); and the 6-month health care resource use and costs. We obtain all clinical and cost outcomes via linkages to provincial administrative databases.

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Conclusions: This protocol describes the implementation and testing of a transitional care model comprising health care services informed by high-level evidence. The study adopts an iKT and pragmatic approach, uses a robust study design, links clinical trial data with outcomes held in administrative databases, and includes patient-reported outcomes. Findings will have implications on clinical practice, health care policy, and Knowledge Translation (KT) research methodology. Keywords: Heart Failure, transitional care, integrated Knowledge Translation, complex health service intervention, stepped wedge clinical trial

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Registration: clinicaltrials.gov (NCT02112227) INTRODUCTION

Heart failure (HF) is a serious and common condition, affecting 2% of the population in western countries and associated with a 28% risk of death at 1 year.[1-3] Disproportionately affecting the elderly, it is the most common cause of hospitalization in people 65 years of age or older.[4,5] Approximately 80% of costs expended on HF care are due to hospitalizations and readmissions.[68] Hospitalizations place a tremendous burden on patients, are associated with a reduced quality of life, and independently predict a higher risk of death after adjusting for HF severity.[9] The period after hospital discharge represents a time of heightened vulnerability as patients learn to manage their disease and access appropriate outpatient care. As many as 40% of early 2

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readmissions after hospitalization for HF may be related to suboptimal transitional care - actions that promote the coordination and continuity of care as patients transfer between health care settings or providers.[10,11] In a database study, 60% of HF patients received no follow-up medical care within a week after discharge, and patients discharged from hospitals with the lowest follow-up rates experienced the highest 30-day readmission rates.[12]

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To develop an evidence-informed transitional care model for patients hospitalized with HF, we performed a comparative effectiveness network meta-analysis of 54 randomized clinical trials (RCTs) evaluating the efficacy of transitional care services in HF.[13] Among the 7 types of services evaluated, only nurse-led home visits and multidisciplinary HF clinics significantly decreased both readmissions and death. These services were either cost saving or cost-neutral to the health care system by the end of the follow up period. Common features of these interventions included education with an emphasis on HF self-care, a multidisciplinary approach that included nurses, and regular contact between clinicians and patients soon after hospital discharge.

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Guided by the results of this meta-analysis and the Knowledge to Action framework [14], we designed the Patient-centered Care Transitions in HF (PACT-HF) transitional care model. This model emphasized care integration and patient-centered care, addressing the needs of the “whole person”, incorporating patient preferences, involving informal caregivers, and emphasizing care coordination.[15,16]

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Using an integrated Knowledge Translation (iKT) approach in which researchers and decisionmakers are partners in the research, we implement the PACT-HF model across several hospitals within our publicly funded health care system.[17] We use a stepped wedge randomized controlled trial design, a form of cross-over cluster randomized trial in which all clusters cross over from usual care to intervention in a randomized sequence. This design is ideal when all clusters wish to receive the intervention; when there are ethical reasons against with-holding the intervention from clusters due to prior established efficacy; or when a staggered approach to implementation is desirable for logistic reasons.[18-20] The trial is designed to be pragmatic, assessing whether services that are efficacious in the highly selective setting of clinical trials can be effective when grouped together and delivered in “real-world” settings.[21,22] If effective, there would be justification in the widespread implementation and public funding of the PACT-HF model. In this paper, we describe the rationale and study design of the PACT-HF pragmatic trial, which will assess the effectiveness of the PACT-HF model in: increasing time to the composite clinical outcomes of readmissions, emergency department (ED) visits, or death at 3 months, and composite readmissions or ED visits at 30 days following discharge; improving the patientreported outcomes of (i) Discharge Preparedness, (ii) Care Transitions Quality, (iii) HealthRelated Quality of Life (QOL), and (iv) Quality Adjusted Life Years (QALY); and reducing the 6month total health care resource use and costs among patients discharged from the hospital with HF.

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METHODS Trial registration and ethics. The trial is registered on clinicaltrials.gov (NCT02112227) and approved by the Research Ethics Boards of participating hospitals and community agencies.

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Selection of hospitals. We sent invitations for trial participation to 11 large, tertiary care cardiac centers across southern Ontario. Hospitals that agreed to implementation of the services following a Stepped Wedge design were included. Representatives of 1 hospital could not agree to a randomized sequence of implementation across sites; we excluded this hospital corporation, recognizing that a non-randomized sequence would introduce bias.

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Patient engagement and iKT approach . This trial was preceded by a single center prospective cohort study [28], in which the intervention was pilot-tested for feasibility among patients hospitalized for HF. Feedback from patients, clinicians, and decision-makers was incorporated into the design of the subsequent PACT-HF multi-center trial. For example, based on feedback from clinicians and decision-makers, we redesigned processes of care to facilitate efficient workflow, reliable communication, and seamless coordination of care within and across institutions. Patient preferences influenced the selection of questionnaires and patient-reported outcomes, and guided the delivery of PACT-HF services (e.g. involving caregivers in discharge planning, providing education in either a single or multiple sessions based on patient/caregiver preference).

