DOI: 10.1161/CIRCULATIONAHA.115.018006
Do Self-Management Interventions Work in Patients With Heart Failure? An Individual Patient Data Meta-Analysis Running title: Jonkman et al.; Self-management interventions for heart failure Nini H. Jonkman, MSc1; Heleen Westland, RN, MSc1; Rolf H.H. Groenwold, MD, PhD2; Susanna Ågren, RN, PhD3,4; Felipe Atienza, MD, PhD5; Lynda Blue, RN6; Pieta W.F. BrugginkAndré de la Porte, MD, PhD7; Darren A. DeWalt, MD, MPH8; Paul L. Hebert, PhD9; Michele Heisler, MD, MPA10; Tiny Jaarsma, RN, PhD11; Gertrudis I.J.M. Kempen, PhD12; Marcia E. Leventhal, MSN, RN13; Dirk J.A. Lok, MD, PhD7; Jan Mårtensson, RN, PhD14; Javier Muñiz, MD, PhD15; Haruka Otsu, RN, PhD16; Frank Peters-Klimm, MD17; Michael W. Rich, MD18; Barbara Riegel, RN, PhD19; Anna Strömberg, RN, PhD4,20; Ross T. Tsuyuki, PharmD, MSc21; Dirk J. van Veldhuisen, MD, PhD22; Jaap C.A. Trappenburg, PhD1; Marieke J. Schuurmans, RN, PhD1; Arno W. Hoes, MD, PhD2 1
Dept of Rehabilitation, Nursing Science and Sports, University Medical Center Utrech Utrecht, ht, t N Netherlands; ethe et herl he rlan rl ands an ds; Julius Center for Health Sciences and Primary Care, University Medical Center Utrech Utrecht, ht, N Netherlands; etthe herl rlan rl ands an ds;; ds 3 Dept of Medical and Health Sciences and Departmentt of Cardiothoracic Surgery, Linköping University, Sweden; 4Dept of Medical and Health Sciences, Division of Nursing Science, Linköping University, Swed den en;; 5De Sweden; Dept D p off Ca pt Cardiology, Hospital General Unive Universitario vers ve rsitario Gregorio Mara rs Marañón, añó ñón, Madrid, Spain; 6British Heart Hear He a t Foundation, Foun und un dation, Glasgow, United Kingdom; 7Dep Dept pt of Cardiology,, De D Deventer ventter Hospital, Netherlands; 8 Division D ivision off General Gene Ge neraal Medicine ne Medi Me dici di c nee and ci and Clinical Cli lini nica ni c l Epidemiology, Epid Ep i em id miology gyy, University Univ Un iverrsi iv sity ty y ooff No Nort North rth Ca Carolina, aro roli lina li na,, Ch na Chapel hap apel el H Hill, ill, il l NC; N C; 9Dept of Health Health He h Services, Ser e viccess, University Uni niv ni verssity y of Washington, Washiing gton, n Sea Seattle, eatttle, WA ea WA;10D Dept ep pt of of IInternal ntern nall Med Medicine, ediicinee, ed University Michigan, Arbor, MI; Un ooff M icchigan an, Ann nA rbor,, M I; 11De Dept eptt of So Social all aand nd W nd Welfare elfaaree Stud Studies, diees, L Linköping inköpin ng Un University, niv versitty,, 12 Swed Sw eden ed en;; De en Sweden; Dept ept ooff H Health eaalth al S Services ervi er vice vi cess Re ce Res Research, sear arch ar ch, CA ch CAPH CAPHRI PHR PH RI Sc Scho School hoo ho ol ffor o P or Public ubli ub l c He li Heal Health a th h aand n P nd Primary rimaary C ri Care, arre, 13 Maastricht University, Netherlands; Insti Institute itute of Nursing Science, University of Basel, Switzerland; 14 Dept of Nursing Science, Jönköping University, Sweden; 15Instituto Universitario de Ciencias de la Salud, Universidad de A Coruña and INIBIC, Spain; 16Graduate School of Health Sciences, Hirosaki University, Aomori, Japan; 17Dept of General Practice and Health Services Research, University Hospital Heidelberg, Germany; 18Cardiovascular Division, Washington University School of Medicine, St. Louis, MO; 19School of Nursing, University of Pennsylvania, Philadelphia, PA; 20Dept of Cardiology, Linköping University, Sweden; 21Division of Cardiology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada; 22Dept of Cardiology, University Medical Center Groningen, Netherlands 2
