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First outline and baseline data of a randomized, controlled multicenter trial to evaluate the health economic impact of home telemonitoring in chronic heart failure – CardioBBEAT

  • Reiner Hofmann1,
  • Heinz Völler2, 3,
  • Klaus Nagels1,
  • Dominik Bindl1Email author,
  • Eik Vettorazzi4,
  • Ronny Dittmar1, 5,
  • Walter Wohlgemuth1, 6,
  • Till Neumann7,
  • Stefan Störk8,
  • Oliver Bruder9,
  • Karl Wegscheider4,
  • Eckhard Nagel1,
  • Eckart Fleck10 and
  • on behalf of the CardioBBEAT Investigators
Trials201516:343

https://doi.org/10.1186/s13063-015-0886-8

Received: 26 November 2014

Accepted: 24 July 2015

Published: 11 August 2015

Abstract

Background

Evidence that home telemonitoring for patients with chronic heart failure (CHF) offers clinical benefit over usual care is controversial as is evidence of a health economic advantage.

Methods

Between January 2010 and June 2013, patients with a confirmed diagnosis of CHF were enrolled and randomly assigned to 2 study groups comprising usual care with and without an interactive bi-directional remote monitoring system (Motiva®). The primary endpoint in CardioBBEAT is the Incremental Cost-Effectiveness Ratio (ICER) established by the groups’ difference in total cost and in the combined clinical endpoint “days alive and not in hospital nor inpatient care per potential days in study” within the follow-up of 12 months.

Results

A total of 621 predominantly male patients were enrolled, whereof 302 patients were assigned to the intervention group and 319 to the control group. Ischemic cardiomyopathy was the leading cause of heart failure. Despite randomization, subjects of the control group were more often in NYHA functional class III–IV, and exhibited peripheral edema and renal dysfunction more often. Additionally, the control and intervention groups differed in heart rhythm disorders. No differences existed regarding risk factor profile, comorbidities, echocardiographic parameters, especially left ventricular and diastolic diameter and ejection fraction, as well as functional test results, medication and quality of life. While the observed baseline differences may well be a play of chance, they are of clinical relevance. Therefore, the statistical analysis plan was extended to include adjusted analyses with respect to the baseline imbalances.

Conclusions

CardioBBEAT provides prospective outcome data on both, clinical and health economic impact of home telemonitoring in CHF. The study differs by the use of a high evidence level randomized controlled trial (RCT) design along with actual cost data obtained from health insurance companies. Its results are conducive to informed political and economic decision-making with regard to home telemonitoring solutions as an option for health care. Overall, it contributes to developing advanced health economic evaluation instruments to be deployed within the specific context of the German Health Care System.

Trial registration

ClinicalTrials.gov NCT02293252; date of registration: 10 November 2014

Keywords

Home telemonitoringChronic heart failure (CHF)Incremental Cost-Effectiveness Ratio (ICER)MortalityTelemedicineHealth economics

Background

Chronic heart failure (CHF) is one of the most frequently diagnosed diseases causing disability and death in the Western hemisphere. It is characterized by a prevalence that increases with age [1]. In Germany, heart failure is the most common reason for hospitalization with about 396,000 cases in 2013 [2]. Direct medical costs related to heart failure account for 1–2 % of total health care expenditure [3].

In the majority of cases, home telemonitoring solutions in health care delivery to patients with CHF show advantages over usual care in terms of clinical outcomes. Several meta-analyses reveal that total mortality and number of hospitalizations tend to decrease, while patients’ quality of life improves [46]. Two subsequently published trials (Telemedical Interventional Management in Heart Failure trial (TIM-HF) [7], Telemonitoring in patients with Heart Failure trial (TELE-HF) [8]) show neutral findings in general. However, the health economic impact has not been clearly demonstrated so far [9]. A meta-analysis by Klersy et al. (2011) states that the difference in costs between remote patient monitoring and usual care ranges from Euro300 to Euro1000, favoring remote patient monitoring because of a lower hospitalization rate. Thus, direct costs for hospitalization were approximated by diagnosis-related group tariffs [10]. A more detailed evaluation of efficiency and economic feasibility could help to determine cost-effectiveness and to avoid misallocation of resources [11].

The CardioBBEAT trial was designed to assess the health economic impact of a dedicated home telemonitoring system for patients with CHF based on actual costs directly obtained from patients’ health care providers. The present report provides details on the outline of the study and an analysis of the study population’s baseline data.

