Abstract
Importance:
The comparative effectiveness of two transitions of care programs for improving health status and reducing readmissions in patients hospitalized with heart failure is unknown.
Objective:
To compare the effectiveness of mobile integrated health (MIH) with a transitions of care coordinator (TOCC) for improving health status and reducing 30-day all-cause readmissions.
Design, Setting, and Participants:
The MIGHTy-Heart trial randomized hospitalized patients with heart failure from 11 New York City hospitals between January 2021 and September 2024.
Interventions:
Participants were randomized 1:1 to MIH (n=1,005) or TOCC (n=998). TOCC provided a follow-up call with a nurse 48–72 hours after discharge. Mobile integrated health included the same TOCC post-discharge call, with the addition of ongoing nurse care coordination, community paramedic home visits, and facilitated, synchronous telehealth with emergency medicine physicians.
Main Outcomes and Measures:
Co-primary outcomes were health status at 30 days, measured with the Kansas City Cardiomyopathy Questionnaire Overall Summary score, and 30-day all-cause hospital readmission, with heart failure-specific readmissions as a secondary outcome.
Results:
Among 2,003 participants (median age, 67; 52% female; 47% Black; 27% Hispanic), no adjusted differences were observed in the Kansas City Cardiomyopathy Questionnaire Overall Summary score at 30 days between MIH and TOCC (mean difference, 1.83; 95% CI: −0.75 to 4.4; p=0.16). Exploratory analysis showed a significant age by treatment interaction effect (p=0.02), with younger participants receiving MIH having larger improvement in health status (β: 4.40, 95% CI: 1.01, 7.79). There were no significant differences in overall 30-day readmissions between study arms (20.3% vs. 20.4%; OR: 0.99; 95% CI, 0.83 to 1.19; p=0.95).
Conclusions and Relevance:
MIH confers no additional benefit on health status or 30-day readmissions for post-acute patients with heart failure compared to TOCC alone. Preliminary subgroup analyses suggest potential variations in MIH effects, and further research is warranted.
Funding:
Patient-Centered Outcomes Research Institute (PCORI), Award HS-2019C2–17373 (MPIs: Masterson Creber, Daniels). R01HL161458 (Masterson Creber)
Trial registration:
Clinicaltrials.gov: NCT04662541, Title: Mobile Integrated Health in Heart Failure
Introduction
Heart failure is the leading cause of hospitalization among older adults in the United States, and has the highest 30-day all-cause readmission rates (20% to 25%) among Medicare beneficiaries.1 These readmissions create significant financial and psychosocial burdens, especially for those facing adverse social determinants of health.1 Despite medical advances, many patients continue to experience poor health status and persistent symptoms.2,3
Multiple interventions have been tested to improve care transitions and post-discharge outcomes for patients hospitalized for heart failure,4 yet few comparative effectiveness trials have evaluated alternative strategies. The “Mobile Integrated Health and Telehealth to support transitions of care among patients with Heart failure” trial (MIGHTy-Heart) was a pragmatic, multicenter, randomized trial comparing two interventions—mobile integrated health (MIH) and a transitions of care coordinator (TOCC)—on 30-day health status and all-cause readmission rates.5 While both interventions have shown effectiveness in smaller trials or observational studies on post-acute healthcare utilization,6–9 and health status,4,10,11 they have not previously been directly compared in a randomized trial.
Methods
Trial Design and Oversight
The design and recruitment protocols for the MIGHTy-Heart trial (NCT04662541) have been described previously.5 The full trial protocol and original statistical analysis plan are available in Supplement 1 and Supplement 2 eAppendix 1, along with all other eTables, eFigures and eAppendices.
MIGHTy-Heart was conducted across 11 academic and community hospitals affiliated with two healthcare systems, NewYork-Presbyterian and Mount Sinai Health, in New York City (eAppendix 2, eFigure 1). The trial was intentionally designed to align with real-world clinical practice, as evidenced by a mean score of 4.6 out of 5 across the Pragmatic Explanatory Continuum Indicator Summary (PRECIS-2) domains (eFigure 2). The MIH and TOCC programs were operating within each health system prior to study initiation, with existing community paramedics and nurse care coordinators available to deliver the interventions. Operational changes, treatment protocols, and clinical decisions were made at the discretion of local medical directors in accordance with regional and New York state regulations.