Address for Correspondence: Nini H. Jonkman, MSc Department of Rehabilitation, Nursing Science & Sports University Medical Center Utrecht Heidelberglaan 100 3508GA, Utrecht, Netherlands Tel/Fax: +31-613244760 E-mail: [email protected] Journal Subject Terms: Heart Failure; Epidemiology; Behavioral/Psychosocial Treatment
1 Downloaded from http://circ.ahajournals.org/ at University of York (yku) / England on February 21, 2016
DOI: 10.1161/CIRCULATIONAHA.115.018006
Abstract
Background—Self-management interventions are widely implemented in care for patients with heart failure (HF). Trials however show inconsistent results and whether specific patient groups respond differently is unknown. This individual patient data meta-analysis assessed the effectiveness of self-management interventions in HF patients and whether subgroups of patients respond differently. Methods and Results—Systematic literature search identified randomized trials of selfmanagement interventions. Data of twenty studies, representing 5624 patients, were included and analyzed using mixed effects models and Cox proportional-hazard models including interaction terms. Self-management interventions reduced risk of time to the combined endpoint HF-related hospitalization or all-cause all cause death (hazard ratio [HR], 0.80; 95% confidence interval [C [[CI], I], 0.71 0.710.89), time to HF-related hospitalization (HR, 0.80; 95%CI, 0.69-0.92), and impr rov oved ed 112-month 2-mo 2month mo improved HF-related quality of life (standardized mean difference 0.15; 95%CI, 0.00-0.30). Subgroup analysis revealedd a protective effect of self-manag f gem e ent on number of H F-related hospital days self-management HF-related n ppatients at atients 80 years 636 00.85 .855 .8 (0.63-1.15) (0 .63-1.15 - 5) Total days HF-related RLOS years 139 HF-rela late la tedd te R RL OS 5 8892 92 00.86 .866 .8 80 years 232 0.96 (0.31-2.97) General outcomes Generic QoL-PCS MD 8 1739 0.95 80 years 296 1.13 (-2.01-4.26)
p-value Subgroups Depression for interaction 0.77
0.65
0.88 88
0.03 03
0.63
25 Downloaded from http://circ.ahajournals.org/ at University of York (yku) / England on February 21, 2016
n patients
Treatment effect (95% CI)
p-value for interaction
No/mild
1274
0.12
Moderate/ severe
696
0.81 (0.66-0.99) 1.05 (0.81-1.36)
No/mild
11832 18 32 2
0.41
Moderate/ severe
772
00.16 .1 16 (0 .14 14-0.19 19)) 19 (0.14-0.19) 0.25 (-0.01-0.50)
No/mild No//mild
1274
0.64
Moderate/ M o er od e ate/ severe seve v ree
6966 69
0.92 (0.71-1.18) (0.7 (0 . 1-1. .7 1.18 1. 18) 18 1.00 1.00 ((0.74-1.35) (0 .74-1.35 3 )
No/mild No/m No /mil /m ildd il
2228 28
0.94
Moderate/ severe
39
00.49 .449 .49 (0.13-1.84) 0.37 (0.01-9.70)
No/mild
796
0.45
Moderate/ severe
191
0.41 (0.09-0.73) -1.29 (-5.67-3.09)
DOI: 10.1161/CIRCULATIONAHA.115.018006
Generic QoL-MCS 12 months
MD
8
1739
0.27 80 years 296 -1.19 (-5.62-3.24) Mortality HR 14 4312 0.91 80 yearss 80 yea 856 0.79 (0.62-1.00) All-cause hospitalization HR 80 years 717 0.79 (0.64-0.97) Total days all-cause RLOS 80 > 0 years >8 453 0.77 0. 77 (0.49-1.20) (0.4 .499-1.20)) CI indicates cconfidence onfi nfi fide denc de ncee interval; nc inte in terv te rval; HF rv HF, hear heart ear artt fa failur failure; ure; ur e; HR, hhazard a ardd ra az rati ratio; t o; M MCS, CS, me CS ment mental n al ccomponent ompo om pone po nent ne n sscale; c le ca le;; MD, M , me MD m mean an n dif difference; iffe if fere fe renc enc nce; P PCS, C , pphysical CS hysi hy sica si call component ca compon co onentt scale; on scal sc ale; al e QoL, e; QoL quality of life; RLOS, relative length of stay; and SMD, standardized mean difference.
