Advertisement

Using Data Science to Predict Readmissions in Heart Failure

  • Donald U. Apakama
  • Benjamin H. SlovisEmail author
Heart Failure (AM Chang, Section Editor)
  • 17 Downloads
Part of the following topical collections:
  1. Heart Failure

Abstract

Purpose of Review

This review describes the current literature on the use of data science to predict readmissions of patients with heart failure. We examine the chronology of heart failure management from the emergency department, inpatient unit, transition of care, and home care. We examine the software and hardware which may improve readmission rates of this common and complex disease process.

Recent Findings

There are multiple novel applications of data science which have been used to predict readmissions of heart failure patients. In the emergency department, efforts are focused on identifying patients who can be safely discharged after a brief period of stabilization; while inpatient endeavors have attempted to predict those patients at risk for decline after discharge. Overall, prediction rules have had mixed results. Outpatient telemonitoring with invasive devices seems to hold promise. New technologies may be the key to future improvements in readmission rates.

Summary

Heart failure holds a high morbidity and mortality, and hospitalizations are common. A number of technological interventions have been developed to prevent readmissions in this complex population. Improvements in technology may lead to reductions in heart failure admissions, reduced mortality, and improved quality of care.

Keywords

Heart failure Data science Informatics Readmissions Telemonitoring Decision support 