A patient stakeholder and advisory board was established at the beginning of the study to incorporate patient and stakeholder perspectives throughout all trial phases (eAppendix 3). Study enrollment began January 4, 2021, and concluded September 27, 2024, with the final 30-day follow-up completed on October 28, 2024. Weill Cornell Medicine was the study data coordinating center (eFigure 3).
MIGHTy-Heart adhered to the Consolidated Standards of Reporting Trials (CONSORT) guidelines for randomized trials12 (Figure 1). The protocol was approved by the Biomedical Research Alliance of New York centralized institutional review board (protocol #20–08-329–380) with reliance agreements from participating sites. All participants provided written informed consent (Supplement 1).
Figure 1: Consort diagram.
Participants
Patients were eligible if they were hospitalized with heart failure at a participating NewYork-Presbyterian or Mount Sinai hospital, resided in New York City, were ≥18 years old, enrolled in Medicare or Medicaid, expected to be discharged home, and able to provide informed consent in English, Spanish, Mandarin, Russian, or French (eTable 1). Exclusion criteria included a diagnosis of dementia or unstable psychiatric illness. Because some patients with advanced heart failure had access to the MIH program as part of their routine care, patients with advanced heart failure were also excluded. Recruitment was conducted across the two health systems using a consistent strategy adapted to each site’s clinical workflow (eAppendix 4), including a custom screening dashboard (eTable 2) and a daily electronic health record (EHR)-generated patient list based on heart failure ICD-10 codes (eTable 3).
Once enrolled, participants completed a baseline survey capturing self-reported sociodemographic information and baseline health status using the: 23-item Kansas City Cardiomyopathy Questionnaire (KCCQ) (eTable 4).13,14 Following survey completion, participants were randomized 1:1 to MIH or TOCC, stratified by health system. All research staff and investigators were blinded to treatment assignment, except for the statisticians and healthcare professionals administering the interventions. A study crossover was defined as a participant who was randomly assigned to the TOCC group and then referred to MIH by their cardiologist or heart team within 30 days of hospital discharge.
Data Sources
Data for this study originated from patient-reported sociodemographic and outcomes surveys, and from multiple EHR data sources (eFigure 3), including the Patient-Centered Outcomes Research Institute (PCORI) INSIGHT Clinical Research Network, a research-focused information exchange that aggregates and standardizes patient data across healthcare systems within a specific network in a common data format,15 and each institution’s clinical data warehouse.
Study Interventions
Transitions of Care Coordinator
The TOCC intervention was implemented at the two health systems independent of the research study for all patients discharged after a heart failure exacerbation. TOCC consisted of a single follow-up phone call 48–72 hours after discharge by registered nurses employed by each health system. Calls were conducted to assess clinical status including new or worsening symptoms, review discharge instructions, identify unmet clinical and social needs, and reinforce patient education on medication adherence and lifestyle modifications. TOCC nurses could provide advice and education, encourage the patient to contact their primary or specialty care provider, or refer the patient to the emergency department.16,17 Due to the pragmatic nature of this study, each health system implemented and modified their TOCC interventions independently, so structured assessments were not implemented consistently between the hospital sites. One system used nurses who worked on the inpatient units where patients had been discharged from to complete TOCC calls and automated text messages to perform post-discharge follow-up, while the other used nurses who were employed at a centralized call center.
Mobile Integrated Health
Patients randomized to MIH received a follow-up call from a nurse care coordinator within 48–72 hours of discharge, as described above for TOCC, in addition to ongoing nurse care coordination, home visits by community paramedics, and telehealth visits with emergency medicine physicians, as needed. Based on clinical acuity, clinical judgement, and patient preference, a small team of trained nurse care coordinators made the outreach calls and could dispatch community paramedics equipped with Wi-Fi, tablets, and laptops to facilitate telehealth evaluations.
Community paramedics—experienced EMTs and paramedics with advanced heart failure training—completed standardized home assessments using REDCap checklists (eAppendix 5), conducted physical exams, reviewed discharge instructions, confirmed follow-up appointments, and performed medication reconciliation. Community paramedics administered treatments per EMS protocols and arranged ED transport when necessary. As part of the MIH program, community paramedics participated in mandatory joint educational sessions, including didactic lectures and simulated cases developed collaboratively by emergency medicine physicians and heart failure cardiologists in both hospital systems.