26 Downloaded from http://circ.ahajournals.org/ at University of York (yku) / England on February 21, 2016
DOI: 10.1161/CIRCULATIONAHA.115.018006
Figure Legend:
Figure 1. Forest plot of effects of self-management interventions on heart failure-related quality of life, heart failure-related hospitalization, and all-cause mortality. CI indicates confidence interval; HR, hazard ratio; and SMD, standardized mean difference.
27 Downloaded from http://circ.ahajournals.org/ at University of York (yku) / England on February 21, 2016
Downloaded from http://circ.ahajournals.org/ at University of York (yku) / England on February 21, 2016
Do Self-Management Interventions Work in Patients With Heart Failure? An Individual Patient Data Meta-Analysis Nini H. Jonkman, Heleen Westland, Rolf H.H. Groenwold, Susanna Ågren, Felipe Atienza, Lynda Blue, Pieta W.F. Bruggink-André de la Porte, Darren A. DeWalt, Paul L. Hebert, Michele Heisler, Tiny Jaarsma, Gertrudis I.J.M. Kempen, Marcia E. Leventhal, Dirk J.A. Lok, Jan Mårtensson, Javier Muñiz, Haruka Otsu, Frank Peters-Klimm, Michael W. Rich, Barbara Riegel, Anna Strömberg, Ross T. Tsuyuki, Dirk J. van Veldhuisen, Jaap C.A. Trappenburg, Marieke J. Schuurmans and Arno W. Hoes Circulation. published online February 12, 2016; Circulation is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231 Copyright © 2016 American Heart Association, Inc. All rights reserved. Print ISSN: 0009-7322. Online ISSN: 1524-4539
The online version of this article, along with updated information and services, is located on the World Wide Web at: http://circ.ahajournals.org/content/early/2016/02/11/CIRCULATIONAHA.115.018006
Data Supplement (unedited) at: http://circ.ahajournals.org/content/suppl/2016/02/12/CIRCULATIONAHA.115.018006.DC1.html
Permissions: Requests for permissions to reproduce figures, tables, or portions of articles originally published in Circulation can be obtained via RightsLink, a service of the Copyright Clearance Center, not the Editorial Office. Once the online version of the published article for which permission is being requested is located, click Request Permissions in the middle column of the Web page under Services. Further information about this process is available in the Permissions and Rights Question and Answer document. Reprints: Information about reprints can be found online at: http://www.lww.com/reprints Subscriptions: Information about subscribing to Circulation is online at: http://circ.ahajournals.org//subscriptions/
Downloaded from http://circ.ahajournals.org/ at University of York (yku) / England on February 21, 2016
Jonkman, et al. – Self-management interventions for heart failure
SUPPLEMENTAL MATERIAL Do self-management interventions work in patients with heart failure work? An individual patient data meta-analysis
Page
Content
2
Supplemental Methods: Statistical analysis plan
6
Supplemental Table 1: Effects of self-management interventions on subordinate outcomes in patients with heart failure included in the individual patient data meta-analysis.
7
Supplemental Table 2: Effects of self-management interventions on subordinate outcomes in subgroups of patients with heart failure included in the individual patient data meta-analysis.
8
Supplemental Table 3: Effects of self-management interventions on main outcomes in subgroups of patients with heart failure included in the individual patient data meta-analysis.
9
Supplemental Table 4: Sensitivity analysis on main outcomes by including published main effects of eligible studies without available individual patient data.
10
Supplemental Table 5: Sensitivity analysis on main outcomes by excluding trials with enhanced usual care in the comparison group.
