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Effectiveness of Remote Patient Monitoring After Discharge of Hospitalized Patients With Heart Failure The Better Effectiveness After Transition–Heart Failure (BEAT- HF) Randomized Clinical Trial Michael K. Ong, MD, PhD; Patrick S. Romano, MD, MPH; Sarah Edgington, MA; Harriet U. Aronow, PhD; Andrew D. Auerbach, MD, MPH; Jeanne T. Black, PhD, MBA; Teresa De Marco, MD; Jose J. Escarce, MD, PhD; Lorraine S. Evangelista, RN, PhD; Barbara Hanna, RN, PhD; Theodore G. Ganiats, MD; Barry H. Greenberg, MD; Sheldon Greenfield, MD, MPH; Sherrie H. Kaplan, PhD, MPH; Asher Kimchi, MD; Honghu Liu, PhD; Dawn Lombardo, MD; Carol M. Mangione, MD, MSPH; Bahman Sadeghi, MD, MBS; Banafsheh Sadeghi, MD, PhD; Majid Sarrafzadeh, PhD; Kathleen Tong, MD; Gregg C. Fonarow, MD; for the Better Effectiveness After Transition–Heart Failure (BEAT-HF) Research Group

IMPORTANCE It remains unclear whether telemonitoring approaches provide benefits for patients with heart failure (HF) after hospitalization.

OBJECTIVE To evaluate the effectiveness of a care transition intervention using remote patient monitoring in reducing 180-day all-cause readmissions among a broad population of older adults hospitalized with HF.

DESIGN, SETTING, AND PARTICIPANTS We randomized 1437 patients hospitalized for HF between October 12, 2011, and September 30, 2013, to the intervention arm (715 patients) or to the usual care arm (722 patients) of the Better Effectiveness After Transition–Heart Failure (BEAT-HF) study and observed them for 180 days. The dates of our study analysis were March 30, 2014, to October 1, 2015. The setting was 6 academic medical centers in California. Participants were hospitalized individuals 50 years or older who received active treatment for decompensated HF.

INTERVENTIONS The intervention combined health coaching telephone calls and telemonitoring. Telemonitoring used electronic equipment that collected daily information about blood pressure, heart rate, symptoms, and weight. Centralized registered nurses conducted telemonitoring reviews, protocolized actions, and telephone calls.

MAIN OUTCOMES AND MEASURES The primary outcome was readmission for any cause within 180 days after discharge. Secondary outcomes were all-cause readmission within 30 days, all-cause mortality at 30 and 180 days, and quality of life at 30 and 180 days.

RESULTS Among 1437 participants, the median age was 73 years. Overall, 46.2% (664 of 1437) were female, and 22.0% (316 of 1437) were African American. The intervention and usual care groups did not differ significantly in readmissions for any cause 180 days after discharge, which occurred in 50.8% (363 of 715) and 49.2% (355 of 722) of patients, respectively (adjusted hazard ratio, 1.03; 95% CI, 0.88-1.20; P = .74). In secondary analyses, there were no significant differences in 30-day readmission or 180-day mortality, but there was a significant difference in 180-day quality of life between the intervention and usual care groups. No adverse events were reported.

CONCLUSIONS AND RELEVANCE Among patients hospitalized for HF, combined health coaching telephone calls and telemonitoring did not reduce 180-day readmissions.

TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT01360203 JAMA Intern Med. 2016;176(3):310-318. doi:10.1001/jamainternmed.2015.7712 Published online February 8, 2016. Last corrected on April 18, 2016.

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Author Affiliations: Author affiliations are listed at the end of this article.

Group Information: The Better Effectiveness After Transition–Heart Failure (BEAT-HF) Research Group members include the authors of the present article and other investigators listed at the end of this article.

Corresponding Author: Michael K. Ong, MD, PhD, Department of Medicine, University of California, Los Angeles, 10940 Wilshire Blvd, Ste 700, Los Angeles, CA 90024 ([email protected]).

Research

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H eart failure (HF) is a prevalent condition in the United States, affecting 5.8 million patients,1 and is associ- ated with high hospitalization and readmission rates,

mortality, and cost of care.1-6 For patients with HF, disconti- nuities and lack of post–acute care monitoring can increase overall health care resource use through readmissions or wors- ened morbidity.7,8 Persistently high readmission rates for pa- tients with HF suggest that further improvements to existing care transition approaches are needed,1,9 as evidenced by the readmission-related financial penalties of approximately $428 million affecting 2610 hospitals in the third year of the Cen- ters for Medicare & Medicaid Services Hospital Readmission Reduction Program.10