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Prior to initiation of the multi-center trial, we met with clinicians and decision-makers across participating hospitals, community agencies, and regional health care networks to align priorities, understand baseline practices, and achieve consensus on the intervention, study design, funding, work flow, and timelines. Guided by the Process Redesign Framework for Implementation [23], we assessed baseline practices and planned ways to address care gaps prior to the intervention phase.

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Patients. We aim to include all patients hospitalized for HF during the intervention period at each hospital. Patients admitted with a most responsible diagnosis of HF are identified daily through screening of the hospital database as well as through referrals from clinicians. A trained nurse independently confirms the diagnosis of HF with Boston criteria [24] and/or serum thresholds of N-terminal prohormone brain natriuretic peptide (NT-proBNP) or BNP.[25] Patients who do not have HF, who die during hospitalization, or who are transferred to another hospital are excluded from the intervention. Intervention. A hospital nurse, who is trained to deliver the intervention, works with the medical team to: (1) assess the patient’s functional capacity using the EQ5D5L scale [36] and provide multidisciplinary care referrals as needed; (2) provide self-care education to the patient and, if available, an informal care giver (iCG). The education, guided by our previously validated tools, is delivered as a single 50-minute session or multiple shorter sessions based on patient / iCG preference, focusing on symptom-recognition and desired behavioural responses to maintain optimal health [26,27]; (3) provide a patient-centered discharge summary – including diagnosis, results of investigations, symptom-guided action plan, updated medication list, and follow-up arrangements - to the patient / iCG and the family physician (FP) on the day of discharge; (4)

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arrange for the patient to be seen by the FP within 7-days post-discharge; (5) risk stratify patients using the validated LACE index, and refer high-risk patients (with LACE score > 13) to nurseled home-care and multi-disciplinary HF clinic [28-30]. Patients referred to the home-care program receive a combination of structured home visits and telephone calls from a transitional care nurse for at least a month, until patients have been seen in the HF clinic. In the event of clinical deterioration, the home transitional care nurse guides the patient in following the discharge action plan and contacts the HF clinic for advice and an expedited appointment. See Figure 1 for further details of the study methodology and data collection.

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Training of personnel. The personnel delivering the intervention in the hospital and at home are existing nurses within the health care organization, but are reassigned to their transitional care role during the intervention phase. To facilitate collaborative networks for the shared care of patients across institutions, personnel from hospitals, HF clinics, and home-care agencies attend training symposia and networking events. Hospital and home-care personnel are offered further training and networking opportunities via clinical placements in their regional HF clinics.

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Routine care. Routine transitional care varies across participating hospitals. In a majority of hospitals, referrals for post-discharge services are left to the discretion of hospital physicians. In one of the ten hospitals, a nurse provides in-hospital education and post-discharge home visits to select patients with HF. A majority of hospitals have access to regional Heart Function clinics, and referrals to these clinics after hospitalization are left to the discretion of providers. Two hospitals do not have regional Heart Function clinics, but are developing such clinics to participate in the trial.

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Study design. The unit of randomization is the hospital. In this multicenter stepped wedge (SW) cluster RCT, the intervention is introduced to 10 Ontario hospitals in a randomized sequence (steps 2 through 11) until all hospitals receive it (Figure 2).[18-20] Once assigned the intervention, a hospital receives it for the remainder of the study. The intervention phase continues until 3 months after the last hospital has begun the intervention. Thus the duration of intervention at hospitals varies from a minimum of 2 months (Hospital 10) to 12 months (Hospital 1). There is a one-month interval between steps. We measure outcomes at participating hospitals the month prior to the intervention phase and whenever a new hospital (“step”) receives the intervention. At each step, there is a comparison of outcomes between the hospitals receiving the intervention (intervention wedge) and hospitals not to receiving it (control wedge); thus comparisons are made both within and between clusters at each step. We measure clinical and cost outcomes, health care resource uses and cost in all 10 hospitals and patient-centered outcomes in 8 hospitals (Figure 2). Thus, there will 11*10 = 110 measurements for the clinical and cost outcomes and 9*8 = 72 measurements for the patient-centered outcomes. All aspects of the study are pragmatic, designed to determine if the intervention is effective in real-world settings (Figure 3). Randomization. We randomize the start sequence of hospital sites using a number generator. Blinding. Patients and care providers receiving the intervention are not blinded to it. Clinical outcomes, health care resource uses and cost are obtained from a provincial database that is

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independent of this study, so adjudication of these outcomes is blinded. Statisticians are blinded to treatment allocation, as are research assistants collecting patient-reported outcomes.

PACT-HF nurse 1) screens hospital admission database for patients admitted with HF and 2) receives referrals from medical team on a daily basis

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PACT-HF nurse provides 1) Comprehensive assessment of patient and multidisciplinary linkages/referrals 2) Patient and informal caregiver self-care education 3) Patient-centred discharge summary, including action plan, to patient and FP 4) Follow-up appointment with FP within 7 days

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Excludes patients who - Do not have diagnosis of HF - Are transferred to another hospital - Died during hospitalization

High-risk criteria for 30-day readmission?