11
Supplemental References
Jonkman, et al. – Self-management interventions for heart failure Supplemental Methods: Statistical analysis plan This document contains the plan for the statistical analysis for the individual patient data (IPD) meta-analysis in heart failure (HF) patients. Input from the conference calls on March 20 th, 2014 and March 31st, 2014 and email contact has been processed in the statistical plan presented in this document. A schematic overview of the statistical analysis is present in Figure 1. Each step will be explained in more detail in the subsequent paragraphs. For all statistical analyses, the software R for Windows version 3.1.1 (R Development Core Team. Released 2013. Vienna, Austria: R Foundation for Statistical Computing) will be used.
Figure 1: Steps of the statistical analysis of patient-specific determinants of self-management interventions.
1. Imputation of missing data To address bias due to missing data, we will impute missing data using multiple imputation by chained equations (MICE).1 The MICE algorithm accounts for the order in which the values of separate variables are predicted through chained equations. To address the uncertainty of just one single imputation, MICE creates multiple imputations, resulting in multiple imputed datasets. The imputation will be performed according to the following principles: • Missing values will only be imputed within studies: this implies that only the correlation between variables available within one study will be used to estimate the missing values in that particular study • All available variables (except patient identifiers) will be used to estimate missing values • Multiple imputation will be used to estimate missing values for patient characteristics and outcomes • Multiple imputation will be performed 25 times, resulting in 25 imputed datasets • As a result, all analyses will be carried out 25 times. Results will be pooled using Rubin’s rule for the final results.2 A complete-case analysis, using only the available patient data, will be performed as a sensitivity analysis to assess the impact of imputing data (see ‘4. Sensitivity analyses’).
2. Analysis of main effects All data will be analyzed according to the intention-to-treat principle. A so-called one-stage approach will be used, where all patients are analyzed simultaneously in one model while clustering of observations within studies is taken into account.3
2
Jonkman, et al. – Self-management interventions for heart failure The present study will analyze the following main outcome measures: Composite of time to first disease-related hospital admission or all-cause death; Change in health-related quality of life (HRQoL) at 12 months, compared to baseline; A distinction will be made between disease-specific and generic HRQoL to address the different instruments used by original studies Time to first disease-related hospital admission; Total number of days spent in hospital for HF at 12 months. Time to all-cause death; Time to first all-cause hospital admission; Total number of days spent in hospital for any cause at 12 months. Additionally, the following subordinate outcomes measures will be analyzed: Change in health-related quality of life (HRQoL) at 6 months, compared to baseline; A distinction will be made between disease-specific and generic HRQoL Total number of days spent in hospital for HF at 6 months and at 12 months; Hospitalized for HF at 6 months; All-cause mortality at 6 months and at 12 months; Hospitalized for any cause at 6 months and at 12 months; Total number of days spent in hospital for any cause at 6 months. For time-to-event data, effects of self-management will be quantified by estimating hazard ratios (HR) and 95% confidence interval (CI). Cox proportional-hazard models will be used to analyze the data, including a cluster statement to allow inter study variability. For binary outcome data (mortality, all-cause and disease-related hospital admissions), risk ratios (RR) and 95% CI will be estimated using log-binomial mixed effects models. Effects on continuous outcomes (HRQoL) will be quantified by mean differences and 95% CI and will be estimated using linear mixed effects models. Effects on total length of hospital stay will be analyzed with negative binomial mixed effects models to model overdispersion in the data. In the (generalized) linear mixed effects models, random intercepts and random slopes will be included to take clustering within studies into account.
3. Patient-specific effect modifiers The aforementioned models will be extended to study effect modification by patient characteristics. Effect modification implies that the effect of the intervention on an outcome differs depending on the value of a third variable, the effect modifier. As such, we will be able to identify subgroups of patients in which selfmanagement interventions work best. Interaction terms will be included in the final model resulting from the previous step, which includes the significant program determinants. We have selected clinically relevant patient characteristics as potential effect modifiers, these are presented in Table 1. Numbers of patients differ per variable due to the fact that some baseline variables have not been collected in one (or more) studies. We would like to categorize the variables to create relevant subgroups for the interpretation of findings. This has been discussed extensively during the conference calls, and the proposed categories are a result of the discussions. Like the analysis of program characteristics, patient characteristics with p35% LVEF (based on ESC Guidelines 2012)
1.