Methods

Study design

CardioBBEAT represents a randomized, controlled, open, multicenter trial with two prospective study arms. Patients were recruited at ten study sites from five areas of varying economic status in Germany: namely, Berlin, Brandenburg, Bavaria, Hamburg and North Rhine-Westphalia. This diversity allows for investigating the impact of regional differences in medical care, with general medical care predominating in rural districts compared to predominantly specialist care in urban areas. Each site was responsible for recruiting as well as following-up on their patients. Specified information about inclusion and exclusion criteria is displayed in Table 1.
Table 1

Summary of inclusion and exclusion criteria at screening

Inclusion criteria

  Confirmed diagnosis of CHF based on ESC guidelines

  Symptoms corresponding to NYHA functional class II–IV

  AHA classification stages C–D

  LVEF ≤ 40 %

  Age ≥ 18 years

  Patient is discharged after being hospitalized for CHF within the last 12 months

  Patient is able to understand the German language

  Patient has sufficient eyesight to understand and follow the instructions communicated by Motiva®

  Patient is willing and able to use the required hardware and software and to maintain a patient diary

  Patient is residing within geographical reach of one of the ten telemonitoring centers in order to receive additional treatment if required as well as follow-up consultation

  Patient gives informed consent regarding benefits and risks related to the trial, and to sign a participation agreement for the installation of the home telemonitoring system Motiva®

Exclusion criteria

  Myocardial infarction within the past 4 weeks

  Heart surgery or any coronary intervention within the past 8 weeks

  Cardiogenic shock within the past 4 weeks

  Intended cardiac surgery within the next 6 months or priority status on a waiting list for organ transplantation

  Severe chronic and pulmonary illness with an immediate impact on the main outcome measures

  Renal dysfunction requiring dialysis

  Dementia or other severe cognitive impairment

  Psychiatric disorders prohibiting a participation in the trial

  Patient is discharged to or living in an older persons clinic or a nursing home

  Patient is participating in another clinical trial

AHA American Heart Association, staging of heart failure, CHF chronic heart failure, ESC European Society of Cardiology, LVEF left ventricular ejection fraction NYHA New York Heart Association, classification of heart failure

The study has been conducted in accordance with the principles stated in the Declaration of Helsinki, the Good Clinical Practice (International Conference on Harmonization), and national as well as local regulations. The research protocol was approved by the responsible ethics committees (Table 2), and written informed consent was obtained from all patients prior to any study-related procedures. The study is monitored by an independent external institute to ensure that every participating site abides by the study protocol and to perform external quality control of the data.
Table 2

List of ethical bodies

Ethical body

Reference number

Ethik-Kommission für Forschungsfragen der Universität Bayreuth

O 1305 – HB/ID

Landesärztekammer Brandenburg, Ethik-Kommission

AS 94(a)/2009

Ethikkommission – Ethikausschuss 2 am Campus Virchow-Klinikum Berlin

EA2/084/09

Ethik-Kommission für Forschungsfragen der Universität Bayreuth

O 1305 – HB/ID

Ethik-Kommission für Forschungsfragen der Universität Bayreuth

O 1305 – HB/ID

Medizinische Fakultät der Universität Duisburg-Essen, Ethik-Kommission

10-4536

Ethik-Kommission der Bayerischen Landesärztekammer

7/11014

Universität Würzburg, Ethik-Kommission bei der Medizinischen Fakultät

128/11

Ethik-Kommission der Ärztekammer Hamburg

MC-141/11

Ethik-Kommission für Forschungsfragen der Universität Bayreuth

O 1305 – GB

Ärztekammer Nordrhein, Ethikkommission

2012451

Setting

During the trial, all patients receive best medical treatment according to the current guidelines of the European Society of Cardiology (ESC) [12, 13].

Patients in the control group only receive best medical treatment as stated above, whereas patients in the intervention group additionally receive home telemonitoring-supported care that connects them to the participating care providers by individual guideline-compliant care plans using the telemedicine-system Motiva® (Philips Medical Systems GmbH, Hamburg, Germany).

Motiva® is an interactive bi-directional home telemonitoring system that provides remote monitoring, empowers patients to manage their disease state more effectively and enables physicians to keep in contact with the patient at home on a daily basis. Patients measure their vital signs (blood pressure, heart rate, and weight) every day and Motiva® transfers the data to the relevant telemonitoring center. In doing so, signals of decompensation regarding their heart function can be detected at an early stage and counteractive measures can be taken. In addition, patients receive information via Motiva®, i.e. coaching material, evaluations, reminders, and feedback regarding their health status as well as references to potentially necessary CHF treatment adaptations. If questionnaires reveal any problems, patients receive a phone call from the study site. Additionally, patients receive standardized questionnaires related to symptoms of cardiac decompensation, hypotension or hypertension or abnormal pulse rates. A call from the telemedicine center is made if patients gain more than 2 kg within 3 days, if their systolic blood pressure exceeds 140 mmHg or is lower than 90 mmHg, or their resting heart rate exceeds 80 bpm or is lower than 50 bpm.

A secured broadband connection (Digital Subscriber Line (DSL) or Universal Mobile Telecommunications System (UMTS)) and a set-top box turn the patient’s television into their center of personalized care protected by a patient-specific password. Thus, patients can transfer all information about their health status to their attending physician safely.

Motiva® is been provided by Philips Medical Systems GmbH, Hamburg, Germany. The telecommunication infrastructure to transfer patients’ data is made available by T-Systems International GmbH, Frankfurt, Germany. Both are provided without any obligations that could influence the study.