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PACT-HF nurse faxes referral to Heart Function Clinic and to home-care transition nurse

Primary Outcome: Time-to-first event of the composite of: (1) all-cause death, readmissions, or Emergency Department (ED) visit at 3 months post discharge; (2) all-cause readmissions or ED visit at 30 days post discharge. Secondary Outcomes: (1) patient-centered outcomes of Discharge Preparedness, Care Transitions Quality, Quality of Life (QOL), and Quality Adjusted Life Years (QALY), and (2) 6-month health system costs in patients discharged with HF.

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Patients are seen in HF clinic within 2-4 weeks and receive home care transition nurse visits and telephone calls from home-care agency nurses for 4-6 weeks

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Outcome Assessment

Figure 1. PACT-HF protocol and data collection Strategies to avoid contamination. To avoid changes in “routine care” prior to the intervention period, we conceal the randomization sequence and intervention initiation date from each participating hospital and community agency until the 3-month period prior to the intervention initiation date. We offer training sessions, networking opportunities, and tool-kits only in the month preceding the intervention’s start date at each hospital. Home-care transition nurses within regions have patients clustered to their care according to site of hospitalization to avoid contamination of care involving patients from sites yet to begin the intervention. There are verbal and contractual agreements with hospital and home care agencies so that the PACT-HF 6

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intervention is limited to patients receiving care from participating hospitals during the intervention period.

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Process-of-care Measures. To improve adherence to the PACT-HF intervention, we audit the following performance indicators at each site within 6 weeks of initiation, and provide feedback to partnering institutions: (1) % of patients admitted with HF who receive the PACT-HF intervention; (2) % of high-risk patients who are referred to home-care and HF clinics; (3) % of patients referred for nurse-led home care who receive a nurse visit; and (4) % of patients referred to HF clinics who are seen at the HF clinic. Thereafter, we measure process-of-care indicators in the 3rd and 6th month of the intervention phase, with feedback provided to the sites before the end of the following month. Benchmark quality targets, based on feasibility demonstrated in the pilot study, are set to optimize implementation of PACT-HF.

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Figure 2. PACT-HF Study Design Pattern where “0” represents sites receiving usual care and “1” represents sites receiving the intervention. There are 10 hospital sites, 11 steps, 55 control cross-sections, and 55 intervention cross-sections. The clear cells represent the control wedge and the shaded cells represent the intervention wedge. The cells within bolded borders sites depict hospitals collecting the patient-centered outcomes. There are 11*10 = 110 measurements for the clinical and cost outcomes and 9*8 = 72 measurements for the patient-centered outcomes.

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PACT-HF Rationale and Study Design

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Figure 3. Domain design choices in PACT-HF. Each of the 9 domains of trial design are scored from 1 (very explanatory, designed to demonstrate efficacy in “ideal conditions”) to 5 (very pragmatic, designed to assess effectiveness in “usual conditions”). The black lines represent scores from the PACT-HF trial and demonstrate that the trial design is closer to pragmatic than explanatory. Scoring tool for domain choices obtained from BMJ 2015;350:h2147

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Clinical outcomes. The primary outcomes, of major interest to decision-makers and clinicians, are hierarchically ordered as (1) time-to-first event of the composite of all-cause death, readmissions, or Emergency Department (ED) visits at 3 months post discharge; and (2) time-tofirst event of the composite of readmissions or ED visits at 30 days post discharge. Outcomes are measured relative to the index HF hospitalization, defined as the first unplanned hospitalization for HF in a participating hospital during the study period.[31] This definition is identical to the one used by the MOHLTC to evaluate hospital performance.[32]

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A fixed-sequence procedure is used for sequentially testing multiple clinical outcomes based on the proposed hierarchical order. The null hypothesis testing the first endpoint is required to be statistically significant at a nominal level of 5% before null hypothesis for the subsequent endpoint is tested; hierarchical testing continues until a null hypothesis fails to be rejected [33]. We will also measure the number of deaths, hospitalizations, and ED visits during the follow-up period. Patient-reported outcomes. In a nested sample of 8 hospitals, we explore the effectiveness of PACT-HF on patient-centered outcomes that were selected by a survey of patients on the basis of relevance and user-friendliness (Figure 2). The outcome measures were selected on the basis of input from patients during a preceding one-center pilot phase. A majority of patients selected the

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measures below over other validated disease-specific health-related quality of life questionnaires, which they deemed challenging to respond to over the telephone.

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(1) Patient Discharge Preparedness, as measured by the validated 11-item B-PREPARED telephone survey instrument, is administered 6 weeks after the patient’s discharge.[34] The scale assesses the domains of self-care information, services, and confidence. Based on a 3-point Likert Scale, scores range from 0 to 22; higher scores correlate with patient satisfaction and predict freedom from Emergency Department usage.[34] (2) Care Transitions Measure (CTM-3), a 3-item measure of coordination at hospital discharge care, is administered 6 weeks after the patient’s discharge. This score reflects the overall quality of the care transition, with higher scores indicating a better transition. Detailed scoring instructions for CTM-3 can be obtained at www.caretransitions.org.[35] (3) EQ5D5L, a preference-based HRQoL instrument measures health status in 5 dimensions: mobility, self-care, usual activities, pain and discomfort, and anxiety and depression.[36] Each dimension has 5 response levels: no problem (Level 1), slight problems (Level 2), moderate problems (Level 3), severe problems (Level 4), and extreme problem (Level 5). Respondents are also asked to rate their overall health status on a 0-100 visual analog scale (EQ-VAS) on which 0 represents the worst imaginable health state and 100 represents the best possible imaginable health state. Health utilities for the EQ5D5L anchored at 0 (dead) to 1(full health) are calculated using the scoring algorithm for Canadians developed by Xie et al.[37] We administer the EQ5D5L during the index admission and via telephone 6 weeks and 6 months after the patient’s hospital discharge. (4) QALY, is a measure of life duration weighed by quality of life. It is a measure that combines the effects of health interventions on mortality and morbidity. The area-under-the-curve approach with the EQ-5D-5L utilities and corresponding time duration will be used to calculate QALY will be calculated using the area-under-the-curve approach.[38]