Interventions to improve the care transition process have been shown to reduce readmissions while potentially improv- ing morbidity and mortality in randomized clinical trials (RCTs),11-14 particularly for patients with HF.15 However, many of these interventions were tested in single centers with lim- ited numbers of patients. Moreover, sustainability of research- derived care transition approaches is difficult, with many re- quiring intensive in-person interactions that are not always acceptable to patients16,17 and incurring costs to health pro- fessional organizations that may not be favorable under cur- rent health care financing arrangements.18 Telehealth tech- nology, including mobile health and remote patient monitoring technologies, potentially provides more cost-effective solu- tions to the problems of financial viability and home visit ac- ceptability by substituting for in-person interactions. How- ever, its effectiveness to date (particularly in patients with HF) has been mixed. The largest RCT in the United States to date in this area, Telemonitoring to Improve Heart Failure Out- comes, did not show any significant benefit from its tele- health approach,19 perhaps because of the type of technol- ogy used, low adherence rates, lack of patient engagement before discharge, or handling of values that exceeded thresh- old variables.19,20 Another large RCT in Europe with high ad- herence rates and improved technology also showed no sig- nificant benefit.21 However, systematic reviews that include these studies continue to suggest significant reductions in mor- tality, morbidity, and HF-related hospitalizations.22-24

The objective of the Better Effectiveness After Transition– Heart Failure (BEAT-HF) study was to evaluate the effective- ness of a care transition intervention using remote patient monitoring in reducing 180-day all-cause readmissions among a broad population of older adults hospitalized with HF. It was designed to address issues identified with the Telemonitor- ing to Improve Heart Failure Outcomes RCT, including using newer remote monitoring approaches, engaging patients be- fore discharge, and pairing remote monitoring with a tele- phone-based nurse care manager via scheduled contacts simi- lar to in-person care transition programs.

Methods Study Design The BEAT-HF study was a prospective, 2-arm (with a 1:1 ran- domization) multicenter RCT conducted at 6 academic medi-

cal centers in California to compare usual care with a telehealth- based care transition intervention for older patients who are discharged home after inpatient treatment for decompen- sated HF.25 Five of the sites are part of the University of Cali- fornia system, including the University of California in Davis, Irvine, Los Angeles, San Diego, and San Francisco. The sixth location is Cedars-Sinai Medical Center in Los Angeles, which has a mixed-model medical staff that includes full-time fac- ulty, a multispecialty group practice, and many independent private physicians. Three of the sites are major heart trans- plant centers, and an additional 3 serve as safety-net hospi- tals for their respective regions. Block randomization was con- ducted within each site using random blocks of 4 to 8 individuals via a web-based, computerized, random number generator. The study was approved by the University of Cali- fornia, Los Angeles (UCLA) institutional review board, and all other study institutions were subject to the UCLA institu- tional review board review. A data and safety monitoring board was convened for the study and reviewed data during the study enrollment period. The study was registered at clinicaltrial- s.gov (NCT01360203). The full study protocol can be found in the Supplement.

Patient Population Individuals admitted as hospital inpatients or on observation status were eligible if they were 50 years or older, were receiv- ing active treatment for decompensated HF (defined as HF with the initiation of or an increase in diuretic treatment), were ex- pected to be discharged to their home, and were capable of pro- viding written informed consent in English, Spanish, Farsi, or Russian. Enrollment criteria were expanded in January 2012 to include all patients being actively treated for HF instead of just those having a principal diagnosis of HF. This change was made because patients deemed prospectively as not having a principal diagnosis of HF were being coded as patients with HF after their discharge because of patients with multiple ac- tive problems.

The study exclusions can be grouped into 3 main categories.25 First were patients who did not have the cogni- tive or physical ability (eg, dementia or weight >204 kg) or ac- cess to resources (eg, working telephone or usual source of care) required to participate fully in the BEAT-HF intervention. Sec- ond were patients already in a system of care providing more health professional contacts than the planned intervention (eg, living in a skilled nursing facility, receiving chronic hemodi- alysis, or awaiting or having received an organ transplant). Third were patients whose HF was due to a cardiovascular con- dition that was expected to improve because of medical in- tervention (eg, percutaneous coronary intervention or inter- ventional valve procedure during hospitalization).

Intervention The intervention consisted of the following 3 components con- ducted by registered nurses: predischarge HF education, regu- larly scheduled telephone coaching, and home telemonitor- ing of weight, blood pressure, heart rate, and symptoms.25 The predischarge health education was conducted by a study nurse who was not part of the usual care team. The nurse guided pa-

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tients through a booklet developed for patients with low health literacy that covered an explanation of HF, medication adher- ence, salt avoidance, fluid monitoring, exercising with HF, and daily checkup of weight and edema, as well as when to call the HF treatment team.26 The study nurse used the “teach-back” method to ensure patient understanding.27,28 The predis- charge education also included a demonstration of how to use the remote home telemonitoring equipment and an explana- tion of why monitoring physiological variables is important for patients.

The electronic equipment (Bluetooth enabled; Bluetooth SIG, Inc) consisted of a wireless transmission pod, a weight scale, and a blood pressure and heart rate monitor integrated with a device that could display text questions and send simple text responses. Devices automatically transmitted data back to central servers for telemonitoring review by telephone call center study nurses based at the primary study site.