Randomization

To assign patients to one of the two study arms, CardioBBEAT used a centralized stacked randomization technique. Patients at home who were managed in cardiologic practices were randomized patient-individually. Patients primarily managed by their general practitioner (GP), on the other hand, were cluster-randomized by their GP’s medical practices, to minimize carry-over effects and to keep the organizational effort manageable. The results of the randomization process with regard to the patients were displayed via the study’s electronic Case Report Form (eCRF) directly after inclusion into the trial. The study center in charge did inform the patients’ attending physicians whether their respective patients were enrolled in the trial and to which study arm they were assigned.

Treatment patterns

After patients are discharged from inpatient care, their GPs or outpatient medical specialists will provide their ambulatory care. These physicians have access to individual patient care plans and are authorized to complete or modify them.

Subjects enrolled in the control group receive best medical treatment according to the current guidelines of the European Society of Cardiology (ESC). Subjects enrolled in the intervention group are additionally supported by the telemedicine system Motiva® installed at the patients’ home usually within 2 weeks.

All trial participants maintain a patient diary and are urged to document any health disturbances at least once a week, such as hospitalizations (date of hospitalization, reason for admission, and length of stay), consultations by any physician, and change in medication or dose rate as well as adverse effects. In addition, every patient has to participate in 3 trial-specific examinations (Table 3) at their relevant study site, that take place at the time of enrollment and after 6 as well as 12 months.
Table 3

Trial-specific examinations

Enrollment examination

 

1. Patient briefing

 

2. Written informed consent

 

3. Verification of inclusion and exclusion criteria

 

4. Demography

 

  Sex

 

  Date of birth

 

  Marital status

 

  Size and weight

 

5. Hemodynamic parameters

 

  Heart rate

 

  Blood pressure (systolic/diastolic)

 

6. Disease-related parameters

 

  Medical history

 

  CHF (date of diagnosis, aetiology, inpatient treatments, NYHA classification, AHA stadium)

 

  Comorbidities

 

  Medication

 

  Care plan compilation

 

  6 MWD

 

7. Health-related quality of life assessment

 

  SF-36v2 (generic quality of life questionnaire)

 

  WHO-5 (generic quality of life questionnaire)

 

  KCCQ (disease-specific quality of life questionnaire)

 

Follow-up and final examination

 

1. Demography

 

  Weight

 

2. Hemodynamic parameters

 

  Heart rate

 

  Blood pressure (systolic/diastolic)

 

3. Disease-related parameters

 

  CHF (NYHA classification, AHA stadium)

 

  Newly occurring comorbidities

 

  Medication

 

  Hospitalizations or admissions to a nursing home

 

  Patient diary monitoring and validation of AE/SAE

 

  6 MWD

 

4. Health-related quality of life assessment

 

  SF-36v2 (generic quality of life questionnaire)

 

  WHO-5 (generic quality of life questionnaire)

 

  KCCQ (disease-specific quality of life questionnaire)

 

In case a patient dies during the trial

 

  Date of death

 

  Cause of death

 

6 MWD 6-minute walking distance, AE adverse event, AHA American Heart Association, staging of heart failure, ESC European Society of Cardiology, KCCQ Kansas City Cardiomyopathy Questionnaire with 23 items for measuring disease-specific domains in CHF, LVEF left ventricular ejection fraction, NYHA New York Heart Association, classification of heart failure, SAE serious adverse event, SF-36v2 short form health survey with 36 questions using norm-based scoring, WHO-5 World Health Organization Five, well-being index

Clinical outcome measures

The primary outcome measure to assess the benefit of home telemonitoring is the combined clinical endpoint “days alive and not in hospital nor inpatient care per potential days in study.” For deceased patients, the loss in lifetime is taken into account by setting the denominator to 360 days, for patients lost to follow-up, time to last contact is used. Secondary outcome measures are total mortality, number of inpatient treatments, length of stay in hospital or nursing home, functional state of health and health-related quality of life. These will be determined by the following parameters: days survived in the study, number of hospitalizations for any reason during the study (especially cardiac and heart failure-related reasons), number of days in hospital or nursing home per study month, generic (short form health survey with 36 questions using norm-based scoring (SF-36v2), World Health Organization Five, well-being index (WHO-5)) and disease-specific (Kansas City Cardiomyopathy Questionnaire, (KCCQ)) health-related quality of life as well as medical condition and capacity of each patient.

CardioBBEAT may also be able to differentiate between particular sub-groups (e.g. gender-specific, NYHA-specific, urban/rural, diabetes mellitus) while analyzing the effectiveness of the intervention. The expectation is that several patient groups can be identified which are particularly suited for home telemonitoring with regard to clinical and/or economic outcome.

Cost data

CardioBBEAT aims to reflect the impact of home telemonitoring within an actual health care setting based on originally obtained cost data subdivided into cost of intervention, cost of inpatient and outpatient care, rehabilitation, nursing, and life-saving appliances. To this end, cost data are obtained from patients’ health insurance companies and later on validated using the records of the telemonitoring centers, GPs and medical specialists as well as patients’ diaries. Health insurance data will be obtained similarly for both, patients in the intervention group as well as the control group to avoid ascertainment bias.