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Health care resource uses and costs. We tabulate 6-month medical costs per patient from person-level health care expenditures, using the perspective of health care system. We obtain records of health care use paid for by the MOHLTC and estimate the 6-month inpatient and outpatient cost associated with each record using costing methods developed for health administrative data.[39] Cost information for sectors / institutions that have global budgets are determined through case-mix methodology.[39] Sectors that have fee payments associated with each use (e.g., drug cost or physician billing) have costs estimated directly. Home care expenditures are calculated as actual total billing charges per patient based on services used during total length of stay in the home care program from date of initiation until discharge from home care (entire home care episode). All costs are expressed in 2016 Canadian dollars, with inflation of past costs using healthcare specific yearly Consumer Price Index reported by Statistics Canada. Health sector cost for the population is the sum of all costs for the population captured in all applicable sectors. Data Collection. The hospital nurses trained in data collection collect baseline data from hospital charts and/or from patients. Research assistants not involved in providing care to patients, and who are blinded to the patient’s allocation, collect the 6-week and 6-month

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telephone surveys using a standard script. Questionnaires are mailed out to those patients who are not successfully reached via the telephone.

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A majority of clinical and outcomes data are sourced from administrative databases that are linked to each other and to patients via common encrypted identifiers. To identify the study cohort for analysis, we identify patients discharged with a most responsible diagnosis of HF using Ontario Congestive Heart Failure (CHF) cohort derived at the Institute for Clinical Evaluative Sciences (ICES). We use databases listed in Appendix 1 to identify health care resource use. Because the baseline characteristics and clinical outcomes of each HF patient discharged alive are documented in these databases, we are able to obtain the data linked to the index hospitalization without budgeting for follow-up of patients.

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Research assistants obtain process-of-care measures for audit-feedback from all 10 hospitals using data in hospital administrative lists, case report forms, and records from the home care agencies.

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Study Cohorts Derived from Administrative Data – Outcomes for the study cohort are derived from the CHF administrative database. We define this cohort as all patients in the participating hospitals discharged during the study period with a most responsible diagnosis (MRD) of heart failure. Patients are identified using ICD-10 diagnostic codes I42, J81, and I150 (150.0, 150.1, 150.9). Patients who are transferred to another hospital prior to discharge or die during hospitalization will be excluded.

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Analytic Plan. The analyses will follow a pragmatic intention-to-treat principle and are summarized in table 1. Patients are identified within the trial through study case report forms that are linked via their unique encrypted health card numbers to provincial databases. For patients without encrypted health card numbers, we use probabilistic analysis via initials, sex, postal code, birth date to link patients to their administrative data and health outcomes.

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For purposes of analysis, the intervention group will include all patients discharged from each participating hospital during the intervention phase with: 1) an index hospitalization for the MRD of HF, as identified in the administrative database; and 2) a diagnosis of HF confirmed by Boston criteria [24] and/or serum values of either NT-proBNP or BNP, regardless of whether they received PACT-HF services.[25] The control group will include all patients discharged from each hospital during the control phase with 1) an index hospitalization for the MRD of HF, as identified in the administrative database, and 2) clinical characteristics that match the patients in the intervention group (since they will not have had independent confirmation of HF by a study nurse). Baseline characteristics will be summarized using mean with standard deviation (SD) and median with interquartile range (IQR) for continuous variables, and counts with percentages for categorical variables. The p value to compare variables in intervention and ‘usual care’ groups will be obtained using generalized linear mixed regression (GLMM) models, with intervention and steps (i.e. time) as fixed-effects and hospitals (i.e. clusters) as random-effects to account for the stepped wedge design.

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The primary outcomes will be analyzed using shared frailty survival models with intervention and steps as fixed-effects and hospitals as random-effects. The hazard ratios with 95% confidence intervals will be used to report the effect of the PACT-HF intervention.

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We will perform the following sensitivity analysis for the primary outcomes: 1) We will expand the intervention and control arms to include all patients discharged during the intervention or control phases after an index hospitalization for the MRD of HF, as identified by the administrative databases (i.e. without independent verification of a clinical / biomarker definition of HF). This will adopt the intention-to-treat principle. 2) We will include in the intervention arm all patients who received PACT-HF services during the intervention phase, and find a matched cohort among the control phase patients, identified by the administrative databases, who did not receive the intervention (as-treatment analysis).

