Digital Health Intervention in Acute Myocardial Infarction

Author:

Marvel Francoise A.12ORCID,Spaulding Erin M.134ORCID,Lee Matthias A.5ORCID,Yang William E.2ORCID,Demo Ryan5ORCID,Ding Jie126ORCID,Wang Jane1ORCID,Xun Helen12ORCID,Shah Lochan M.12ORCID,Weng Daniel12ORCID,Carter Jocelyn7ORCID,Majmudar Maulik78ORCID,Elgin Eric9,Sheidy Julie9,McLin Renee9ORCID,Flowers Jennifer9,Vilarino Valerie1610ORCID,Lumelsky David N.110ORCID,Bhardwaj Vinayak,Padula William V.3111213ORCID,Shan Rongzi1ORCID,Huynh Pauline P.12ORCID,Wongvibulsin Shannon12ORCID,Leung Curtis14,Allen Jerilyn K.236ORCID,Martin Seth S.12456ORCID

Affiliation:

1. Digital Health Innovation Laboratory, Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., E.M.S., J.D., J.W., H.X., L.M.S., D.W., V.V., D.N.L., R.S., P.P.H., S.W., S.S.M.).

2. Johns Hopkins University School of Medicine, Baltimore, MD (F.A.M., W.E.Y., J.D., J.W., H.X., L.M.S., D.W., P.P.H., S.W., J.K.A., S.S.M.).

3. Johns Hopkins University School of Nursing, Baltimore, MD (E.M.S., W.V.P., J.K.A.).

4. The Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD (E.M.S., S.S.M.).

5. Johns Hopkins University Whiting School of Engineering, Baltimore, MD (M.A.L., R.Y., S.S.M.).

6. Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (J.D., V.B., J.K.A., S.S.M.).

7. Massachusetts General Hospital, Boston (J.C., M.M.).

8. Harvard Medical School, Boston, MA (M.M.).

9. Reading Hospital, West Reading, PA (E.E., J.S., R.M., J.F.).

10. Johns Hopkins University Krieger School of Arts and Sciences, Baltimore, MD (V.V., D.N.L.).

11. Department of Pharmaceutical and Health Economics, School of Pharmacy (W.V.P.)

12. University of Southern California, Los Angeles, CA (W.V.P.).

13. Leonard D. Schaeffer Center for Health Economics and Policy, University of Southern California, Los Angeles, CA (W.V.P.).

14. Johns Hopkins Health System, Baltimore, MD (C.L.).

Abstract

Background: Thirty-day readmissions among patients with acute myocardial infarction (AMI) contribute to the US health care burden of preventable complications and costs. Digital health interventions (DHIs) may improve patient health care self-management and outcomes. We aimed to determine if patients with AMI using a DHI have lower 30-day unplanned all-cause readmissions than a historical control. Methods: This nonrandomized controlled trial with a historical control, conducted at 4 US hospitals from 2015 to 2019, included 1064 patients with AMI (DHI n=200, control n=864). The DHI integrated a smartphone application, smartwatch, and blood pressure monitor to support guideline-directed care during hospitalization and through 30-days post-discharge via (1) medication reminders, (2) vital sign and activity tracking, (3) education, and (4) outpatient care coordination. The Patient Activation Measure assessed patient knowledge, skills, and confidence for health care self-management. All-cause 30-day readmissions were measured through administrative databases. Propensity score–adjusted Cox proportional hazard models estimated hazard ratios of readmission for the DHI group relative to the control group. Results: Following propensity score adjustment, baseline characteristics were well-balanced between the DHI versus control patients (standardized differences <0.07), including a mean age of 59.3 versus 60.1 years, 30% versus 29% Women, 70% versus 70% White, 54% versus 54% with private insurance, 61% versus 60% patients with a non ST-elevation myocardial infarction, and 15% versus 15% with high comorbidity burden. DHI patients were predominantly in the highest levels of patient activation for health care self-management (mean score 71.7±16.6 at 30 days). The DHI group had fewer all-cause 30-day readmissions than the control group (6.5% versus 16.8%, respectively). Adjusting for hospital site and a propensity score inclusive of age, sex, race, AMI type, comorbidities, and 6 additional confounding factors, the DHI group had a 52% lower risk for all-cause 30-day readmissions (hazard ratio, 0.48 [95% CI, 0.26–0.88]). Similar results were obtained in a sensitivity analysis employing propensity matching. Conclusions: Our results suggest that in patients with AMI, the DHI may be associated with high patient activation for health care self-management and lower risk of all-cause unplanned 30-day readmissions. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03760796.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Cardiology and Cardiovascular Medicine

Reference46 articles.

1. Fingar, K, Washington, R. Trends in hospital readmissions for four high-volume conditions, 2009–2013. HCUP Statistical Brief #196. 2015. Agency for Healthcare Research and Quality, Rockville, MD. Accessed January 7, 2021. www.hcup-us.ahrq.gov/reports/statbriefs/sb196-Readmissions-Trends-High-Volume-Conditions.pdf.

2. Yale New Haven Health Services Corporation Center for Outcomes Research and Evaluation. Medicare hospital quality chartbook: variation in 30-day readmission rates across hospitals following hospitalization for acute myocardial infarction. 2015. Accessed 7 May 2021. https://www.cmshospitalchartbook.com/file/1146/download?token=LuX9PSZr.

3. Diagnoses and Timing of 30-Day Readmissions After Hospitalization for Heart Failure, Acute Myocardial Infarction, or Pneumonia

4. Medicare Payment Advisory Commission. Medicare Payment Advisory Commission.;Medicare Payment Advisory Commission;Report to the Congress: Promoting Greater Efficiency in Medicare,2007

5. Health System Characteristics and Rates of Readmission After Acute Myocardial Infarction in the United States

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