All data are analyzed with appropriate statistical methods to determine the economic effectiveness of home telemonitoring for patients with CHF. Common approaches for the analysis of cost data such as t tests, analysis of covariance, bootstrap techniques or permutation tests will be compared regarding their feasibility, the validity of underlying assumptions and their stability and robustness in particular if missing values have to be taken into account.

Especially when analyzing cost data or determining the Incremental Cost-Effectiveness Ratio (ICER), assumptions are made regarding discount rate, utilities, projections or estimation of costs, which are based on uncertain hypotheses. To better understand the outcomes of these analyses, CardioBBEAT uses sensitivity analyses considering best-case and worst-case scenarios to demonstrate in which way the outcomes depend on these assumptions and how they affect their assessment.

Statistical analysis

The primary endpoint ICER, consisting of the group’s difference in total cost and the combined clinical endpoint “days alive and not in hospital nor inpatient care per potential days in study”, is calculated with confidence intervals obtained by resampling methods. The comparative conventional endpoint “event-free survival” to measure the intervention’s effectiveness is evaluated using the Kaplan-Meier analysis and log rank tests. To incorporate possible repeated hospitalizations of a patient, additional analyses will be performed, e.g. comparison of quarterly data and recurrent event analysis.

Secondary outcome measures such as number of stays in hospital per quarter, health-related quality of life or time of survival are analyzed via permutation test, covariance analysis or log rank test as part of the Kaplan-Meier analysis.

Furthermore, the trial uses a cluster-randomization technique and, therefore, correlation effects can evolve due to the collective treatment of patients in a cardiology center, medical practice, or by a single study nurse. Such effects can result in incorrect p values. CardioBBEAT uses frailty and multi-level models to assess if and where such correlations occur. The magnitude of these correlations will be measured and the p values will be rectified.

Since at planning stage neither an established statistical method to directly compare costs and ICERs nor sufficient data to estimate the variability of cost estimates was available, the sample size was determined based on literature data. With respect to clinical endpoints, the figures were rather stable and converged to a minimum of 300 patients per group. Since the primary endpoint was expected to be mainly driven by clinical events, it was assumed that the sample size will also be sufficient for the continuously distributed ICER.

Results

The study group comprised 621 patients, predominantly men. Four hundred and seventy-two (76 %) patients were treated by 449 GPs and 149 (24 %) were treated in 119 cardiologic practices. Three hundred and two patients were randomized into the intervention group and 319 patients into the control group. Ischemic cardiomyopathy was the leading cause of heart failure (59 %). Although randomly assigned, subjects of the control group were significantly more often in NYHA functional classes III or IV and exhibited peripheral edema or renal dysfunction, respectively, more frequently (Table 4). Additionally, the control and intervention group differed in heart rhythm disturbances (Table 5). No differences were detected regarding risk factor profile, comorbidities, echocardiographic parameters, especially left ventricular and diastolic diameter and ejection fraction, as well as functional test (6 MWD) results, medication and quality of life (Tables 4, 5, 6 and 7).
Table 4

Baseline characteristics of the CardioBBEAT study participants

Characteristic

All patients n = 621

Usual care n = 319 (51 %)

Monitoring n = 302 (49 %)

p value

Demographic profile

    

  Age (years)

    

   mean ± SD

63.0 ± 11.5

63.5 ± 11.4

62.5 ± 11.6

0.303

   median (IQR)

65 (55–72)

65 (55–73)

64 (54–72)

 

  Male sex, n (%)

544 (88)

280 (88)

264 (87)

0.990

  Living alone, n (%)

159 (26)

77 (24)

82 (27)

0.442

  Education (years) – number of patients (% valid)

607 (98)

311 (98)

296 (98)

 

   mean ± SD

12 ± 3

12 ± 3

12 ± 3

0.803

   median (IQR)

11 (10–13)

11 (10–13)

12 (10–13)

 

  Causes of heart failure, n (%)

   

0.797

   Ischemic CM

363 (59)

185 (58)

178 (59)

 

   Non-ischemic CM

258 (42)

134 (42)

124 (41)

 

  NYHA class, n (%)

   

0.086

   II

430 (69)

209 (66)

221 (73)

 

   III

186 (30)

108 (34)

78 (26)

 

   IV

5 (1)

2 (1)

3 (1)

 

  NYHA class III–IV, n (%)

191 (31)

110 (35)

81 (27)

0.048

  Peripheral edema, n (%)

131 (21)

83 (26)

48 (16)

0.003

Comorbidities, n (%)

    

  Stroke/TIA

41 (7)

21 (7)

20 (7)

1.000

  PAD

56 (9)

24 (8)

32 (11)

0.232

  COPD

87 (14)

48 (15)

39 (13)

0.516

  Sleep apnea

45 (7)

25 (8)

20 (7)

0.668

  Renal dysfunction (GFR ≤ 60 ml/min)

148 (24)

87 (27)

61 (20)

0.048

  Depression

51 (8)