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We will summarize the B-prepared and CTM-3 scores using means with SDs and medians with IQRs. We will obtain p-values using the similar modeling approach as the one for baseline characteristics. We will summarize EQ5D5L and EQ-VAS indices using proportions, mean with SDs and medians with IQRs at each time point (baseline, 6 weeks, and 6 months after discharge). We will account for death as a competing risk in completion of the questionnaires. Missing outcomes will be imputed using the multiple imputation or other relevant imputation techniques. We will analyze the effect of PACT-HF on the patient reported outcomes using linear mixed regressions with repeated measures based on the complete dataset. Average changes in each outcomes compared to baseline will be compared between PACT-HF vs. usual care. The treatment effect on QALY will be assessed using mixed linear regression model, accounting for a stepped wedge design with baseline utility and other relevant covariates included as independent variables.

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We will also present categories of cost and overall costs using means with SDs and medians with IQRs. We will assess the normality assumption of the cost. If the normality assumption is violated, we will log transform the cost variables. Then we will evaluate the intervention using linear regression models, accounting for the stepped wedge design.

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We will conduct cost effectiveness analysis by comparing the incremental cost per readmission / death averted in the PACT-HF and usual care groups. We will conduct cost-utility analysis by comparing the incremental cost per QALY gained in the PACT-HF and usual care groups. The 95% confidence intervals will be identified using the non-parametric bootstrapping approach. We will use a cost effectiveness acceptability curve to present the probability of PACT-HF being the cost effective strategy over a range of maximum willingness to pay per QALY gained. Sample size and statistical power. To control the overall type I error rate at the 5% level for clinical endpoints, we will apply the fixed-sequence procedure to test the outcomes in hierarchical order. Assuming balanced cluster sizes, an event rate of 28% at 1 year, an intracluster correlation (ICC) of 0.01, and 320 index HF hospitalizations in each of the 10 hospitals during the study duration, we will have 80% power and a Type I error rate of 5% to detect a relative risk reduction of 25% based on a log-rank test.[33,40] Assuming 350 patients admitted per year at each hospital collecting patient-reported outcomes,

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measurement across 9 steps, a 65% questionnaire completion rate, a death rate of 4% from discharge to 6 weeks, and additional 10% of death rate from 6 weeks to 6 months, there will be a sample size of 164 per hospital from discharge to 6 weeks and 148 per hospital from 6 weeks to 6 months for the patient-reported outcomes.

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Patient discharge preparedness. Assuming an ICC of 0.01 and 5% statistical significance, a study with 164 index HF patients across 8 hospitals will have 99% power to detect a 1.5 point difference with a SD of 4 in the B-PREPARED score, and a 7 point difference with a SD of 20 in the CTM-3 score.

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Quality of life. Assuming an ICC of 0.01 and 5% statistical significance, a study with 148 index HF patients across 8 hospitals will have 90% power to detect a 0.05 point difference (SD 0.2) in the EQ-5D-5L index at 6 months.

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Cost (available for all the hospitals). Assuming an ICC of 0.01 and 5% statistical significance, a study with 320 index HF patients across 10 hospitals will have 92% power to detect a difference in means of 15% with a mean ± SD of $36,600±4,000 in log scale.

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All statistical analyses will be conducted using SAS Version 9.4 for UNIX (SAS Institute Inc., Cary, North Carolina).

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Table 1. Outcomes and Analysis Summary Table Outcome Measure

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Outcome 1) Primary

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Composite of time to all- a) Time-to-first event in the 3 cause death, months post discharge readmissions, or Emergency Department (ED) visit Composite of time to all- a) Time-to-first event in the 3 cause readmissions or months post discharge ED

Methods of Analysis Shared frailty survival models with intervention and steps as fixed effects, and hospitals as random effects. Shared frailty survival models with intervention and steps as fixed effects, and hospitals as random effects.

2) Secondary

Patient Discharge Preparedness

Quality of Care Transitions Health-Related Quality of Life

11-item B-PREPARED questionnaire, administered 6 weeks post-discharge; satisfaction scores range from 0-22 3-item Care Transitions Measure (CTM-3), administered 6 weeks after the discharge a) EQ5D5L, a measure of health status in 5 dimensions, each with

Linear mixed regression accounting for the stepped wedge design. Linear mixed regression accounting for the stepped wedge design. Linear mixed regression with repeated measures

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accounting for the stepped wedge design.

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Linear mixed regression accounting for baseline utility and other relevant covariates

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Quality-Adjusted Life Years

5 response levels ranging from no problems to extreme problems b) EQ-VAS, a scale ranging from 0100 where 0 represents the worst imaginable and 100 represents the best imaginable health status Scales are administered during hospitalization, and at 6 weeks and 6 months post-discharge QALY, a measure which combines the effect of the intervention on morbidity and mortality. Calculated using area under the curve of the EQ5D5L score vs. time graph. a) Cost-effectiveness, measured 6 months post-discharge

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Costs

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b) Cost-utility, measured 6 months post-discharge

a) Incremental cost per readmission/death averted in the PACT-HF and usual care groups b) Incremental cost per QALY gained in the PACT-HF and usual care groups

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DISCUSSION

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Funding. This study is funded by peer-reviewed grants from Canadian Institutes of Health Research (#135917) and Ontario Ministry of Health and Long Term Care (MOHLTC) Health System Research Fund (#6686). HGCV receives salary support from MOHLTC and from Hamilton Health Sciences Career Award. IDG is recipient of a Canadian Institutes of Health Research Foundation Grant (FDN #143237). DTK is supported by an Ontario Mid-Career Scientist Award from the Heart and Stroke Foundation of Canada, Ontario’s office.