Do Self-Management Interventions Work in Patients With Heart Failure? An Individual Patient Data Meta-Analysis Running title: Jonkman et al.; Self-management interventions for heart failure Nini H. Jonkman, MSc1; Heleen Westland, RN, MSc1; Rolf H.H. Groenwold, MD, PhD2; Susanna Ågren, RN, PhD3,4; Felipe Atienza, MD, PhD5; Lynda Blue, RN6; Pieta W.F. BrugginkAndré de la Porte, MD, PhD7; Darren A. DeWalt, MD, MPH8; Paul L. Hebert, PhD9; Michele Heisler, MD, MPA10; Tiny Jaarsma, RN, PhD11; Gertrudis I.J.M. Kempen, PhD12; Marcia E. Leventhal, MSN, RN13; Dirk J.A. Lok, MD, PhD7; Jan Mårtensson, RN, PhD14; Javier Muñiz, MD, PhD15; Haruka Otsu, RN, PhD16; Frank Peters-Klimm, MD17; Michael W. Rich, MD18; Barbara Riegel, RN, PhD19; Anna Strömberg, RN, PhD4,20; Ross T. Tsuyuki, PharmD, MSc21; Dirk J. van Veldhuisen, MD, PhD22; Jaap C.A. Trappenburg, PhD1; Marieke J. Schuurmans, RN, PhD1; Arno W. Hoes, MD, PhD2 1
Dept of Rehabilitation, Nursing Science and Sports, University Medical Center Utrech Utrecht, ht, t N Netherlands; ethe et herl he rlan rl ands an ds; Julius Center for Health Sciences and Primary Care, University Medical Center Utrech Utrecht, ht, N Netherlands; etthe herl rlan rl ands an ds;; ds 3 Dept of Medical and Health Sciences and Departmentt of Cardiothoracic Surgery, Linköping University, Sweden; 4Dept of Medical and Health Sciences, Division of Nursing Science, Linköping University, Swed den en;; 5De Sweden; Dept D p off Ca pt Cardiology, Hospital General Unive Universitario vers ve rsitario Gregorio Mara rs Marañón, añó ñón, Madrid, Spain; 6British Heart Hear He a t Foundation, Foun und un dation, Glasgow, United Kingdom; 7Dep Dept pt of Cardiology,, De D Deventer ventter Hospital, Netherlands; 8 Division D ivision off General Gene Ge neraal Medicine ne Medi Me dici di c nee and ci and Clinical Cli lini nica ni c l Epidemiology, Epid Ep i em id miology gyy, University Univ Un iverrsi iv sity ty y ooff No Nort North rth Ca Carolina, aro roli lina li na,, Ch na Chapel hap apel el H Hill, ill, il l NC; N C; 9Dept of Health Health He h Services, Ser e viccess, University Uni niv ni verssity y of Washington, Washiing gton, n Sea Seattle, eatttle, WA ea WA;10D Dept ep pt of of IInternal ntern nall Med Medicine, ediicinee, ed University Michigan, Arbor, MI; Un ooff M icchigan an, Ann nA rbor,, M I; 11De Dept eptt of So Social all aand nd W nd Welfare elfaaree Stud Studies, diees, L Linköping inköpin ng Un University, niv versitty,, 12 Swed Sw eden ed en;; De en Sweden; Dept ept ooff H Health eaalth al S Services ervi er vice vi cess Re ce Res Research, sear arch ar ch, CA ch CAPH CAPHRI PHR PH RI Sc Scho School hoo ho ol ffor o P or Public ubli ub l c He li Heal Health a th h aand n P nd Primary rimaary C ri Care, arre, 13 Maastricht University, Netherlands; Insti Institute itute of Nursing Science, University of Basel, Switzerland; 14 Dept of Nursing Science, Jönköping University, Sweden; 15Instituto Universitario de Ciencias de la Salud, Universidad de A Coruña and INIBIC, Spain; 16Graduate School of Health Sciences, Hirosaki University, Aomori, Japan; 17Dept of General Practice and Health Services Research, University Hospital Heidelberg, Germany; 18Cardiovascular Division, Washington University School of Medicine, St. Louis, MO; 19School of Nursing, University of Pennsylvania, Philadelphia, PA; 20Dept of Cardiology, Linköping University, Sweden; 21Division of Cardiology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada; 22Dept of Cardiology, University Medical Center Groningen, Netherlands 2