Notes

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflicts of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.
    Ambrosy AP, Fonarow GC, Butler J, Chioncel O, Greene SJ, Vaduganathan M, et al. The global health and economic burden of hospitalizations for heart failure: lessons learned from hospitalized heart failure registries. J Am Coll Cardiol. 2014;63(12):1123–33.  https://doi.org/10.1016/j.jacc.2013.11.053.CrossRefGoogle Scholar
  2. 2.
    Heart failure fact sheet. In: Prevention CfDCa, editor. cdc.gov. Accessed 10 April 2019
  3. 3.
    Inamdar AA, Inamdar AC. Heart failure: diagnosis, management and utilization. J Clin Med. 2016;5(7):E62.  https://doi.org/10.3390/jcm5070062.CrossRefGoogle Scholar
  4. 4.
    Havranek EP, Masoudi FA, Smith GL, Wolfe P, Ralston DL, Krumholz HM, et al. Lessons learned from the national heart failure project: a center for medicare and medicaid services initiative to improve the care of medicare beneficiaries with heart failure. Congest Heart Fail. 2001;7(6):334–6.CrossRefGoogle Scholar
  5. 5.
    Goldfarb M, Bibas L, Newby LK, Henry TD, Katz J, van Diepen S, et al. Systematic review and directors survey of quality indicators for the cardiovascular intensive care unit. Int J Cardiol. 2018;260:219–25.  https://doi.org/10.1016/j.ijcard.2018.02.113.CrossRefGoogle Scholar
  6. 6.
    Glance LG, Li Y, Dick AW. Impact on hospital ranking of basing readmission measures on a composite endpoint of death or readmission versus readmissions alone. BMC Health Serv Res. 2017;17(1):327.  https://doi.org/10.1186/s12913-017-2266-4.CrossRefGoogle Scholar
  7. 7.
    Fischer C, Steyerberg EW, Fonarow GC, Ganiats TG, Lingsma HF. A systematic review and meta-analysis on the association between quality of hospital care and readmission rates in patients with heart failure. Am Heart J. 2015;170(5):1005–17.e2.  https://doi.org/10.1016/j.ahj.2015.06.026.CrossRefGoogle Scholar
  8. 8.
    Hospital Readmissions Reduction Program (HRRP). cms.gov2019. Accessed 5 June 2019
  9. 9.
    Weintraub NL, Collins SP, Pang PS, Levy PD, Anderson AS, Arslanian-Engoren C, et al. Acute heart failure syndromes: emergency department presentation, treatment, and disposition: current approaches and future aims. Circulation. 2010;122(19):1975–96.  https://doi.org/10.1161/CIR.0b013e3181f9a223.CrossRefGoogle Scholar
  10. 10.
    Collins SP, Lindsell CJ, Naftilan AJ, Peacock WF, Diercks D, Hiestand B, et al. Low-risk acute heart failure patients: external validation of the Society of Chest Pain Center’s recommendations. Critical Pathways Cardiol. 2009;8(3):99–103.  https://doi.org/10.1097/HPC.0b013e3181b5a534.CrossRefGoogle Scholar
  11. 11.
    Collins SP, Pang PS, Fonarow GC, Yancy CW, Bonow RO, Gheorghiade M. Is hospital admission for heart failure really necessary?: the role of the emergency department and observation unit in preventing hospitalization and rehospitalization. J Am Coll Cardiol. 2013;61(2):121–6.  https://doi.org/10.1016/j.jacc.2012.08.1022.CrossRefGoogle Scholar
  12. 12.
    Smith WR, Poses RM, McClish DK, Huber EC, Clemo FLW, Alexander D, et al. Prognostic judgments and triage decisions for patients with acute congestive heart failure. CHEST. 2002;121(5):1610–7.  https://doi.org/10.1378/chest.121.5.1610.CrossRefGoogle Scholar
  13. 13.
    Hsieh M, Auble TE, Yealy DM. Validation of the acute heart failure index. Ann Emerg Med. 2008;51(1):37–44.  https://doi.org/10.1016/j.annemergmed.2007.07.026.CrossRefGoogle Scholar
  14. 14.
    Collins SP, Jenkins CA, Harrell FE Jr, Liu D, Miller KF, Lindsell CJ, et al. Identification of emergency department patients with acute heart failure at low risk for 30-day adverse events: the STRATIFY decision tool. JACC Heart failure. 2015;3(10):737–47.  https://doi.org/10.1016/j.jchf.2015.05.007.CrossRefGoogle Scholar
  15. 15.
    Stiell IG, Perry JJ, Clement CM, Brison RJ, Rowe BH, Aaron SD, et al. Prospective and explicit clinical validation of the Ottawa heart failure risk scale, with and without use of quantitative NT-proBNP. Acad Emerg Med Off J Soc Acad Emerg Med. 2017;24(3):316–27.  https://doi.org/10.1111/acem.13141.CrossRefGoogle Scholar
  16. 16.
    Miro O, Rossello X, Gil V, Martin-Sanchez FJ, Llorens P, Herrero-Puente P, et al. Predicting 30-day mortality for patients with acute heart failure in the emergency department: a cohort study. Ann Intern Med. 2017;167(10):698–705.  https://doi.org/10.7326/M16-2726.CrossRefGoogle Scholar