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Knowledge Translation (KT) is an iterative process that includes synthesis, dissemination, exchange and application of knowledge to improve health, provide effective health services and products, and strengthen the health care system.[43] The PACT-HF model, informed by a prior comparative effectiveness meta-analysis of transitional care services, translates knowledge to action by implementing in real world settings those services that improved clinical outcomes in the tightly-controlled settings of RCTs. All patients hospitalized with HF are offered the intervention, and services are titrated to risk of readmission and death to conserve resources that are finite in our publicly funded health care system. The study is designed to be pragmatic, assessing whether PACT-HF can improve outcomes under the usual conditions in which patients receive care. By measuring clinical outcomes of readmission, ED visits, or death following hospital discharge; patient-reported outcomes of discharge preparedness, quality of transitional care, and quality adjusted life years (QALY); and

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total costs of medical care and cost per QALY, we include outcomes that are meaningful to clinicians, patients, and the health care system. Results from this pragmatic trial will be generalizable to hospitals across Canada and other high-income countries burdened with avoidable readmissions in HF. Results from this trial may also be relevant to the management of other complex, chronic diseases associated with high health care utilization.

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We anticipate that the complexity of the intervention – requiring integration of care across institutions and regions that often function independently of each other in our health care system – will pose another significant challenge. We have planned for this challenge by engaging clinicians and decision-makers in all aspects of the research, by creating networks for collaboration and coordination of care between institutions, and by actively preparing each site for the intervention phase (ensuring that the required infrastructure, personnel, and processes are in place prior to the start date). The staggered approach to implementation in the stepped-wedge (SW) randomized cluster RCT design is ideal in this setting as it will allow for individual sites to receive adequate preparation prior to the intervention start date.

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The diagnosis of HF in clinical situations can be challenging, and patients presenting with HF may initially receive other diagnoses. Furthermore, patients presenting to the hospital with other conditions can subsequently develop HF during hospitalization. To facilitate accurate diagnoses on admission and include patients who develop HF after admission, we screen the hospital database daily for patients diagnosed with HF, use validated Boston diagnostic criteria and pointof-care NT-proBNP testing [24,25], and actively solicit referrals from inpatient clinicians.

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With a view to patient-centeredness, we have incorporated patient preference into the delivery of PACT-HF services, selection of patient-reported outcomes, and selection of questionnaires. During the pilot phase, patients highlighted the importance of involving their informal caregivers in discharge planning; having self-care education offered as either single or divided sessions; and having access to multidisciplinary team members during hospitalization. They selected questionnaires that evaluated important outcomes and that were brief and user-friendly (e.g. CTM-3, EQ5D5L) over disease-specific HrQOL scales that they considered challenging to complete via the telephone.

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KT studies pertaining to quality improvement are typically constrained in study quality, often employing before-after methodology that is subject to confounding.[41,42] In adopting the robust SW study design, we will test the effectiveness of our model of care while offering the intervention to all participating hospitals.[18-20] In applying a pragmatic approach to all aspects of this clinical trial (figure 3), our results will reflect effectiveness in usual care settings and assess whether publicly-funded resources should be used to support such a model of care. By using administrative databases rather than clinical follow-up to establish clinical and cost outcomes, we will maintain cost-efficiency within our trial. Our embedding of a clinical trial within administrative databases, collection of patient-reported outcomes, and use of time-toevent analysis as well as continuous outcomes in the context of a SW trial will advance the science of KT and inform the design of other KT studies.

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Acknowledgements. We are indebted to Daniel Ball, Elizabeth Banting, Maria De Leon, Nancy Elford, Jeanine Harrison, Elena Holt, Gail Johnson, Colleen Lackey, Bill Liu, Roberta Napoleoni, Tracey Renton, Donna Richardson, Teresa Smith, Nazneen Solkar, Anne Stephens, and Shannon Tyrrell for their important roles in the PACT-HF trial. This work is dedicated to the memory of Aubrey Ignatius Van Spall, beloved father and friend.