Intervention patients were scheduled to receive 9 tele- phone coaching calls over a 6-month period, generally from the same call center nurse, who had access to patients’ medi- cal histories and medication records. The nurse first con- tacted each enrolled patient 2 or 3 days after discharge from the hospital to reinforce the predischarge health coaching top- ics. Subsequent telephone nurse coaching then occurred on a weekly basis during the first month after discharge. After the first month, nurse coaching telephone calls were made monthly until the end of the 6-month study period. All telephone calls covered content reinforcing the predischarge education ma- terials. Intervention patients were asked to use the telemoni- toring equipment daily to transmit their weight, blood pres- sure, heart rate, and responses to 3 symptom questions, which were sent via cellular bandwidth to a secure server and were accessed daily by the telephone call center nurses. Readings that exceeded predetermined threshold variables generated a trigger for the telephone call center nurse to telephone the patient to investigate potential causes. When symptoms were concerning, patients were encouraged to contact their health professionals, although these individuals were also notified by the telephone call center nurses. If deemed necessary, the tele- phone call center nurses advised patients to call 911 or go to their nearest hospital emergency department. Telephone call center nurses also called patients who had stopped transmit- ting data to determine the reason and encourage the patient to resume daily monitoring.

Usual care at the sites included robust predischarge edu- cation and often a postdischarge follow-up telephone call.29

No additional surveillance was provided to control patients be- yond whatever may have been requested as part of routine clinical practice, and the intervention did not substitute for usual care surveillance. Patients were not precluded from ex- posure to other readmission reduction or chronic disease man- agement programs implemented by hospitals, physician groups, or health plans, such as education about HF, pharma- cist consultation, and postdischarge telephone calls.

For all participants, enrollment nurses conducted base- line surveys via in-person interviews before randomization. On completion, patients were randomized via the web-based enrollment software, with randomization notification pro-

vided by the enrollment nurse. All participants were con- tacted for survey interviews at 7 days, 30 days, and 180 days after discharge by staff at the coordinating center who were unaware of the treatment randomizations. During these tele- phone interviews, information was collected about quality of life, satisfaction with care, and use of medications.

Outcome Measures The primary outcome measure was 180-day all-cause read- mission. Secondary outcomes reported herein include 30- day all-cause readmission, 30-day mortality, and 180-day mortality.25 Readmissions were identified from participating sites’ hospitalization data, combined with California’s inpa- tient discharge data for hospitalizations at nonstudy sites ob- tained from the California Department of Public Health Of- fice of Statewide Health Planning and Development. Mortality was assessed using the Social Security and National Death In- dex, hospital data systems, contact with family members, and searches of obituaries.30 Quality of life was measured using the Minnesota Living With Heart Failure Questionnaire con- ducted via computer-assisted telephone interview.30

Statistical Analysis Our sample size provided 80% power to detect a relative re- duction of 28% in the primary outcome of 180-day readmis- sion with a type I error of 0.05 after adjusting for within- hospital clustering. We conducted unadjusted intent-to-treat analyses. Individuals who had fully withdrawn consent were censored on the date of withdrawal in hazard models for the primary outcome and for secondary outcomes related to re- admission and mortality. We conducted unadjusted analy- ses, followed by prespecified multivariable analyses to adjust for patient characteristics that may have been unequally dis- tributed across treatment groups and may have influenced out- comes. These multivariable analyses include logistic regres- sion models for readmission and mortality analyses. Models controlled for age, sex, race/ethnicity, insurance, comorbidi- ties based on the Health Care Utilization Project methods,6 year and quarter of enrollment, social isolation as measured by the Lubben Social Network Scale score,31 and income level. En- rollment site was controlled for using random effects. Mod- els also controlled for baseline quality-of-life scores in quality- of-life score analyses. Quality-of-life 30-day and 180-day analyses were only conducted for those individuals who re- ported quality-of-life data at the analyzed time point. After ad- justing for days alive, adherence was measured in each mea- surement period separately for health coaching telephone calls (as the percentage of protocol-required calls that were com- pleted) and for telemonitoring (as the percentage of days trans- mitting any type of data using telemonitoring).

Results We assessed 30 844 individuals between October 12, 2011, and September 30, 2013, for study eligibility (Figure 1). Of these in- dividuals, 28 476 did not meet inclusion criteria, including 18 005 patients without decompensated HF, 1383 transplant

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patients or candidates, and 1122 hemodialysis patients. An- other 550 individuals declined to participate, and 381 indi- viduals were discharged or died before they could be ap- proached regarding the study. In total, 1437 patients were enrolled and randomized in the study, with 715 randomized to the intervention and 722 randomized to usual care. Thirty- three participants (20 intervention and 13 usual care) com- pletely withdrew from the study. No individuals withdrew be- cause of adverse events. There were no significant differences in participant characteristics (Table 1 and Table 2) between in- tervention and control participants. The median age of par- ticipants was 73 years. In total, 46.2% (664 of 1437) were fe- male, 22.0% (316 of 1437) were African American, and 61.2% (880 of 1437) had a New York Heart Association classification of III or IV during their enrollment hospitalization.