During telehealth visits, emergency medicine physicians could access clinical notes, discharge summaries, and medication lists from the EHR. They could consult the heart team in real time to adjust medications, arrange follow-up care, request diagnostics (e.g., electrocardiograms), and suggest treatments, such as IV diuretics. Encounter summaries were sent to the patient’s heart team.
Nurse care coordinators also addressed home-based needs, including nursing services, physical therapy, assistive devices, lab testing, transportation, and appointments. MIH services continued for up to three months, with further outreach or visits at the discretion of the nurse care coordinator.
Study Outcomes
Baseline measures (e.g. education, financial resources, and social isolation)14 were collected during hospitalization. Follow-up assessments were conducted electronically or by telephone by trained research personnel blinded to group assignment (eFigure 4). The co-primary outcomes, both measured at 30 days, were health status and all-cause readmission. Health status was assessed using the 23-item Kansas City Cardiomyopathy Questionnaire Overall Summary Score (KCCQ-OSS), ranging from 0 to 100 (higher scores indicate better health); a 5-point change is considered clinically meaningful.18 All-cause 30-day readmission was defined as any rehospitalization within 30 days of the index hospitalization discharge date and categorized as a binary variable. Thirty-day heart failure specific readmissions were defined as any rehospitalization using ICD-10 codes for the admitting or discharge diagnosis (eAppendix 6). Readmission outcomes were assessed using electronic health record data from INSIGHT and each institution’s clinical data warehouse (eAppendix 1).
Sample Size Calculation
Power calculations were based on simulations using historical data from the INSIGHT Clinical Research Network in collaboration with the Sponsor (PCORI). An alpha of 0.04 was allocated for 30-day all-cause readmissions and 0.01 for 30-day health status. Assuming a binary outcome for readmissions, a sample size of 2,000 (1,000 per group) provided ≥84% power to detect a 5% absolute reduction in readmissions at a 0.04 significance level using a minimum loss-based estimation. The sample size also provided ≥84% power to detect a 0.13 SD difference in health status using a two-group t-test at the 0.01 level (eAppendix 1).
Statistical Analysis
Main analyses were conducted using a complete-case approach. Baseline characteristics were compared using Wilcoxon rank sum tests for continuous variables and Chi-square tests for categorical variables. The initial protocol included time-to-event analysis via TMLE as the primary analytic approach; however, due to high administrative censoring at the 30-day timepoint (with only 20% of participants experiencing the event), we chose a weighted logistic regression model. This approach aligns with clinical decision-making at these fixed intervals and provides straightforward interpretation of treatment effects on event probability 19–21 (eAppendix 1).
Health status was assessed with multivariable linear regression. For 30-day all-cause readmissions and heart failure specific readmissions, a weighted logistic regression model was used to examine the differences in proportions of readmissions between study arms, accounting for class imbalance in readmission status.19–21 Models were run with and without adjustment for pre-specified covariates (eAppendix 1).
To assess heterogeneity of treatment effect prespecified interactions by age, sex and site were tested. Stratum-specific odds ratios or differences in marginal means were reported. Two-sided tests for main treatment effects used an overall alpha of 0.05 (0.04 for readmissions, 0.01 for health status). In exploratory subgroup analyses, we used a permutation test that randomly shuffled the subgroup labels to assess heterogeneity of treatment effects between subgroups. For each permutation we re-estimated subgroup-specific treatment effects, and calculated treatment differences between subgroups. A two-sided p-value was obtained by calculating the proportion of permutations where the absolute treatment difference between subgroups was equal to or greater than the observed absolute difference from 10,000 permutations. We also include weighted logistic regression and time to event analyses for both 60- and 90-day readmission data (eAppendix 1). Analyses were conducted in R version 4.2.2.
To address missing data for the co-primary outcome of health status at 30 days, several sensitivity analyses were conducted. First, multivariate imputation by chained equations (MICE) was used to handle missing KCCQ data. The covariates used in the imputation model, including the interaction terms, were the same as those that used in the primary analytic model. Fifty imputed datasets were generated and combined using Rubin’s rules.22 Second, inverse probability weighting (IPW) was used, based on the inverse of predicted probability from a logistic regression model estimating likelihood of missing 30-day KCCQ data, to more heavily weight the observations of those with complete data who were most like those missing 30-day KCCQ OSS (eAppendix 1).