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REFERENCES 1. Lloyd-Jones DM, Larson MG, Leip EP, et al. Framingham Heart Study. Lifetime risk for

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developing congestive heart failure: the Framingham Heart Study. Circulation 2010;121:e1-e170. 2. Levy D, Kenchaiah S, Larson MG, et al. Long-term trends in the incidence of and survival with heart failure. N Engl J Med 2002;347(18):1397. 3. Chow CM, Donovan L, Manuel D, et al. Canadian Cardiovascular Outcomes Research Team. Regional variation in self-reported heart disease prevalence in Canada. Can J Cardiol 2005;21(14):1265-71. 4. Jencks SF, Williams MV, and Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med 2009;360(14):1418. 5. Desai AS and Stevenson LW. Rehospitalization for heart failure: predict or prevent? Circulation 2012;126(4):501-6. (instead of http://www.heartandstroke.com/site/c.ikIQLcMWJtE/b.3483991/k.34A8/Statistics.htm. Accessed Oct 30, 2011.) – broken link – unable to find similar updated document from heart and stroke 6. Stewart S, Jenkins A, Buchan S, et al. The current cost of heart failure to the National Health Service in the UK. Eur J Heart Fail 2002;4:361-371. 7. Kaul P, McAlister FA, Ezekowitz JA et al. The Evolving Economic Burden of Heart Failure In Alberta. Circulation 2010; Abstract A13165. 8. Discharge Abstract Database (DAD), Ministry Of Health and Long Term Care, SAS Server. Extracted August 29, 2012. 9. Solomon SD, Dobson J, Pocock S, et al. Candesartan in Heart failure: Assessment of Reduction in Mortality and morbidity (CHARM) Investigators. Influence of nonfatal hospitalization for heart failure on subsequent mortality in patients with chronic heart failure. Circulation 2007;116(13):1482. 10. Phelan D, Smyth L, Ryder M, et al. Can we reduce preventable heart failure readmissions in patients enrolled in a Disease Management Programme? Ir J Med Sci 2009;178(2):167-71. 11. Makaryus AN and Friedman EA. Patients’ understanding of their treatment plans and diagnosis at discharge. Mayo Clin Proc 2005;80:991-4. 12. Hernandez AF, Greiner MA, Fonarow, GC, et al. Relationship Between Early Physician Follow-up and 30-Day Readmission Among Medicare Beneficiaries Hospitalized for Heart Failure. JAMA 2010;303(17):1716-1722. 13. Van Spall HG, Rahman T, Mytton O, et al. Comparative effectiveness of transitional care services in patients discharged from the hospital with heart failure: a systematic review and network meta-analysis. Eur J Heart Fail 2017; doi:10.1002/ejhf.765. 14. Graham IDG, Logan J, Harrison M, eta al. Lost in Knowledge Translation: Time for a map? Journal of Continuing Education in the Health Professions 2006;26:13-24. 15. Kodner DL and Spreeuwenberg C. Integrated care: meaning, logic, applications, and implications—a discussion paper. Int J Integr Care [serial online] 2002 Nov 14;2 16. Institute of Medicine. "Crossing the Quality Chasm: A New Health System for the 21st Century". 2012. 26 November 2012. 16

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17. Canadian Institutes of Health Research. Guide to knowledge translation planning at CIHR and end-of-grant approaches. Available from: http://www.cihrirsc.gc.ca/e/45321.html [Accessed March 24, 2017]. 18. Brown CA and Lilford RJ. The stepped wedge design: a systematic review. BMC Med Res Methodol 2006;6:54. 19. Woertman W, de Hoop E, Moerbeek M, et al. Stepped wedge designs could reduce the required sample size in cluster randomized trials. J Clin Epidemiol 2013;66(7):752-8. 20. Hussey MA and Hughes JP. Design and analysis of stepped wedge cluster randomized trials. Contemporary Clinical Trials 2007;28:182–191. 21. Loudon K, Trewek S, Sullivan F, et al. The PRECIS-2 tool: designing trials that are fit for purpose. BMJ 2015;350:h2147. 22. Van Spall HGC, Toren A, Kiss A, Fowler RA. Eligibility criteria of randomized controlled trials published in high-impact general medical journals: a systematic sampling review. JAMA 2007; 297(11):1233-40. 23. Smith LR, Ashok M, Dy SM, et al. Contextual Frameworks for Research on the Implementation of Complex System Interventions. Rockville, MD, US: Agency for Healthcare and Quality (US); 2014 p.8-26. 24. White M, Garbez R, Carroll M, Brinker E, et al. Is "teach-back" associated with knowledge retention and hospital readmission in hospitalized heart failure patients? J Cardiovasc Nurs 2013;28(2):137-46. 25. Roberts Emmert, Ludman Andrew J, Dworzynski Katharina, Al-Mohammad Abdallah, Cowie Martin R, McMurray John J V et al. The diagnostic accuracy of the natriuretic peptides in heart failure: systematic review and diagnostic meta-analysis in the acute care setting BMJ 2015; 350 :h910 26. Harrison MB, Graham ID, Logan J, et al. Evidence to practice: pre-post implementation study of a patient/provider resource for self-management with heart failure. Int J Evid Based Healthc 2007;5(1):92-101. 27. Harrison MB, Toman C, and Logan J. Managing Congestive Heart Failure. Heart and Stroke Foundation; 2002. Available from: https://www.hsnsudbury.ca/PortalEn/Portals/8/Managing_CHF.pdf. [Accessed March 24, 2017] 28. Yazdan-Ashoori P, Lee SF, Ibrahim Q, et al. Utility of the LACE index at the bedside in predicting 30-day readmission or death in patients hospitalized with heart failure. Am Heart J 2016;179:51-8. 29. van Walraven C, Wong J, and Foster AJ. LACE+ index: extension of a validated index to predict early death or urgent readmission after hospital discharge using administrative data. Open Med 2012; 6(3):e80-90. 30. Wang H, Robinson RD, Johnson C, et al. Using the LACE index to predict hospital readmissions in congestive heart failure patients. BMC Cardiovasc Disord 2014;14:97. 31. Yale New Haven Health Services Corporation/Center for Outcomes Research and Evaluation (YNHHSC/CORE). Measures updates and specifications report: hospital-level 30-day risk-standardized readmission measures for acute myocardial infarction, heart failure, and pneumonia. Prepared for: Centers for Medicare and Medicaid Services (CMS); 2013. Available at: 17