Overall, 82.7% (591 of 715) of intervention participants used the telemonitoring equipment. Telemonitoring adherence to greater than 50% of days was documented in 55.4% (396 of 715) of intervention patients at 30 days and in 51.7% (370 of 715) at 180 days, while telephone coaching adherence to greater than 50% of calls was 61.4% (439 of 715) of intervention patients at 30 days and 68.0% (486 of 715) at 180 days. There were 221 211 remote patient observations, including 18 531 observations that exceeded threshold variables, with a median of 22 (interquar- tile range [IQR], 8-48) per intervention patient. There were 3700 scheduled health coaching telephone calls completed, with a median of 6 (IQR, 3-8) per intervention patient.

Primary Outcome The overall proportion of study participants who experi- enced our primary outcome (unadjusted, 180-day all-cause re- admission) was 50.0% (718 of 1437) (Table 3). There was no sig-

nificant difference detected in unadjusted (P = .42) or adjusted (P = .39) analyses between the proportion of intervention par- ticipants (50.8% [363 of 715]) or usual care participants (49.2% [355 of 722]) with 180-day all-cause readmission. The unad- justed hazard ratio for 180-day all-cause readmission with re- ceipt of the intervention was 1.03 (95% CI, 0.89-1.19; P = .73). The adjusted hazard ratio for 180-day all-cause readmission with receipt of the intervention was 1.03 (95% CI, 0.88-1.20; P = .74) (Figure 2). Subgroup analyses for our primary out- come showed no significant differences for those 65 years or older, male or female sex, race/ethnicity categories, or New York Heart Association classification. None of the subgroups showed evidence of meaningful effect modification (Table 4).

Secondary Outcomes Findings for 30-day readmission mirrored those for 180-day readmission. The overall proportion of study participants with 30-day all-cause readmission was 22.7% (162 of 715) (Table 2). There was no significant difference detected in unadjusted (P = .56) or adjusted (P = .63) analyses between the propor- tion of intervention participants (22.7% [162 of 715]) or usual care participants (21.6% [156 of 722]) with 30-day all-cause re- admission. The unadjusted hazard ratio for 30-day all-cause readmission with receipt of the intervention was 1.03 (95% CI, 0.83-1.29; P = .77). The adjusted hazard ratio for 30-day all- cause readmission with receipt of the intervention was 1.01 (95% CI, 0.80-1.28; P = .91) (Figure 2).

The overall proportion of study participants with 30-day all-cause mortality was 4.4% (63 of 1437) (Table 2). Nonsig- nificant differences were detected in unadjusted analysis (P = .06) and significant differences in adjusted analysis (P = .04) between the proportion of intervention participants

Figure 1. BEAT-HF CONSORT Flow Diagram

30 844 Assessed for eligibility

29 407 Excluded 28 476 Did not meet inclusion criteria

6 Died before approach

550 Declined to participate 375 Discharged before approach

1437 Randomized

722 Randomized to usual care 722 Received allocated

intervention 0 Did not receive allocated

intervention

722 Included in analysis 715 Included in analysis

307 Discontinued intervention 106 Died

13 Withdrew 188 Lost to follow-up (includes withdrawal from survey and unreached)

715 Randomized to intervention 715 Received allocated

intervention 0 Did not receive allocated

intervention

92 Died 20 Full withdrawal 87 Withdrawal from

intervention 107 Lost to follow-up (includes withdrawal from survey and unreached)

306 Discontinued intervention

BEAT-HF indicates Better Effectiveness After Transition–Heart Failure; CONSORT, Consolidated Standards of Reporting Trials.

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(3.4% [24 of 715]) or usual care participants (5.4% [39 of 722]) with 30-day all-cause mortality. The unadjusted hazard ratio for 30-day all-cause mortality with receipt of the interven- tion was 0.61 (95% CI, 0.37-1.02; P = .06), and the adjusted haz- ard ratio for 30-day all-cause mortality with receipt of the in- tervention was 0.53 (95% CI, 0.31-0.93; P = .03) (Figure 2). Review of the timing of deaths indicates that this finding was because of in-hospital death differences after randomiza- tion, which would make it less likely to be owing to the inter- vention.

The overall proportion of study participants with 180- day all-cause mortality was 14.9% (214 of 1437) (Table 2). There was no significant difference detected in unadjusted (P = .34) or adjusted (P = .30) analyses between the proportion of in- tervention participants (14.0% [100 of 715]) or usual care par- ticipants (15.8% [114 of 722]) with 180-day all-cause mortal- ity. The hazard ratio for 180-day all-cause mortality with receipt of the intervention was 0.88 (95% CI, 0.67-1.15; P = .32), and the adjusted hazard ratio for 180-day all-cause mortality with receipt of the intervention was 0.85 (95% CI, 0.64-1.13; P = .26) (Figure 2).

The overall mean, 30-day quality-of-life score was 31.23, and the overall mean, 180-day quality-of-life score was 30.49 (Table 3). There was a significant difference in 180-day quality- of-life scores between the intervention participants (mean, 28.50) and the control participants (mean, 32.63) in unad- justed (P = .02) and adjusted (P = .02) analyses.