  17. 17.
    Lee DS, Lee JS, Schull MJ, Grimshaw JM, Austin PC, Tu JV. Design and rationale for the acute congestive heart failure urgent care evaluation: the ACUTE study. Am Heart J. 2016;181:60–5.  https://doi.org/10.1016/j.ahj.2016.07.016.CrossRefGoogle Scholar
  18. 18.
    •• Lee DS, Lee JS, Schull MJ, Borgundvaag B, Edmonds ML, Ivankovic M, et al. Prospective validation of the emergency heart failure mortality risk grade for acute heart failure. Circulation. 2019;139(9):1146–56.  https://doi.org/10.1161/circulationaha.118.035509 Validation of the EHMRG7 and EHMRG30-ST models that were able to identify heart failure patients at low risk of mortality. CrossRefGoogle Scholar
  19. 19.
    Collins SP, Pang PS. ACUTE heart failure risk stratification. Circulation. 2019;139(9):1157–61.  https://doi.org/10.1161/circulationaha.118.038472.CrossRefGoogle Scholar
  20. 20.
    Saxena K, Lung BR, Becker JR. Improving patient safety by modifying provider ordering behavior using alerts (CDSS) in CPOE system. AMIA Ann Symp Proc AMIA Symp. 2011;2011:1207–16.Google Scholar
  21. 21.
    Krive J, Shoolin JS, Zink SD. Effectiveness of evidence-based congestive heart failure (CHF) CPOE order sets measured by health outcomes. AMIA Annual Symposium proceedings AMIA Symposium. 2014;2014:815–24.Google Scholar
  22. 22.
    Soundarraj D, Singh V, Satija V, Thakur RK. Containing the cost of heart failure management: a focus on reducing readmissions. Heart Fail Clin. 2017;13(1):21–8.  https://doi.org/10.1016/j.hfc.2016.07.002.CrossRefGoogle Scholar
  23. 23.
    Polanczyk CA, Ruschel KB, Castilho FM, Ribeiro AL. Quality measures in heart failure: the past, the present, and the future. Curr Heart Fail Rep. 2019;16:1–6.  https://doi.org/10.1007/s11897-019-0417-0.CrossRefGoogle Scholar
  24. 24.
    Ross JS, Mulvey GK, Stauffer B, Patlolla V, Bernheim SM, Keenan PS, et al. Statistical models and patient predictors of readmission for heart failure: a systematic review. Arch Intern Med. 2008;168(13):1371–86.  https://doi.org/10.1001/archinte.168.13.1371.CrossRefGoogle Scholar
  25. 25.
    Keenan PS, Normand SL, Lin Z, Drye EE, Bhat KR, Ross JS, et al. An administrative claims measure suitable for profiling hospital performance on the basis of 30-day all-cause readmission rates among patients with heart failure. Circ Cardiovasc Qual Outcomes. 2008;1(1):29–37.  https://doi.org/10.1161/circoutcomes.108.802686.CrossRefGoogle Scholar
  26. 26.
    Eapen ZJ, Liang L, Fonarow GC, Heidenreich PA, Curtis LH, Peterson ED, et al. Validated, electronic health record deployable prediction models for assessing patient risk of 30-day rehospitalization and mortality in older heart failure patients. JACC Heart Failure. 2013;1(3):245–51.  https://doi.org/10.1016/j.jchf.2013.01.008.CrossRefGoogle Scholar
  27. 27.
    Sudhakar S, Zhang W, Kuo YF, Alghrouz M, Barbajelata A, Sharma G. Validation of the readmission risk score in heart failure patients at a tertiary hospital. J Card Fail. 2015;21(11):885–91.  https://doi.org/10.1016/j.cardfail.2015.07.010.CrossRefGoogle Scholar
  28. 28.
    Frizzell JD, Liang L, Schulte PJ, Yancy CW, Heidenreich PA, Hernandez AF, et al. Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: comparison of machine learning and other statistical approachesprediction of 30-day readmission in patients with heart failureprediction of 30-day readmission in patients with heart failure. JAMA Cardiol. 2017;2(2):204–9.  https://doi.org/10.1001/jamacardio.2016.3956.CrossRefGoogle Scholar
  29. 29.
    Amarasingham R, Moore BJ, Tabak YP, Drazner MH, Clark CA, Zhang S, et al. An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data. Med Care. 2010;48(11):981–8.  https://doi.org/10.1097/MLR.0b013e3181ef60d9.CrossRefGoogle Scholar
  30. 30.
    • Huynh Q, Negishi K, De Pasquale CG, Hare JL, Leung D, Stanton T, et al. Validation of predictive score of 30-day hospital readmission or death in patients with heart failure. Am J Cardiol. 2018;121(3):322–9.  https://doi.org/10.1016/j.amjcard.2017.10.031 The authors demonstrated that incorporation of socioecomonimc, cognitive, and mental health information can impact prediction of 30-day readmissions for heart failure. CrossRefGoogle Scholar
  31. 31.
    Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE Jr, Drazner MH, et al. 2013 ACCF/AHA guideline for the management of heart failure: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines. Circulation. 2013;128(16):1810–52.  https://doi.org/10.1161/CIR.0b013e31829e8807.CrossRefGoogle Scholar