24 (8)

27 (9)

0.619

  Resuscitation

81 (13)

37 (12)

44 (15)

0.327

Risk factor profile

    

  BMI (kg/m2) – number of patients (% valid)

619 (100)

318 (100)

301 (100)

 

   mean ± SD

28 ± 5

28 ± 5

28 ± 5

0.529

   median (IQR)

28 (25–31)

28 (25–32)

27 (25–31)

 

  History of smoking – number/total number (%)

435/620 (70)

221/319 (69)

214/301 (71)

0.684

  Diabetes mellitus, n (%)

219 (35)

103 (32)

116 (38)

0.131

  Hypertension, n (%)

533 (86)

269 (84)

264 (87)

0.323

BMI body mass index, CM cardiomyopathy, COPD chronic obstructive pulmonary disease, GFR glomerular filtration rate, IQR interquartile range, NYHA New York Heart Association, classification of heart failure, PAD peripheral arterial disease, TIA transient ischemic attack

Table 5

Baseline characteristics of the CardioBBEAT study participants - Diagnostic

Characteristic

All patients n = 621

Usual care n = 319 (51 %)

Monitoring n = 302 (49 %)

p value

ECG

    

  Heart rate (1/min) – number of patients (% valid)

616 (99)

315 (99)

301 (100)

 

   mean ± SD

72 ± 14

72 ± 14

72 ± 13

0.862

   median (IQR)

71 (63–81)

71 (63–82)

71 (62–80)

 

  Heart rhythm – number/total number (%)

   

0.031

   Sinus rhythm

379/619 (61)

185/318 (58)

194/301 (65)

 

   Atrial fibrillation

78/619 (13)

46/318 (15)

32/301 (11)

 

   Pacemaker ECG

154/619 (25)

86/318 (27)

68/301 (23)

 

   Other

8/619 (1)

1/318 (0)

7/301 (2)

 

  Conduction disorder – number/total number (%)

    

   LBBB

145/543 (27)

69/272 (25)

76/271 (28)

0.543

   RBBB

49/544 (9)

28/273 (10)

21/271 (8)

0.383

  QRS duration (ms) – number of patients (% valid)

568 (92)

288 (90)

280 (93)

 

   mean ± SD

123 ± 33

125 ± 34

121 ± 32

0.151

   median (IQR)

110 (100–144)

116 (100–150)

110 (100–140)

 

2D echocardiography

    

  LVEDD (mm) – number of patients (% valid)

584 (94)

297 (93)

287 (95)

 

   mean (mm) ± SD

62 ± 9

62 ± 9

63 ± 9

0.580

   median (IQR)

62 (57–68)

62 (57–68)

62 (57–68)

 

  LVEF (%) – number of patients (% valid)

619 (100)

317 (100)

302 (100)

 

   mean ± SD

30 ± 7

31 ± 7

30 ± 8

0.580

   median (IQR)

31 (25–37)

31 (25–37)

30 (25–36)

 

  Mitral insufficiency – number/total number (%)

   

0.319

   none

102/614 (17)

55/315 (18)

47/299 (16)

 

   mild

370/614 (60)

180/315 (57)

190/299 (64)

 

   moderate

121/614 (20)

70/315 (22)

51/299 (17)

 

   severe

21/614 (3)

10/315 (3)

11/299 (4)

 

6-minute walk test – number of patients (% valid)

559 (90)

284 (89)

275 (91)

 

   mean (m) ± SD

375 ± 132

376 ± 132

374 ± 131

0.804

   median (IQR)

400 (300–458)

404 (300–455)

400 (300–460)

 

2D two-dimensional, ECG electrocardiogram, IQR interquartile range, LBBB left bundle branch block,LVEDD left ventricular end-diastolic dimension, LVEF left ventricular ejection fraction, QRS combination of three of the graphical deflections seen on a typical ECG, RBBB right bundle branch block

Table 6

Baseline characteristics of the CardioBBEAT study participants - Therapy

Characteristic

All patients n = 621

Usual care n = 319 (51 %)

Monitoring n = 302 (49 %)

p value

Medication

    

  ACE inhibitor/ARB, n (%)

577 (93)

297 (93)

280 (93)

0.974

  Beta blocker – number/total number (%)

591/620 (95)

303/319 (95)

288/301 (96)

0.826

  MR antagonist, n (%)

439 (71)

232 (73)

207 (69)

0.291

  Diuretics, n (%)

506 (82)

255 (80)

251 (83)

0.360

  Glycosides, n (%)

95 (15)

50 (16)

45 (15)

0.876

  Amiodarone, n (%)

80 (13)

48 (15)

32 (11)

0.125

  Anticoagulation, n (%)

    

   Vitamin K antagonist

231 (37)

122 (38)

109 (36)

0.637

   Other

42 (7)

24 (8)

18 (6)

0.538

Devices, n (%)

    

  Pacemaker

101 (16)

61 (19)

40 (13)

0.061

  ICD

   

0.280

   with monitoring

63 (10)

31 (10)

32 (11)