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http://www.qualitynet.org/dcs/ContentServer?c5Page&pagename5QnetPublic%2FPage% 2FQnetTier3&cid5122877437100. 32. Health Quality Ontario; Ministry of Health and Long-Term Care. Quality-based procedures: clinical handbook for heart failure (acute and postacute). Toronto: Health Quality Ontario; 2015 February. 78 p. Available from: http://www.hqontario.ca/evidence/evidence-process/episodes-of-care#community-chf. [Accessed March 24, 2017] 33. Hemming K, Taljaard M.Sample size calculations for stepped wedge and cluster randomised trials: a unified approach. J Clin Epidemiol. 2016; 69:137-46. 34. Graumlich JF, Novotny NL, and Aldag JC. Brief scale measuring patient preparedness for hospital discharge to home: psychometric properties. J Hosp Med 2008;3(6):446-54. 35. Care Transitions Program. Tools and Resources. Available from: http://caretransitions.org/tools-and-resources/. [Accessed March 24, 2017] 36. Mandy van Reenen and Bas Janssen. EQ-5D-5L User Guide. EuroQol Group; 2015. Available from: http://www.euroqol.org/fileadmin/user_upload/Documenten/PDF/Folders_Flyers/EQ-5D5L_UserGuide_2015.pdf. [Accessed March 24, 2017] 37. Xie F, Pullenayegum E, Gaebel K, et al. A time trade-off-derived value set of the EQ-5D5L for Canada. Med Care 2016;54(1):98-105. 38. Whitehead SJ, Ali S. Health outcomes in economic evaluation: the QALY and utilities. Br Med Bull 2010;96:5-21. 39. Wodchis WP, Bushmeneva K, Nikitovic M, McKillop I. Guidelines on Person-Level Costing Using Administrative Databases in Ontario. Working Paper Series. Vol 1. Toronto: Health System Performance Research Network; 2013. 40. Woertman W, de Hoop E, Moerbeek M, Zuidema SU, Gerritsen DL, Teerenstra S. Stepped wedge designs could reduce the required sample size in cluster randomized trials. J Clin Epidemiol. 2013 Jul;66(7):752-8. 41. Van Spall HGC, Shanbhag D, Gabizon I, et al. Effectiveness of implementation strategies in improving physician adherence to guideline recommendations in heart failure: a systematic review protocol. BMJ Open 2016;6(3):e009364. 42. Eccles M, Grimshaw J, Campbell M, et al. Research designs for studies evaluating the effectiveness of change and improvement strategies. Qual Saf Health Care. 2003;12(1):47-52.http://www.cihr-irsc.gc.ca/e/29418.html#2. Accessed October 17, 2017.

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Appendix 1. Provincial databases used for baseline characteristics and outcomes

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Ontario Home Care Administrative System (OHCAS)

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National Rehabilitation Reporting System (NRS) Ontario Mental Health Reporting System (OMHRS)

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Office of the Registrar General - Deaths (ORGD) Management Information System (MIS) GAPP decision support systems (GAPP) Ontario Case Costing Initiative (OCCI) Estimated Schedule of Benefits (ESTSOB)

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Patients enrolled in primary care groups Provider billings and laboratory services Drugs Devices Demographic variables Long-term and complex continuing care Encounters between client, service providers, and the Ontario Community Care Access Centres (CCACs) Demographic, clinical, functional and resource utilization information on clients served by publicly funded home care programs in Canada Demographic, diagnostic and, treatment information about clients of the Ontario Home Care Program Rehabilitation

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Resident Assessment Instrument-Home Care (RAI-HC)

Inpatient services Emergency department and outpatient clinics

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Canadian Institute for Health InformationDischarge Abstract Database (CIHI-DAD) Same Day Surgery Database (SDS) National Ambulatory Care Reporting System (NACRS) Client Agency Program Enrolment (CAPE) Ontario Health Insurance Plan (OHIP) Ontario Drug Benefit (ODB) Assistive Devices Program (ADP) Ontario Census Area Profiles (CENSUS) Continuing Care Reporting System (CCRS) Home Care Database (HCD)

Information Accessed Patients eligible to receive publicly-funded health care services Acute care and rehabilitation discharges

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Database Registered Persons Database (RPDB)

Mental and physical health, social supports and service use, as well as care planning, outcome measurement, and quality improvement for all individuals receiving adult mental health services in Ontario. Deaths and vital statistics Financial and statistical data for the entire hospital sector Physician payments Patient-specific case costing data Price associated with each OHIP fee code and suffix

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