Results
Among 2,012 patients randomized between January 4, 2021, and September 27, 2024, 2,003 were included in the analyses (1,005 to MIH and 998 to TOCC; Figure 1). Participants’ median age was 67 years (range 19 to 98) and 1,040 (52%) were women. A total of 937 (47%) were Black, 570 (28%) White, and 537 (27%) Hispanic. Participants had notable adverse social determinants of health, with 803 (40%) reporting not having enough money to make ends meet, and 369 (18%) not having graduated from high school. The recruitment was relatively balanced across health systems, with 1,142 (57%) recruited from health system 1 and 862 (43%) from health system 2. Baseline characteristics were similar between the MIH and TOCC arms (Table 1), except for modest differences by race and marital status.
Table 1:
Baseline Characteristics of the MIGHTy-Heart Participantsa
| Characteristic | MIH (N=1,005) | TOCC (N=998) |
|---|---|---|
| Age, median age (IQR), — yr | 67 (58, 78) | 68 (58, 78) |
| Age categorized – no. (%) | ||
| Age <70 | 435 (43) | 465 (47) |
| Age >=70 | 570 (47) | 533 (53) |
| Sex | ||
| Female | 529 (53) | 511 (51) |
| Male | 476 (47) | 487 (49) |
| Race-- no./total no. (%)b | ||
| American Indian/Alaskan Native | 1 (<0.1) | 8 (0.8) |
| Asian | 27 (3) | 28 (3) |
| Black or African-American | 504 (50) | 433 (43) |
| Hawaiian/Pacific Islander | 23 (2) | 17 (2) |
| More than one race | 41 (4) | 42 (4) |
| Other | 153 (15) | 154 (15) |
| White | 255 (25) | 315 (32) |
| Missing | 1 | 1 |
| Ethnicity — no. (%),b | ||
| Hispanicc | 274 (27) | 263 (27) |
| Non-Hispanicc | 725 (73) | 727 (73) |
| Missing | 6 | 8 |
| Marital status — no. (%) | ||
| Married/living with a partner | 283 (28) | 279 (28) |
| Divorced/separated/widowed | 263 (26) | 318 (32) |
| Single | 459 (46) | 400 (40) |
| Missing | 0 | 1 |
| Financial Resources — no. (%) | ||
| Not enough to make ends meet | 401 (40) | 402 (40) |
| Enough to make ends meet | 541 (54) | 526 (53) |
| More than enough to make ends meet | 62 (6.2) | 69 (6.9) |
| Missing | 1 | 1 |
| Education — no. (%) | ||
| Less than high school | 182 (18) | 187 (19) |
| Completed high school | 539 (54) | 537 (54) |
| College and/or graduate school | 281 (28) | 272 (27) |
| Missing | 3 | 2 |
| Hypertension — no. (%)c | 764 (76) | 710 (71) |
| Diabetes — no. (%)c | 463 (46) | 444 (45) |
| COPD — no. (%)c | 372 (37) | 363 (36) |
| Chronic kidney disease — no. (%)c | 457 (46) | 445 (45) |
| Chronic atrial fibrillation or flutter — no. (%)c | 330 (33) | 326 (33) |
| Stroke of transient ischemic attack — no. (%)c | 124 (12) | 113 (11) |
| Metastatic Cancer — no. (%)c | 29 (2.9) | 24 (2.4) |
| Previous hospital admissions within two years of index hospitalizationc | ||
| None— no. (%) | 327 (33) | 348 (35) |
| 1–2 hospitalizations — no. (%) | 376 (37) | 370 (37) |
| 3+ hospitalizations — no. (%) | 301 (30) | 279 (28) |
| Elixhauser Indexc | 15 (6, 23) | 15 (5, 23) |
| Charlson Comorbidity Indexc | 4 (2, 5) | 3 (1, 5) |
| KCCQ Overall Summary Score, mean (SD) | 46 (25) | 46 (26) |
| Missing | 0 | 1 |
| Recruitment | ||
| Total recruitment health system 1— no. (%) | 571 (57) | 571 (57) |
| Total recruitment health system 2— no. (%) | 435 (43) | 427 (43) |
| UCLA Loneliness Score | ||
| Lonely— no. (%) | 308 (32) | 301 (32) |
| Not Lonely— no. (%) | 659 (68) | 641 (68) |
| Missing | 38 | 56 |
Percentages may not total 100 because of rounding and missing data.