Address for Correspondence: Nini H. Jonkman, MSc Department of Rehabilitation, Nursing Science & Sports University Medical Center Utrecht Heidelberglaan 100 3508GA, Utrecht, Netherlands Tel/Fax: +31-613244760 E-mail: [email protected] Journal Subject Terms: Heart Failure; Epidemiology; Behavioral/Psychosocial Treatment
1 Downloaded from http://circ.ahajournals.org/ at University of York (yku) / England on February 21, 2016
DOI: 10.1161/CIRCULATIONAHA.115.018006
Abstract
Background—Self-management interventions are widely implemented in care for patients with heart failure (HF). Trials however show inconsistent results and whether specific patient groups respond differently is unknown. This individual patient data meta-analysis assessed the effectiveness of self-management interventions in HF patients and whether subgroups of patients respond differently. Methods and Results—Systematic literature search identified randomized trials of selfmanagement interventions. Data of twenty studies, representing 5624 patients, were included and analyzed using mixed effects models and Cox proportional-hazard models including interaction terms. Self-management interventions reduced risk of time to the combined endpoint HF-related hospitalization or all-cause all cause death (hazard ratio [HR], 0.80; 95% confidence interval [C [[CI], I], 0.71 0.710.89), time to HF-related hospitalization (HR, 0.80; 95%CI, 0.69-0.92), and impr rov oved ed 112-month 2-mo 2month mo improved HF-related quality of life (standardized mean difference 0.15; 95%CI, 0.00-0.30). Subgroup analysis revealedd a protective effect of self-manag f gem e ent on number of H F-related hospital days self-management HF-related n ppatients at atients 80 years 636 00.85 .855 .8 (0.63-1.15) (0 .63-1.15 - 5) Total days HF-related RLOS years 139 HF-rela late la tedd te R RL OS 5 8892 92 00.86 .866 .8 80 years 232 0.96 (0.31-2.97) General outcomes Generic QoL-PCS MD 8 1739 0.95 80 years 296 1.13 (-2.01-4.26)
p-value Subgroups Depression for interaction 0.77
0.65
0.88 88
0.03 03
0.63
25 Downloaded from http://circ.ahajournals.org/ at University of York (yku) / England on February 21, 2016
n patients
Treatment effect (95% CI)
p-value for interaction
No/mild
1274
0.12
Moderate/ severe
696
0.81 (0.66-0.99) 1.05 (0.81-1.36)
No/mild
11832 18 32 2
0.41
Moderate/ severe
772
00.16 .1 16 (0 .14 14-0.19 19)) 19 (0.14-0.19) 0.25 (-0.01-0.50)
No/mild No//mild
1274
0.64
Moderate/ M o er od e ate/ severe seve v ree
6966 69
0.92 (0.71-1.18) (0.7 (0 . 1-1. .7 1.18 1. 18) 18 1.00 1.00 ((0.74-1.35) (0 .74-1.35 3 )
No/mild No/m No /mil /m ildd il
2228 28
0.94
Moderate/ severe
39
00.49 .449 .49 (0.13-1.84) 0.37 (0.01-9.70)
No/mild
796
0.45
Moderate/ severe
191
0.41 (0.09-0.73) -1.29 (-5.67-3.09)
DOI: 10.1161/CIRCULATIONAHA.115.018006
Generic QoL-MCS 12 months
MD
8
1739
0.27 80 years 296 -1.19 (-5.62-3.24) Mortality HR 14 4312 0.91 80 yearss 80 yea 856 0.79 (0.62-1.00) All-cause hospitalization HR 80 years 717 0.79 (0.64-0.97) Total days all-cause RLOS 80 > 0 years >8 453 0.77 0. 77 (0.49-1.20) (0.4 .499-1.20)) CI indicates cconfidence onfi nfi fide denc de ncee interval; nc inte in terv te rval; HF rv HF, hear heart ear artt fa failur failure; ure; ur e; HR, hhazard a ardd ra az rati ratio; t o; M MCS, CS, me CS ment mental n al ccomponent ompo om pone po nent ne n sscale; c le ca le;; MD, M , me MD m mean an n dif difference; iffe if fere fe renc enc nce; P PCS, C , pphysical CS hysi hy sica si call component ca compon co onentt scale; on scal sc ale; al e QoL, e; QoL quality of life; RLOS, relative length of stay; and SMD, standardized mean difference.