Discussion

The BEAT-HF study is one of the largest RCTs of remote pa- tient telemonitoring in an HF population. It was designed to determine the effectiveness of the intervention using a broad population of patients hospitalized with HF that would be con- sistent with actual practice. Similar to other large RCTs of te- lemonitoring, we did not find significant effects of the BEAT-HF intervention on all-cause readmission within the first 30 or 180 days. The physiological signals of changes in daily weights and increased symptoms may not provide adequate warning of im- pending decompensation in patients with HF.20,32 Trials of im- planted hemodynamic monitoring systems in ambulatory pa- tients with HF have shown that weight is a poor surrogate for filling pressures and is not a reliable signal for impendent decompensation.33 However, readmission is also increas- ingly recognized as a complex phenomenon, the cause of which is not solely limited to physiological variables.34 In addition,

Table 1. Baseline Demographic Characteristics

Variable Intervention Usual Care Age, median (interquartile range), y

73 (62-84) 74 (63-82)

Sociodemographics, Mean % (95% CI)

Female sex 46.6 (42.9-50.2) 47.1 (42.8-51.4)

Race/ethnicity

African American 21.5 (18.5-24.5) 22.7 (19.6-25.8)

Hispanic/Latino 12.0 (9.6-14.3) 10.9 (8.6-13.1)

White 54.7 (51.0-58.4) 54.3 (50.7-58.0)

Asian/Pacific Islander or other

11.8 (9.4-14.2) 12.1 (9.7-14.5)

Insurance

Private and other 18.4 (15.5-21.3) 17.6 (14.8-20.4)

Medicaid 10.0 (7.7-12.2) 10.4 (8.1-12.7)

Medicare 44.9 (41.1-48.7) 45.3 (41.6-49.0)

Medicare and Medicaid 26.7 (23.3-30.0) 26.7 (23.4-30.0)

Income, $

<25 000 31.3 (27.8-34.7) 31.7 (28.3-35.2)

25 000 to 50 000 19.6 (16.7-22.5) 20.9 (17.9-23.9)

>50 000 to 75 000 11.1 (8.8-13.4) 12.1 (9.7-14.5)

>75 000 17.9 (15.1-20.7) 13.1 (10.6-15.6)

Refused to answer or did not know

20.2 (17.2-23.1) 22.2 (19.1-25.3)

Social isolation 21.4 (18.4-24.5) 21.1 (18.0-24.1)

Table 2. Baseline Comorbidities, Heart Failure Severity, and Discharge Medications

Intervention Usual Care Comorbidities, Mean % (95% CI)

Valvular disease 36.2 (32.6-39.9) 34.3 (30.8-37.9)

Pulmonary circulation disease 23.9 (20.6-27.0) 22.9 (19.8-26.1)

Peripheral vascular disease 13.3 (10.7-15.8) 11.5 (9.2-13.9)

Other neurological disorder 5.7 (4.0-7.5) 5.9 (4.2-7.7)

Chronic pulmonary disease 32.4 (28.9-35.9) 32.5 (29.0-36.0)

Diabetes mellitus

Without chronic complications

34.0 (30.4-37.6) 35.8 (32.2-39.4)

With chronic complications 10.8 (8.4-13.1) 11.8 (9.4-14.2)

Hypothyroidism 20.8 (17.7-23.8) 20.3 (17.3-23.4)

Renal failure 39.0 (35.3-42.7) 42.7 (39.0-46.4)

Liver disease 7.1 (5.1-9.0) 5.6 (3.9-7.3)

Rheumatoid arthritis or collagen vascular disease

4.6 (3.0-6.1) 3.6 (2.2-5.0)

Obesity 17.1 (14.2-19.9) 16.5 (13.7-19.2)

Deficiency anemia 34.0 (30.4-37.6) 32.0 (28.6-35.5)

Depression 10.6 (8.3-12.9) 11.1 (8.8-13.5)

Hypertension 81.7 (78.8-84.7) 80.1 (77.1-83.1)

Heart Failure Severity, Mean % (95% CI)

Ejection fraction 42.7 (41.3-44.3) 43.0 (41.6-44.3)

New York Heart Association classification

I 0.2 (0.0-0.5) 0.7 (0.0-1.4)

II 23.4 (20.0-26.9) 25.8 (22.2-29.4)

III 65.6 (61.8-69.4) 63.9 (59.9-67.8)

IV 10.8 (8.3-13.3) 9.6 (7.2-12.0)

Discharge Medications, Mean % (95% CI)

Angiotensin-converting enzyme inhibitor or angiotensin receptor blocker

56.6 (52.8-60.3) 54.6 (50.9-58.4)

β-Blocker 73.2 (69.8-76.5) 76.1 (72.9-79.4)

Digoxin 16.7 (13.9-19.6) 17.3 (14.5-20.2)

Loop diuretic 80.3 (77.2-83.3) 77.7 (74.6-80.9)

Aldosterone antagonist 18.9 (15.9-21.8) 19.7 (16.7-22.7)

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all participating sites were already focused on readmissions among patients with HF because of impending potential pen- alties from the Hospital Readmission Reduction Program and had implemented readmission reduction efforts.29 Similar types of interventions potentially could show effects among patients who have not previously been the focus of readmis- sion reduction efforts.