 

   without monitoring

242 (39)

134 (42)

108 (36)

 

  CRT-D

89 (14)

40 (13)

49 (16)

0.232

ACE angiotensin converting enzyme, ARB angiotensin receptor blocker, CRT-D cardiac resynchronization therapy combined with defibrillation ICD implantable cardioverter defibrillator, MR mineralocorticoid receptor

Table 7

Baseline characteristics of the CardioBBEAT study participants – Quality of life

Characteristic

All patients n = 621

Usual care n = 319 (51 %)

Monitoring n = 302 (49 %)

p value

SF-36v2

    

  Physical comp. sum – number of patients (% valid)

581 (94)

299 (94)

282 (93)

 

   mean ± SD

39 ± 10

39 ± 10

38 ± 10

0.458

   median (IQR)

38 (32–46)

38 (32–46)

38 (32–45)

 

  Mental comp. sum – number of patients (% valid)

581 (94)

299 (94)

282 (93)

 

   mean ± SD

45 ± 13

45 ± 12

44 ± 13

0.239

   median (IQR)

46 (35–56)

46 (36–55)

45 (34–56)

 

  Physical functioning – number of patients (% valid)

591 (95)

304 (95)

287 (95)

 

   mean ± SD

51 ± 27

52 ± 27

50 ± 27

0.445

   median (IQR)

50 (30–75)

50 (30–75)

50 (30–70)

 

WHO-5

    

  Score – number of patients (% valid)

586 (94)

301 (94)

285 (94)

 

   mean ± SD

55 ± 25

55 ± 25

54 ± 25

0.588

   median (IQR)

56 (36–76)

56 (36–76)

56 (32–76)

 

KCCQ

    

  Overall sum – number of patients (% valid)

591 (95)

305 (96)

286 (95)

 

   mean ± SD

59 ± 24

59 ± 24

60 ± 23

0.574

   median (IQR)

61 (42–80)

60 (42–80)

62 (42–80)

 

  Clinical sum – number of patients (% valid)

591 (95)

305 (96)

286 (95)

 

   mean ± SD

63 ± 25

63 ± 26

64 ± 24

0.409

   median (IQR)

67 (45–85)

67 (44–85)

69 (49–84)

 

Comp component, IQR interquartile range, KCCQ Kansas City cardiomyopathy questionnaire with 23 items for measuring disease-specific domains in CHF, SF-36v2 short form health survey with 36 questions using norm-based scoring, sum summary, WHO-5 World Health Organization Five, well-being index

In comparison with recently published German trials (TIM-HF [7], Interdisciplinary Network for Heart failure study (INH) [14]) the CardioBBEAT target population was slightly younger and comprised more male patients with fewer in NYHA classes III and IV. Nevertheless, all patients were either categorized in AHA stages C or D and every fifth patient of the given cohort was diagnosed with peripheral edema; this suggests that the study population had a comparable degree of heart failure. Regarding comorbidities and risk factor profile, the study population was very similar, particularly for diabetes or renal dysfunction. Remarkably, it revealed a left bundle branch block in approximately 25 % of the population, a rhythm disorder with a known worse prognosis [15]. Furthermore, detailed information on prognostic relevant therapies was documented. Besides a high proportion of guideline-based pharmacotherapy including beta blockers and ACE inhibitors/ARB inhibitors, the study population was treated with mineralocorticoid receptor blockers in 71 % of all cases. A cardioverter-defibrillator or a resynchronization system was implanted in two thirds of our patients (comparable to TIM-HF study [7]) with a considerable prognostic impact on primary and secondary endpoints. Table 8 compares the most important baseline characteristics with TIM-HF [7] and INH [14].
Table 8

Compared baseline characteristics of the CardioBBEAT, TIM-HF and INH study participants

Variable

CardioBBEAT

TIM-HF

INH

Usual care

Monitoring

Usual care

Monitoring

Usual care

Monitoring

(n = 319)

(n = 302)

(n = 356)

(n = 354)

(n = 363)

(n = 352)

Demographic profile

      

  Age (years)

63.5 ± 11.4

62.5 ± 11.6

66.9 ± 10.5

66.9 ± 10.8

69.4 ± 11.5

67.7 ± 12.8

  Male sex (%)

88

87

82

81

71

71

  Living alone (%)

24

27

22

21

35

30

Clinical profile

      

  NYHA class (%)

      

   I

0

0

0

0

2

3

   II

65

73

51

50

62

54

   III

34

26

49

50

31

40

   IV

1

1

0

0

5

3

Risk factor profile

      

  BMI (kg/m2)

28 ± 5

28 ± 5

28 ± 5

28 ± 5

n.a.

n.a.