Race and ethnic group were reported by the participants from a list of options. “Other” was an available option for race, This characteristic has missing data from 2 participants, so percentages are of those with data
Among participants randomized to MIH (n=1,005), post-discharge follow-up calls were initiated, and 414 (41%) received a community paramedic home visit within 30 days of discharge. Eight participants assigned to TOCC were later referred to MIH and received MIH visits within 30 days of discharge. There were no adverse events related to the interventions (eAppendix 7). Overall, 17 participants died after randomization and before discharge, while 37 were discharged and died before 30 days. There were no differences between study arms (23 in MIH and 14 in TOCC, p=0.19).
Health status
Overall, 1,070 (53%) completed the 30-day KCCQ OS score. A table comparing the characteristics of patients who did and did not complete the 30-day KCCQ OS score is reported in eTable 5. The observed mean (SD) baseline KCCQ OS scores were 46±25 in the MIH arm and 46±26 in the TOCC arm (Table 1), with 30-day unadjusted mean KCCQ OS scores of 55.6±26 in the MIH arm and 53.2±25 in the TOCC arm. There were no significant differences in 30-day KCCQ OSS between the two arms in our overall model (MIH: 9.82 vs TOCC 7.99; adjusted mean difference: 1.83 (95% CI: −0.75 to 4.4; p=0.16). Age-specific subgroup analyses showed MIH was associated with better health status among younger patients (β=4.40, 95% CI: 1.01, 7.79), but not among older patients (β= −1.87, 95% CI: −5.91, 2.17); age-by-treatment interaction was statistically significant (p=0.02). Age-by-treatment effects were consistent across multiple sensitivity analyses (MI, p<0.01 and IPW, p=0.02) (eFigure 5). There were no significant site- or sex-by-treatment effects.
30-Day All-Cause Readmissions
Overall, 20.4% (20.3% with MIH and 20.4% with TOCC) were readmitted within 30 days of discharge. There were no significant main treatment effects on 30-day all-cause readmissions in unadjusted (OR: 0.99; 95% CI: 0.83, 1.19; p=0.95), or adjusted analyses (OR: 0.97; 95% CI: 0.80, 1.16; p=0.72). Sex-specific subgroup analyses showed MIH was associated with higher odds of 30-day readmission in males (OR = 1.19, 95% CI: 0.91,1.56) and lower odds in females (OR = 0.78, 95% CI: 0.60,1.01); however, the sex-by-treatment interaction was not statistically significant (p = 0.07) and there were no significant sex-specific treatment differences. There were no significant site- or age-by-treatment effects.
30-Day Heart Failure Readmissions
A total of 259 patients (12.9%) experienced a heart failure-specific readmission within 30 days. There were no significant main treatment effects on 30-day heart failure specific readmissions in unadjusted (OR: 0.90; 95% CI: 0.74, 1.09; p=0.27) or adjusted (OR: 0.90; 95% CI: 0.74, 1.10; p=0.32) analyses. Similarly, MIH was associated with higher odds of heart failure specific readmissions in males (OR = 1.16, 95% CI: 0.87, 1.54) and lower odds in females (OR = 0.70, 95% CI: 0.52, 0.92); however, this interaction effect was not statistically significant (p = 0.08) and there were no significant sex-specific treatment differences. There were no significant site- or age-by-treatment effects.
60 and 90-Day All-Cause Readmissions
There were no significant main treatment effects on 60- and 90-day all-cause readmissions in both the unadjusted (60 days: p=0.9; 90 days: p>0.9) and adjusted analyses (60 days: p>0.9; 90 days: p=0.6) (eAppendix1). There was also no further interaction with treatment at 60 days (sex-by-treatment: p=0.85; age-by-treatment: p=0.92; site-by-treatment: p=0.37) or 90 days (sex-by-treatment: p=0.84; age-by-treatment: p=0.30; site-by-treatment: p=0.13) (eAppendix1).