Although the primary and secondary readmission out- come measures were not met, we found that the BEAT-HF in- tervention had significant effects on adjusted analyses of the prespecified secondary outcome measure of 30-day mortal- ity. However, review of deaths suggests that this finding was a result of in-hospital death differences after randomization, which would make it less likely to be due to the intervention.

We also found that the BEAT-HF intervention had signifi- cant effects on quality of life among 180-day survey respon- dents. Findings for quality of life are limited by survey re- sponse rates. Although these rates were stable between 30 days and 180 days after accounting for mortality, survey nonre- spondents differed in baseline characteristics from survey re- spondents and potentially could have different quality-of- life outcomes. Further study would be required to validate this finding because the BEAT-HF trial was not specifically de- signed or powered for this outcome.

The BEAT-HF study had several limitations. Because the major source of the BEAT-HF funding was derived from Ameri- can Recovery and Reinvestment Act of 2009 funds, we could not extend enrollment beyond September 30, 2013. Doing so may have strengthened the 30-day mortality findings be- cause the study was not powered for this specific outcome. The study sites are all academic medical centers in California, which could limit generalizability. However, half of the sites in- cluded safety-net hospitals, and the broad patient eligibility

criteria increase generalizability. The use of other types of per- sonnel instead of registered nurses potentially could have af- fected study outcomes. The intervention was not directly in- tegrated with the physician practices caring for the patients, which is increasingly possible with advances in electronic health records. The effectiveness of transition of care, dis- ease management, and telemonitoring interventions may be highly dependent on how they are integrated and adhered to in practice.32 Adherence to the BEAT-HF intervention ap- pears to have been a critical factor. Despite deploying several strategies to promote patient engagement and foster adher- ence with the telemonitoring and telephone call center inter- vention, only 61.4% (439 of 715) and 55.4% (396 of 715) of pa- tients randomized to the intervention were more than 50% adherent to telephone calls and telemonitoring, respectively, within the first 30 days. Remote patient monitoring has also experienced significant technological change, including in- creasing use of tablets and other remote sensors. Newer ap- proaches, such as implantable devices, could increase adher- ence or provide better information to identify problems after discharge.

Conclusions The BEAT-HF study found that a combination of remote pa- tient monitoring with care transition management did not re- duce 180-day all-cause readmission after hospitalization for HF. Hospitalizations in the first 30 days and 180-day mortal- ity were also not reduced with the intervention. Individuals participating in this intervention may experience quality-of- life improvements at 180 days. However, further studies would be needed to confirm these findings.

ARTICLE INFORMATION

Correction: This article was corrected on March 14, 2016, to fix errors in the text and Table 3.

Accepted for Publication: November 16, 2015.

Published Online: February 8, 2016. doi:10.1001/jamainternmed.2015.7712.

Author Affiliations: Department of Medicine, University of California, Los Angeles (Ong, Edgington, Escarce, Mangione, Bahman Sadeghi, Fonarow); Department of Medicine, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, California (Ong); Department of Internal Medicine, University of California, Davis (Romano, Banafsheh Sadeghi, Tong); Department of

Pediatrics, University of California, Davis (Romano); Office of Nursing Research and Development, Cedars-Sinai Medical Center, Los Angeles, California (Aronow); Department of Medicine, University of California, San Francisco (Auerbach, De Marco); Department of Resource and Outcomes Management, Cedars-Sinai Medical Center, Los Angeles, California (Black); Department of Health

Table 3. Primary and Secondary Outcomes

Variable Total (N = 1437)

Intervention (n = 715)

Usual Care (n = 722) P Value

Adjusted P Valuea

Readmission, No. (%)

30 d 318 (22.1) 162 (22.7) 156 (21.6) .63 .63

180 d 718 (50.0) 363 (50.8) 355 (49.2) .54 .39

Mortality, No. (%)

30 d 63 (4.4) 24 (3.4) 39 (5.4) .06 .04

180 d 214 (14.9) 100 (14.0) 114 (15.8) .34 .30

Readmission or Mortality, No. (%)

30 d 359 (25.0) 173 (24.2) 186 (25.8) .49 .44

180 d 792 (55.1) 393 (55.0) 399 (55.3) .91 .93

Quality-of-Life Score, No. (Mean)b

30 d 988 (31.23) 485 (30.28) 503 (32.21) .25 .34

180 d 796 (30.49) 383 (28.50) 413 (32.63) .02 .02

a Adjusted P values are from multivariable logistic regression models for readmission and mortality analyses. Models controlled for age, sex, race/ethnicity, insurance, income, social isolation, comorbidities, year, and quarter of enrollment, with enrollment site controlled for as random effects.

b The Minnesota Living With Heart Failure Questionnaire30 has a total score that can range from 0 to 105. A lower score indicates less effect of heart failure on a patient’s quality of life.