  32. 32.
    Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE Jr, Drazner MH, et al. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines. J Am Coll Cardiol. 2013;62(16):e147–239.  https://doi.org/10.1016/j.jacc.2013.05.019.CrossRefGoogle Scholar
  33. 33.
    Feltner C, Jones CD, Cene CW, Zheng ZJ, Sueta CA, Coker-Schwimmer EJ, et al. Transitional care interventions to prevent readmissions for persons with heart failure: a systematic review and meta-analysis. Ann Intern Med. 2014;160(11):774–84.  https://doi.org/10.7326/M14-0083.CrossRefGoogle Scholar
  34. 34.
    Ponikowski P, Voors AA, Anker SD, Bueno H, Cleland JGF, Coats AJS, et al. 2016 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure: the task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur Heart J. 2016;37(27):2129–200.  https://doi.org/10.1093/eurheartj/ehw128.CrossRefGoogle Scholar
  35. 35.
    Andrès E, Talha S, Zulfiqar A-A, Hajjam M, Ervé S, Hajjam J, et al. Current research and new perspectives of telemedicine in chronic heart failure: narrative review and points of interest for the clinician. J Clin Med. 2018;7(12):544.CrossRefGoogle Scholar
  36. 36.
    Inglis SC, Clark RA, McAlister FA, Ball J, Lewinter C, Cullington D, et al. Structured telephone support or telemonitoring programmes for patients with chronic heart failure. Cochrane Database Syst Rev. 2010;2010(8):Cd007228.  https://doi.org/10.1002/14651858.CD007228.pub2.Google Scholar
  37. 37.
    Anker SD, Koehler F, Abraham WT. Telemedicine and remote management of patients with heart failure. Lancet. 2011;378(9792):731–9.  https://doi.org/10.1016/S0140-6736(11)61229-4.CrossRefGoogle Scholar
  38. 38.
    Cleland JG, Louis AA, Rigby AS, Janssens U, Balk AH, Investigators T-H. Noninvasive home telemonitoring for patients with heart failure at high risk of recurrent admission and death: the trans-European Network-Home-Care Management System (TEN-HMS) study. J Am Coll Cardiol. 2005;45(10):1654–64.  https://doi.org/10.1016/j.jacc.2005.01.050.CrossRefGoogle Scholar
  39. 39.
    Chaudhry SI, Mattera JA, Curtis JP, Spertus JA, Herrin J, Lin Z, et al. Telemonitoring in patients with heart failure. N Engl J Med. 2010;363(24):2301–9.  https://doi.org/10.1056/NEJMoa1010029.CrossRefGoogle Scholar
  40. 40.
    Ong MK, Romano PS, Edgington S, Aronow HU, Auerbach AD, Black JT, et al. 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. JAMA Intern Med. 2016;176(3):310–8.  https://doi.org/10.1001/jamainternmed.2015.7712.CrossRefGoogle Scholar
  41. 41.
    Koehler F, Winkler S, Schieber M, Sechtem U, Stangl K, Bohm M, et al. Impact of remote telemedical management on mortality and hospitalizations in ambulatory patients with chronic heart failure: the telemedical interventional monitoring in heart failure study. Circulation. 2011;123(17):1873–80.  https://doi.org/10.1161/CIRCULATIONAHA.111.018473.CrossRefGoogle Scholar
  42. 42.
    Burdese E, Testa M, Raucci P, Ferreri C, Giovannini G, Lombardo E, et al. Usefulness of a telemedicine program in refractory older congestive heart failure patients. Diseases. 2018;6(1):E10.  https://doi.org/10.3390/diseases6010010.CrossRefGoogle Scholar
  43. 43.
    Andres E, Talha S, Hajjam M, Hajjam J, Erve S, Hajjam A. Experimentation of 2.0 telemedicine in elderly patients with chronic heart failure: a study prospective in 175 patients. Eur J Intern Med. 2018;51:e11–e2.  https://doi.org/10.1016/j.ejim.2018.02.022.CrossRefGoogle Scholar
  44. 44.
    Suh MK, Chen CA, Woodbridge J, Tu MK, Kim JI, Nahapetian A, et al. A remote patient monitoring system for congestive heart failure. J Med Syst. 2011;35(5):1165–79.  https://doi.org/10.1007/s10916-011-9733-y.CrossRefGoogle Scholar
  45. 45.
    Suh M-k, Evangelista LS, Chen C-A, Han K, Kang J, Tu MK et al. An automated vital sign monitoring system for congestive heart failure patients. Proceedings of the 1st ACM International Health Informatics Symposium; Arlington, Virginia, USA. 1883010: ACM; 2010. p. 108–17.Google Scholar
  46. 46.
    Chen XW, Arabnia HR. Guest editorial data mining in bioinformatics and biomedicine. IEEE Trans Inf Technol Biomed. 2010;14(1):1–2.  https://doi.org/10.1109/TITB.2009.2039678.CrossRefGoogle Scholar
  47. 47.