Discussion
More than 1 in 5 patients hospitalized for decompensated heart failure are readmitted within 30 days, highlighting the need for more effective post-acute care strategies. In this large, pragmatic randomized trial involving a racially and socioeconomically diverse cohort, no significant differences were observed in 30-day readmission rates or patient-reported health status between two effective post-discharge interventions: Mobile Integrated Health and Transitions of Care Coordinators. Exploratory analyses suggest significant interaction effects between age and treatment, with younger participants having better 30-day health status with MIH than TOCC. Collectively these findings do not support universal adoption of either strategy. However, future research may help refine the MIH program by identifying which components are most effective for optimizing the transitions of care for patients living with heart failure after hospitalization.
While there was no overall difference in health status scores between patients randomized to MIH compared to TOCC, the finding that health status outcomes with TOCC were significantly worse in younger participants is consistent with prior studies23 and may be explained by younger patients having a higher burden of adverse social determinants of health. The added at-home support and ongoing care coordination provided by MIH may have contributed to the observed increase in health status in this subgroup. Similarly, there were no overall differences in hospital readmissions. However, among women, a lower proportion of patients randomized to the MIH arm had 30-day all cause (OR: 0.78, 95% CI: 0.60, 1.01), or HF-specific hospital readmissions (OR: 0.70, 95% CI: 0.52, 0.92), warranting further study.
Limitations
These findings should be interpreted in the context of several potential limitations. MIGHTy-Heart was a pragmatic trial of existing transitions of care programs, and both TOCC and MIH were operationalized differently across the two health systems. The interventions were delivered by health system-employed professionals (nurse care coordinators, physicians, community paramedics) independent of the research team, enhancing real-world relevance and generalizability to medically underserved populations, but potentially diluting the fidelity of the interventions. It is noteworthy that MIH continues to be used in these health systems and some heart failure cardiologists deferred randomizing patients in MIGHTy-Heart because they felt that their patients needed MIH. The trial’s first year also coincided with the peak of the COVID-19 pandemic in New York City, which may have limited enrollment and reduced acceptance of in-home paramedic visits (eAppendix 8).
Of note, less than half of patients randomized to MIH received a home visit, likely reflecting a combination of clinical need, difficulties contacting patients post-discharge and patient preference. Those not receiving the full suite of MIH services ended up receiving a similar intervention as TOCC, likely diluting the potential benefit of MIH, but also reflecting real-world practice. Patients who did not receive an MIH visit did receive nurse care coordinator calls. During this call, the need for a MIH home visit was assessed by the care managers along with patients’ willingness to accept a home visit. While reasons for not receiving a MIH visit were not documented during the trial, exploratory analyses of patients who did and did not receive a visit suggest participants were similar with respect to key demographic characteristics.
Follow-up was limited by several factors common in pragmatic trials: unreachable with the provided contact information, competing demands following hospital discharge in a medically and socially complex population, and patient preference not to engage with further data collection. These factors help to explain the low response rate for the 30-day KCCQ outcome despite extensive outreach effort, including multiple phone, text, and email attempts by trained study coordinators (eFigure 4). Importantly, participants were not excluded from the trial based on their availability for follow-up survey completion, which enhances the generalizability of the findings. We used MICE and IPW sensitivity analyses to evaluate the potential impact of response bias due to missing KCCQ scores at 30 days. These analyses yielded similar results as the primary analysis (eFigure 5).
Conclusions
The MIGHTy-Heart trial demonstrates similar overall outcomes with respect to health status and 30-day readmissions, suggesting no added benefit of MIH to TOCC alone to support transitions of care for hospitalized patients with heart failure. The exploratory findings that MIH may have supported better health status in younger patients, underscores the importance of further research to better define optimal transition of care strategies for hospitalized patients with heart failure.
Supplementary Material
Figure 2. Heterogeneity of Treatment Effects for 30-Day Kansas City Cardiomyopathy Questionnaire (KCCQ) Overall Summary Scores Across Prespecified Subgroups.
MIH indicates mobile integrated health; TOCC, transitions of care coordinator. a. Adjusted β estimates show effect of MIH vs TOCC on 30-day KCCQ scores. b. P values derived from permutation-based tests
Figure 3. Heterogeneity of Treatment Effects for 30-Day All-Cause Readmissions Across Prespecified Subgroups.