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Policy & Management, University of California, Los Angeles (Escarce, Mangione); RAND Health, RAND Corporation, Santa Monica, California (Escarce); Program in Nursing Science, University of California, Irvine (Evangelista); School of Nursing, University of California, Davis (Hanna); Department of Family and Preventive Medicine, University of California, San Diego (Ganiats); Department of Family Medicine and Community Health, University of Miami, Miami, Florida (Ganiats); Department of Medicine, University of California, San Diego (Greenberg); Department of Medicine, University of California, Irvine (Greenfield, Kaplan, Lombardo); Division of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California (Kimchi); Division of Public Health & Community Dentistry, University of California, Los Angeles (Liu); Department of Computer Science, University of California, Los

Angeles (Sarrafzadeh); Department of Electrical Engineering, University of California, Los Angeles (Sarrafzadeh).

Author Contributions: Dr Ong and Ms Edgington had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Ong, Romano, Aronow, Auerbach, Black, De Marco, Escarce, Evangelista, Ganiats, Greenberg, Greenfield, Kaplan, Mangione, Banafsheh Sadeghi, Sarrafzadeh, Fonarow. Acquisition, analysis, or interpretation of data: Ong, Romano, Edgington, Aronow, Auerbach, Black, De Marco, Hanna, Ganiats, Greenberg, Greenfield, Kaplan, Kimchi, Liu, Lombardo, Bahman Sadeghi, Sarrafzadeh, Tong, Fonarow. Drafting of the manuscript: Ong, Edgington,

Aronow, Auerbach, Kaplan, Bahman Sadeghi. Critical revision of the manuscript for important intellectual content: Ong, Romano, Aronow, Auerbach, Black, De Marco, Escarce, Evangelista, Hanna, Ganiats, Greenberg, Greenfield, Kaplan, Kimchi, Liu, Lombardo, Mangione, Banafsheh Sadeghi, Sarrafzadeh, Tong, Fonarow. Statistical analysis: Ong, Edgington, Hanna, Kaplan, Liu. Obtained funding: Ong, Evangelista, Ganiats, Kimchi, Sarrafzadeh, Fonarow. Administrative, technical, or material support: Romano, Aronow, Auerbach, Black, Ganiats, Greenfield, Kaplan, Lombardo, Bahman Sadeghi, Banafsheh Sadeghi, Sarrafzadeh, Tong, Fonarow. Study supervision: Ong, Auerbach, De Marco, Escarce, Ganiats, Greenberg, Greenfield.

Figure 2. Hazard Ratios for Readmission and Mortality at 30 Days and 180 Days

1.0

0.8

0.6

0.4

0.2

0 0

722 715

10 20 30

526 530

H az

ar d

Ra tio

Time, d

Readmission at 30 dA

1.0

0.8

0.6

0.4

0.2

0 0

722 715

10 20 30

674 680

H az

ar d

Ra tio

Time, d

No. at risk Usual care Intervention

No. at risk Usual care Intervention

Mortality at 30 dC

1.0

0.8

0.6

0.4

0.2

0 0

722 715

60

459 454

120

367 364

180

310 302

H az

ar d

Ra tio

Time, d

Readmission at 180 dB

1.0

0.8

0.6

0.4

0.2

0 0

722 715

60

656 658

120

624 621

180

595 595

H az

ar d

Ra tio

Time, d

Mortality at 180 dD

Usual care Intervention

No. at risk Usual care Intervention

No. at risk Usual care Intervention

Dashed lines are for the intervention, and solid lines are for usual care. Adjusted hazard ratios, 95% CIs, and P values are from multivariable Cox proportional hazards regression models for readmission and mortality analyses. Models controlled for age, sex, race/ethnicity, insurance, income, social isolation, comorbidities, year, and quarter of enrollment, with enrollment site controlled for as random effects. A and B, The hazard ratio for 30-day readmission with the intervention is 1.03 (95% CI, 0.83-1.29; P = .77). The adjusted hazard ratio for 30-day readmission with the intervention is 1.01 (95% CI, 0.80-1.28; P = .91).

The hazard ratio for 180-day readmission with the intervention is 1.03 (95% CI, 0.89-1.19; P = .73). The adjusted hazard ratio for 180-day readmission with the intervention is 1.03 (95% CI, 0.88-1.20; P = .74). C and D, The hazard ratio for 30-day mortality with the intervention is 0.61 (95% CI, 0.37-1.02; P = .06). The adjusted hazard ratio for 30-day mortality with the intervention is 0.53 (95% CI, 0.31-0.93; P = .03). The hazard ratio for 180-day mortality with the intervention is 0.88 (95% CI, 0.67-1.15; P = .32). The adjusted hazard ratio for 180-day mortality with the intervention is 0.85 (95% CI, 0.64-1.13; P = .26).

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Conflict of Interest Disclosures: Dr De Marco reported being a consultant to Boston Scientific, Cardiokinetics, and Gambro and reported serving on advisory boards for Otsuka and Bayer. Dr Fonarow reported being a consultant to Amgen, Bayer, Baxter, Medtronic, and Novartis. No other disclosures were reported.