  Diabetes mellitus (%)

32

38

39

40

36

36

  Hypertension (%)

84

87

66

68

77

72

Diagnostic

      

  Heart rate (1/min)

72 ± 14

72 ± 13

71 ± 13

71 ± 13

80 ± 18

80 ± 20

  LVEF (%)

31 ± 7

30 ± 8

27 ± 6

27 ± 6

30 ± 8

30 ± 8

Medication (%)

      

  ACE inhibitor/ARB

93

93

97

94

87

89

  Beta blocker

95

96

93

92

79

81

  Diuretics

80

83

94

94

86

90

ACE angiotensin converting enzyme, ARB angiotensin receptor blocker, BMI body mass index, LVEF left ventricular ejection fraction, n.a. not available, NYHA New York Heart Association, classification of heart failure

Discussion

Several recent randomized controlled trials (RCTs), including TIM-HF [7, 16] and INH [14], have proven the positive clinical effects of home telemonitoring on several groups of patients diagnosed with CHF. The meta-analyses of Clark et al. [4], Klersy et al. [5] and Inglis et al. [6] also showed its potential to improve several clinical outcomes such as quality of life. However, many results were not statistically significant mostly due to the fact that effects were too small despite adequately sized studies.

Another meta-analysis by Klersy et al. [10] focused on the economic impact of remote patient monitoring. It showed that management of HF patients by remote monitoring is cost-saving due to a substantial reduction in health care resource utilization, mostly driven by a reduction in the number of HF hospitalizations. However, cost data in this meta-analysis was estimated using 3 diagnosis-related group reimbursements (minimum, median, maximum over countries) and 3 different incidence rates and their lower and upper 95 % CI (confidence interval). These facts reflect the requirement for additional study-derived and reliable evidence based on originally obtained cost data unlike previously negotiated prices.

In CardioBBEAT, the follow-up care of patients was more diverse than expected: the number of participating practices was higher whereas the number of patients per practice was lower than expected, resulting in an incomplete use of the random blocks implemented in the eCRF and slightly unequal sample sizes between the random groups. However, slight differences in group sizes are of no concern with respect to unbiasedness of results.

At first glance, an imbalance in baseline variables stands out. But, even with perfect randomization the expected number of statistically significant differences between baseline variables is 5 % or 2.45 of the 49 baseline comparisons in Tables 4, 5, 6 and 7, on average. In case of independence of the baseline variables, the observed number of significances will thus follow a binomial distribution with p = 0.05. In CardioBBEAT, 4 out of 49 baseline comparisons (8.2 %) were significant. In case of independence, 4 or more significant comparisons would occur in 23 % of the cases even in perfect randomization. The observed baseline differences could thus well be a play of chance. However, NYHA functional class, peripheral edema, heart rhythm, and renal dysfunction were clinically highly relevant variables that might bias the conclusions even if evoked by chance. Therefore, the statistical analysis plan was extended to include adjusted analyses with respect to the baseline imbalances.

Conclusions

CardioBBEAT is a RCT that adds a comprehensive cost assessment to the clinical component of the study including actual costs generated by patients, health services and health products. The corresponding data have been obtained directly from patients’ health insurances including statutory sickness funds and private insurances. This will provide more reliable information about the cost-effectiveness of home telemonitoring in CHF patients based on the actual health care setting. CardioBBEAT may also be able to differentiate between particular sub-groups (gender-specific, NYHA-specific, urban/rural, diabetes mellitus) while analyzing the effectiveness of the intervention. The expectation is that important patient groups, which are better suited for the input of telemedicine with regard to the clinical and/or economic outcome, can be identified. The study results, reflecting a guideline-compliant, highly accurate treatment of the whole CardioBBEAT study population shown above, will significantly contribute to the existing data basis on home telemonitoring in CHF. Therefore, it adds to informed political and economic decision-making within the specific context of the German Health Care System.

Individual gratitude

Study center home telemonitoring teams

German Heart Institute Berlin

Fleck Eckart, Prof. Dr.

Furundzija Vesna, Dr.

Götze Stephan , PD Dr.

Roser Mattias, Dr.

Ahmed-Taner Funda, Dr.

Weinkopf Katharina

Gültekin Nicole

Post Monica

 

Klinik am See Rüdersdorf

Völler Heinz, Prof. Dr.

Belozerov Sergej, Dr.

Michely Beate, Dr.

Hartwig-Zaidan Ines

Stolze Kirsten

Salzwedel Annett

University Hospital Essen

Neumann Till, Prof. Dr.

Krings Peter, Dr.

 

Comprehensive Heart Failure Center Würzburg

Störk Stefan, Prof. Dr.

Angermann Christiane, Prof. Dr.

Reichert Clemens, Dr.

Schmidt Maximilian, Dr.

Menhofer Dominik, Dr.

Hartner Gabriele

Schupfner Elisabeth

  

Contilia Heart and Vascular Center Essen

Bruder Oliver, PD Dr.

Blank Elisabeth

Grosch Bernhard, Dr.

Seifert Vanessa, Dr.

Waidelich Lioba, Dr.

Keinhorst Jens

Reuter Vanessa

Steffen Melanie

 

Munich Municipal Hospital Bogenhausen

Leber Alexander, PD Dr.

Antoni Diethmar, Dr.

Kloos Patrick, Dr.

Landwehr Peter, Dr.