MIH indicates mobile integrated health; OR, odds ratio; TOCC, transitions of care coordinator. a. Adjusted ORs examine odds of 30-day all-cause readmissions among MIH patients compared with TOCC patients. b. P values derived from permutation-based tests
Figure 4. Heterogeneity of Treatment Effects for 30-day Heart Failure (HF) Readmissions Across Prespecified Subgroups.
MIH indicates mobile integrated health; TOCC, transitions of care coordinator. a. Adjusted ORs examine odds of 30-day HF-specific readmissions among MIH patients compared with TOCC patients. b. P values derived from permutation-based tests.
Key Points.
Question
What is the added benefit of mobile integrated health compared with a transitions of care coordinator for hospitalized patients with heart failure?
Findings
Among 2,003 randomized patients, health status and 30-day readmissions were similar. However, exploratory analyses suggested mobile integrated health was associated with better health status in patients <70 years old and possible effect modification by sex on readmissions.
Meaning
The addition of mobile integrated health to a transitions of care coordinator did not reduce readmissions at 30 days or improve health status among patients with heart failure after hospital discharge. However, specific groups of patients experienced greater improvement in health status at 30 days and differences in readmissions warranting further research.
Funding/Support:
The MIGHTy-Heart study and this publication were made possible by Contract No. HS-2019C2–17373 from the Patient-Centered Outcomes Research Institute. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Patient-Centered Outcomes Research Institute. REDCap is supported by grant number UL1TR 002384 from the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH). In addition, Masterson Creber was also funded by R01HL161458.
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.
Footnotes
Conflict of Interest Disclosures: Dr. Masterson Creber reported receiving grants from the National Institutes of Health (NIH) during the conduct of the study and PCORI. Dr. Daniels reported receiving grants from NIH, PCORI, NewYork Presbyterian Digital Health Outcomes Research Institute and the Emergency Medicine Foundation during the conduct of the study. Mr. Sholle reports receiving funding from Leap of Faith Inc. Dr. Reading Turchioe reported receiving grants from NIH. Dr. Spertus discloses providing consultative services on patient-reported outcomes and evidence evaluation to Alnylam, AstraZeneca, Bayer, Janssen, Bristol Meyers Squibb, Terumo, Cytokinetics, BridgeBio, VentricHealth, and Imbria. He holds research grants from the NIH, PCORI, the American College of Cardiology Foundation, Lexicon, Imbria, and Janssen. He owns the copyright to the Seattle Angina Questionnaire, KCCQ, and Peripheral Artery Questionnaire and serves on the Board of Directors for Blue Cross Blue Shield of Kansas City. None of the other co-authors have any conflicts of interest to disclose.
Contributor Information
Ruth Masterson Creber, Columbia University School of Nursing, New York, NY, USA.
Brock Daniels, Departments of Emergency Medicine and Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
Meghan Reading Turchioe, Columbia University School of Nursing, New York, NY, USA.
Leah Shafran Topaz, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
Yihong Zhao, Columbia University School of Nursing, New York, NY, USA.
Jacky Choi, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
Melani Ellison, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
Roland C. Merchant, Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Erik Blutinger, Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Parag Goyal, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
Jiani Yu, Weill Cornell Medicine, New York, NY, USA.
Mark G. Weiner, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
Evan Sholle, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
Kumudha Ramasubbu, Division of Cardiology, New York Presbyterian Brooklyn Methodist Hospital, Brooklyn, NY, USA.
Shudhanshu Alishetti, Division of Cardiology, New York Presbyterian Brooklyn Methodist Hospital, Brooklyn, NY, USA.
Kelly Axsom, Department of Medicine, Division of Cardiology, Center for Advanced Cardiac Care, Columbia University Medical Center, New York, NY, USA.
David Slotwiner, Division of Cardiology, New York Presbyterian Queens, NY, USA.
Maya Rao, Division of Nephrology, Columbia University Medical Center, New York, NY, USA.
Ivan Diaz, Department of Population Health at NYU Grossman School of Medicine, New York, NY, USA.
John A. Spertus, University of Missouri-Kansas City’s Healthcare Institute for Innovations in Quality and Saint Luke’s Mid America Heart Institute, Kansas City, MO, USA.
Rahul Sharma, Department of Emergency Medicine, Weill Cornell Medicine/New York-Presbyterian Hospital, New York, NY, USA.
Rainu Kaushal, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
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