26 Downloaded from http://circ.ahajournals.org/ at University of York (yku) / England on February 21, 2016
DOI: 10.1161/CIRCULATIONAHA.115.018006
Figure Legend:
Figure 1. Forest plot of effects of self-management interventions on heart failure-related quality of life, heart failure-related hospitalization, and all-cause mortality. CI indicates confidence interval; HR, hazard ratio; and SMD, standardized mean difference.
27 Downloaded from http://circ.ahajournals.org/ at University of York (yku) / England on February 21, 2016
Downloaded from http://circ.ahajournals.org/ at University of York (yku) / England on February 21, 2016
Do Self-Management Interventions Work in Patients With Heart Failure? An Individual Patient Data Meta-Analysis Nini H. Jonkman, Heleen Westland, Rolf H.H. Groenwold, Susanna Ågren, Felipe Atienza, Lynda Blue, Pieta W.F. Bruggink-André de la Porte, Darren A. DeWalt, Paul L. Hebert, Michele Heisler, Tiny Jaarsma, Gertrudis I.J.M. Kempen, Marcia E. Leventhal, Dirk J.A. Lok, Jan Mårtensson, Javier Muñiz, Haruka Otsu, Frank Peters-Klimm, Michael W. Rich, Barbara Riegel, Anna Strömberg, Ross T. Tsuyuki, Dirk J. van Veldhuisen, Jaap C.A. Trappenburg, Marieke J. Schuurmans and Arno W. Hoes Circulation. published online February 12, 2016; Circulation is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231 Copyright © 2016 American Heart Association, Inc. All rights reserved. Print ISSN: 0009-7322. Online ISSN: 1524-4539
The online version of this article, along with updated information and services, is located on the World Wide Web at: http://circ.ahajournals.org/content/early/2016/02/11/CIRCULATIONAHA.115.018006
Data Supplement (unedited) at: http://circ.ahajournals.org/content/suppl/2016/02/12/CIRCULATIONAHA.115.018006.DC1.html
Permissions: Requests for permissions to reproduce figures, tables, or portions of articles originally published in Circulation can be obtained via RightsLink, a service of the Copyright Clearance Center, not the Editorial Office. Once the online version of the published article for which permission is being requested is located, click Request Permissions in the middle column of the Web page under Services. Further information about this process is available in the Permissions and Rights Question and Answer document. Reprints: Information about reprints can be found online at: http://www.lww.com/reprints Subscriptions: Information about subscribing to Circulation is online at: http://circ.ahajournals.org//subscriptions/
Downloaded from http://circ.ahajournals.org/ at University of York (yku) / England on February 21, 2016
Jonkman, et al. – Self-management interventions for heart failure
SUPPLEMENTAL MATERIAL Do self-management interventions work in patients with heart failure work? An individual patient data meta-analysis
Page
Content
2
Supplemental Methods: Statistical analysis plan
6
Supplemental Table 1: Effects of self-management interventions on subordinate outcomes in patients with heart failure included in the individual patient data meta-analysis.
7
Supplemental Table 2: Effects of self-management interventions on subordinate outcomes in subgroups of patients with heart failure included in the individual patient data meta-analysis.
8
Supplemental Table 3: Effects of self-management interventions on main outcomes in subgroups of patients with heart failure included in the individual patient data meta-analysis.
9
Supplemental Table 4: Sensitivity analysis on main outcomes by including published main effects of eligible studies without available individual patient data.
10
Supplemental Table 5: Sensitivity analysis on main outcomes by excluding trials with enhanced usual care in the comparison group.