Funding/Support: This study was supported by grant R01HS019311 from the Agency for Healthcare Research and Quality; by grant RC2HL101811 from the National Heart, Lung, and Blood Institute (NHLBI); by grant UL1TR000124 from the National Center for Advancing Translational Science (NCATS) of the University of California, Los Angeles, Clinical and Translational Science Institute; by grant 66336 from the Robert Wood Johnson Foundation; by the Sierra Health Foundation; by the University of California Center for Health Quality and Innovation; and by the participating institutions. All grants were to Dr Ong except for the NHLBI (to Dr Mangione, the final principal investigator) and the NCATS (to Steven M. Dubinett, MD).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Group Information: In addition to the authors of the present article, other members of the Better Effectiveness After Transition–Heart Failure (BEAT- HF) Research Group (in alphabetical order) are Bruce Davidson, PhD, MBA, Hassan Ghasemzadeh, PhD, Michael Gropper, MD, MPH, and Michelle Mourad, MD. The BEAT-HF project managers are Arjang Ahmadpour, BS, Wendy Davila, RN, Susanne Engel, MBA, Ronald Jacolbia, BS, Herman Lee, BS, Laura Linares, RN, Elizabeth Michel, RN, Meghan Soulsby Weyrich, MS, and Elizabeth Zellmer, BS. The BEAT-HF postdischarge intervention nurses are Lida Esbati-Mashayekhi, RN, Elisabeth Haddad, RN, Marian Haskins, RN, Tianne Larson, RN, and Kathryn Pratt, RN. The BEAT-HF enrollment nurses are Hendry Ansorie, RN, Kymberly Aoki, RN, Ruth Baron, RN, Eileen Brinker, RN, Maureen Carroll, RN, Annette Contasti, RN, Anne Fekete, RN, Vivian

Guzman, RN, Linda Larsen, RN, Lisa Martinez, RN, Sharon Myers, RN, Melanee Schimmel, RN, Amanda Schnell-Heringer, RN, Arlene Taylor, RN, Tracy Tooley, RN, Geoffrey van den Brande, RN, and Evanthia Zaharias, RN. The BEAT-HF research staff are John Billimek, PhD, Rocio Castaneda, BS, Ma Elloi Delos Reyes, BS, Dana Fine, BS, Tammy Lo, BS, Xoan Luu, BA, Socorro Ochoa, BA, Mayra Perez, BA, David Rincon, BA, Fidelia Sillas, BA, Visith Uy, BS, Esther Wang, BS, Haiyong Xu, PhD, Stella Yala, MD, and Tingjian Yan, PhD.

Additional Information: The telemonitoring system vendor (IDEAL Life; IDEAL Life Inc) was paid for its services and had no role in the study design, the analysis of the data, or the writing of the manuscript. The study was designed by one of us (Dr Ong.), data were gathered by site coordinators and the UCLA coordinating center, and the data were analyzed by 2 of us (Dr Ong and Ms Edgington). One of us (Dr Ong) drafted the manuscript and made the decision to submit it for publication. All authors contributed to subsequent revisions and vouch for the accuracy and completeness of the data and analyses.

Additional Contributions: Meghan Soulsby Weyrich, MS, provided editorial assistance with the manuscript, for which she did not receive compensation outside of her usual salary. The California Department of Public Health Office of Statewide Health Planning and Development and the Social Security and National Death Index assisted with the data collection. We thank the BEAT-HF project managers, nurses, and research staff for their assistance with the project.

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Table 4. Subgroup Analyses for the Primary Outcome

Variable

No. (%)

P Value P Value for InteractionaTotal Intervention Usual Care

Age, y

≤65 (n = 452) 212 (46.9) 119 (51.1) 93 (42.5) .07 .10

>65 (n = 985) 506 (51.4) 244 (50.6) 262 (52.1) .65

Sex

Male (n = 772) 384 (49.7) 196 (51.3) 188 (48.2) .39 .38

Female (n = 664) 334 (50.3) 167 (50.2) 167 (50.5) .94

Race/ethnicity

African American (n = 316)

168 (53.2) 88 (57.5) 80 (49.1) .13 .41

Hispanic/Latino (n = 163) 78 (47.9) 44 (51.8) 34 (43.6) .30 .85

White (n = 779) 374 (48.0) 185 (47.6) 189 (48.5) .80 NA

Asian/Pacific Islander or other (n = 171)

98 (57.3) 46 (54.8) 52 (54.8) .51 .26

New York Heart Association classification

Not III or IV (n = 557) 247 (44.3) 115 (43.9) 132 (44.8) .84 .49

III or IV (n = 880) 471 (53.5) 248 (54.8) 223 (52.2) .45

Abbreviation: NA, not applicable. a P values for interaction are from

multivariable logistic regression models for 180-d readmission analyses. Models controlled for age, sex, race/ethnicity, insurance, income, social isolation, comorbidities, year, and quarter of enrollment, with enrollment site controlled for as random effects.

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