Tomelden June

 

Vivantes Medical Center Berlin Neukölln

Darius Harald, Prof. Dr.

Meincke Carsten, Dr.

Kirchner Aenn

Maselli Astrid

  

Jewish Hospital Berlin

Graf Kristof, Prof. Dr.

Bolay Miriam, Dr.

Pfürtner Mona, Dr.

Fischer Lidia

  

University Heart Center Hamburg-Eppendorf

Patten-Hamel Monica, PD Dr.

Molz Simon, Dr.

Sinning Christoph, Dr.

de Boer Imkje

Hermes Monika

Kupper-Schmidt Claudia

Hospitals Essen Süd

Koslowski Bernd, Dr.

Kemper Nicole

Reintges Nicole

Technology partners

Philips Medical Systems Boeblingen GmbH

Brüge Armin, MBA, Dipl.-Ing.

Bui Nhat Kha, Certified Engineer, Dipl. Can. Theol.

Goldbach Udo, Dipl.-Ing.

Richter Wolfgang, Dr.

Sarantos Melanie

Westerteicher, Christoph, Dipl.-Ing.

T-Systems International GmbH

Bruns Uta

Cech Martin, Dipl.-Ing.

Foth Gerd, Dipl.-Ing.

Hauptmann Imke

  

Study and project management

Institute of Health Care Management and Health Sciences

Nagels Klaus, Prof. Dr. Dr.

Nagel Eckhard, Prof. Dr. mult.

Wohlgemuth Walter, Prof. Dr. Dr.

Dittmar Ronny, Dr.

Hofmann Reiner

Bindl Dominik

Department of Medical Biometry and Epidemiology

Wegscheider Karl, Prof. Dr.

Balzer Klaus

Treszl Andras, Dr.

Vettorazzi Eik

  

Monitoring

Clinical Trial Center North

Freese Ralf, Dr.

Papavlassopoulos Martin, Dr.

Henkes Liliane, Dr.

Borregaard Saskia, Dr.

  

Development of cost data set

AOK Nordost

Kornek Stefanie

Schönfelder Moritz

 

DAK-Gesundheit

Wobbe Stefanie

  

TK

Kettlitz Mandy

Petereit Frank

 

Abbreviations

ACE: 

angiotensin converting enzyme

AE: 

adverse event

AHA: 

American Heart Association, staging of heart failure

ARB: 

angiotensin receptor blocker

BMI: 

body mass index

CHF: 

chronic heart failure

CI: 

confidence interval

CM: 

cardiomyopathy

COPD: 

chronic obstructive pulmonary disease

CRT-D: 

cardiac resynchronization therapy combined with defibrillation

DSL: 

Digital Subscriber Line (broadband connection)

ECG: 

electrocardiogram

eCRF: 

electronic Case Report Form

ESC: 

European Society of Cardiology

GFR: 

glomerular filtration rate

GP: 

general practitioner

HF: 

heart failure

ICD: 

implantable cardioverter defibrillator

ICER: 

Incremental Cost-Effectiveness Ratio

INH: 

Interdisciplinary Network for Heart failure study

KCCQ: 

Kansas City Cardiomyopathy Questionnaire with 23 items for measuring disease-specific domains in CHF

LBBB: 

left bundle branch block

LVEDD: 

left ventricular end-diastolic dimension

LVEF: 

left ventricular ejection fraction

MR: 

mineralocorticoid receptor

MWD: 

minute walking distance

n.a.: 

not available

NYHA: 

New York Heart Association, classification of heart failure

PAD: 

peripheral arterial disease

RBBB: 

right bundle branch block

RCT: 

randomized controlled trial

SAE: 

serious adverse event

SF-36v2: 

short form health survey with 36 questions using norm-based scoring

TELE-HF: 

Telemonitoring in patients with Heart Failure trial

TIA: 

transient ischemic attack

TIM-HF: 

Telemedical Interventional Management in Heart Failure trial

UMTS: 

Universal Mobile Telecommunications System (mobile cellular system)

WHO-5: 

World Health Organization Five, well-being index

Declarations

Funding

The CardioBBEAT trial is supported by the Federal Ministry of Education and Research in cooperation with (and managed by) the German Aerospace Center (grant number 01KX0805). The home telemonitoring system Motiva® is provided by Philips Medical Systems GmbH, the electronic infrastructure by T-Systems International GmbH.

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Institute for Healthcare Management and Health Sciences, University of Bayreuth
(2)
Rehabilitation Center for Internal Medicine
(3)
Center of Rehabilitation Research, University of Potsdam
(4)
Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf
(5)
Professional Board of German Surgeons
(6)
Radiology, University Medical Center of Regensburg
(7)
Clinic for Cardiology, University Hospital Essen
(8)
Comprehensive Heart Failure Center Würzburg and Department of Internal Medicine I, University of Würzburg
(9)
Contilia Heart and Vascular Center, Department of Cardiology and Angiology, Elisabeth Hospital Essen
(10)
Department of Medicine/Cardiology, German Heart Institute Berlin

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Copyright

© Hofmann et al. 2015

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