11
Supplemental References
Jonkman, et al. – Self-management interventions for heart failure Supplemental Methods: Statistical analysis plan This document contains the plan for the statistical analysis for the individual patient data (IPD) meta-analysis in heart failure (HF) patients. Input from the conference calls on March 20 th, 2014 and March 31st, 2014 and email contact has been processed in the statistical plan presented in this document. A schematic overview of the statistical analysis is present in Figure 1. Each step will be explained in more detail in the subsequent paragraphs. For all statistical analyses, the software R for Windows version 3.1.1 (R Development Core Team. Released 2013. Vienna, Austria: R Foundation for Statistical Computing) will be used.
Figure 1: Steps of the statistical analysis of patient-specific determinants of self-management interventions.
1. Imputation of missing data To address bias due to missing data, we will impute missing data using multiple imputation by chained equations (MICE).1 The MICE algorithm accounts for the order in which the values of separate variables are predicted through chained equations. To address the uncertainty of just one single imputation, MICE creates multiple imputations, resulting in multiple imputed datasets. The imputation will be performed according to the following principles: • Missing values will only be imputed within studies: this implies that only the correlation between variables available within one study will be used to estimate the missing values in that particular study • All available variables (except patient identifiers) will be used to estimate missing values • Multiple imputation will be used to estimate missing values for patient characteristics and outcomes • Multiple imputation will be performed 25 times, resulting in 25 imputed datasets • As a result, all analyses will be carried out 25 times. Results will be pooled using Rubin’s rule for the final results.2 A complete-case analysis, using only the available patient data, will be performed as a sensitivity analysis to assess the impact of imputing data (see ‘4. Sensitivity analyses’).
2. Analysis of main effects All data will be analyzed according to the intention-to-treat principle. A so-called one-stage approach will be used, where all patients are analyzed simultaneously in one model while clustering of observations within studies is taken into account.3
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Jonkman, et al. – Self-management interventions for heart failure The present study will analyze the following main outcome measures: Composite of time to first disease-related hospital admission or all-cause death; Change in health-related quality of life (HRQoL) at 12 months, compared to baseline; A distinction will be made between disease-specific and generic HRQoL to address the different instruments used by original studies Time to first disease-related hospital admission; Total number of days spent in hospital for HF at 12 months. Time to all-cause death; Time to first all-cause hospital admission; Total number of days spent in hospital for any cause at 12 months. Additionally, the following subordinate outcomes measures will be analyzed: Change in health-related quality of life (HRQoL) at 6 months, compared to baseline; A distinction will be made between disease-specific and generic HRQoL Total number of days spent in hospital for HF at 6 months and at 12 months; Hospitalized for HF at 6 months; All-cause mortality at 6 months and at 12 months; Hospitalized for any cause at 6 months and at 12 months; Total number of days spent in hospital for any cause at 6 months. For time-to-event data, effects of self-management will be quantified by estimating hazard ratios (HR) and 95% confidence interval (CI). Cox proportional-hazard models will be used to analyze the data, including a cluster statement to allow inter study variability. For binary outcome data (mortality, all-cause and disease-related hospital admissions), risk ratios (RR) and 95% CI will be estimated using log-binomial mixed effects models. Effects on continuous outcomes (HRQoL) will be quantified by mean differences and 95% CI and will be estimated using linear mixed effects models. Effects on total length of hospital stay will be analyzed with negative binomial mixed effects models to model overdispersion in the data. In the (generalized) linear mixed effects models, random intercepts and random slopes will be included to take clustering within studies into account.
3. Patient-specific effect modifiers The aforementioned models will be extended to study effect modification by patient characteristics. Effect modification implies that the effect of the intervention on an outcome differs depending on the value of a third variable, the effect modifier. As such, we will be able to identify subgroups of patients in which selfmanagement interventions work best. Interaction terms will be included in the final model resulting from the previous step, which includes the significant program determinants. We have selected clinically relevant patient characteristics as potential effect modifiers, these are presented in Table 1. Numbers of patients differ per variable due to the fact that some baseline variables have not been collected in one (or more) studies. We would like to categorize the variables to create relevant subgroups for the interpretation of findings. This has been discussed extensively during the conference calls, and the proposed categories are a result of the discussions. Like the analysis of program characteristics, patient characteristics with p35% LVEF (based on ESC Guidelines 2012)
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