    •• Koehler F, Koehler K, Deckwart O, Prescher S, Wegscheider K, Kirwan BA, et al. Efficacy of telemedical interventional management in patients with heart failure (TIM-HF2): a randomised, controlled, parallel-group, unmasked trial. Lancet. 2018;392(10152):1047–57.  https://doi.org/10.1016/S0140-6736(18)31880-4 The authors demonstrated that remote patient monitoring reduced days lost due to unplanned hospital admissions for cardiovascular disease and all-cause mortality when compared with usual care. CrossRefGoogle Scholar
  48. 48.
    Eurlings C, Boyne JJ, de Boer RA, Brunner-La Rocca HP. Telemedicine in heart failure-more than nice to have? Neth Hear J. 2019;27(1):5–15.  https://doi.org/10.1007/s12471-018-1202-5.CrossRefGoogle Scholar
  49. 49.
    Landolina M, Perego GB, Lunati M, Curnis A, Guenzati G, Vicentini A, et al. Remote monitoring reduces healthcare use and improves quality of care in heart failure patients with implantable defibrillators: the evolution of management strategies of heart failure patients with implantable defibrillators (EVOLVO) study. Circulation. 2012;125(24):2985–92.  https://doi.org/10.1161/CIRCULATIONAHA.111.088971.CrossRefGoogle Scholar
  50. 50.
    Hindricks G, Taborsky M, Glikson M, Heinrich U, Schumacher B, Katz A, et al. Implant-based multiparameter telemonitoring of patients with heart failure (IN-TIME): a randomised controlled trial. Lancet. 2014;384(9943):583–90.  https://doi.org/10.1016/S0140-6736(14)61176-4.CrossRefGoogle Scholar
  51. 51.
    De Simone A, Leoni L, Luzi M, Amellone C, Stabile G, La Rocca V, et al. Remote monitoring improves outcome after ICD implantation: the clinical efficacy in the management of heart failure (EFFECT) study. Europace. 2015;17(8):1267–75.  https://doi.org/10.1093/europace/euu318.CrossRefGoogle Scholar
  52. 52.
    Kurek A, Tajstra M, Gadula-Gacek E, Buchta P, Skrzypek M, Pyka L, et al. Impact of remote monitoring on long-term prognosis in heart failure patients in a real-world cohort: results from all-comers COMMIT-HF trial. J Cardiovasc Electrophysiol. 2017;28(4):425–31.  https://doi.org/10.1111/jce.13174.CrossRefGoogle Scholar
  53. 53.
    Bohm M, Drexler H, Oswald H, Rybak K, Bosch R, Butter C, et al. Fluid status telemedicine alerts for heart failure: a randomized controlled trial. Eur Heart J. 2016;37(41):3154–63.  https://doi.org/10.1093/eurheartj/ehw099.CrossRefGoogle Scholar
  54. 54.
    Parthiban N, Esterman A, Mahajan R, Twomey DJ, Pathak RK, Lau DH, et al. Remote monitoring of implantable cardioverter-defibrillators: a systematic review and meta-analysis of clinical outcomes. J Am Coll Cardiol. 2015;65(24):2591–600.  https://doi.org/10.1016/j.jacc.2015.04.029.CrossRefGoogle Scholar
  55. 55.
    Klersy C, Boriani G, De Silvestri A, Mairesse GH, Braunschweig F, Scotti V, et al. Effect of telemonitoring of cardiac implantable electronic devices on healthcare utilization: a meta-analysis of randomized controlled trials in patients with heart failure. Eur J Heart Fail. 2016;18(2):195–204.  https://doi.org/10.1002/ejhf.470.CrossRefGoogle Scholar
  56. 56.
    Ritzema J, Troughton R, Melton I, Crozier I, Doughty R, Krum H, et al. Physician-directed patient self-management of left atrial pressure in advanced chronic heart failure. Circulation. 2010;121(9):1086–95.  https://doi.org/10.1161/CIRCULATIONAHA.108.800490.CrossRefGoogle Scholar
  57. 57.
    Abraham WT, Adamson PB, Bourge RC, Aaron MF, Costanzo MR, Stevenson LW, et al. Wireless pulmonary artery haemodynamic monitoring in chronic heart failure: a randomised controlled trial. Lancet. 2011;377(9766):658–66.  https://doi.org/10.1016/S0140-6736(11)60101-3.CrossRefGoogle Scholar
  58. 58.
    Abraham WT, Stevenson LW, Bourge RC, Lindenfeld JA, Bauman JG, Adamson PB, et al. Sustained efficacy of pulmonary artery pressure to guide adjustment of chronic heart failure therapy: complete follow-up results from the CHAMPION randomised trial. Lancet. 2016;387(10017):453–61.  https://doi.org/10.1016/S0140-6736(15)00723-0.CrossRefGoogle Scholar
  59. 59.
    Adamson PB, Abraham WT, Bourge RC, Costanzo MR, Hasan A, Yadav C, et al. Wireless pulmonary artery pressure monitoring guides management to reduce decompensation in heart failure with preserved ejection fraction. Circ Heart Fail. 2014;7(6):935–44.  https://doi.org/10.1161/CIRCHEARTFAILURE.113.001229.CrossRefGoogle Scholar
  60. 60.
    Bourge RC, Abraham WT, Adamson PB, Aaron MF, Aranda JM Jr, Magalski A, et al. Randomized controlled trial of an implantable continuous hemodynamic monitor in patients with advanced heart failure: the COMPASS-HF study. J Am Coll Cardiol. 2008;51(11):1073–9.  https://doi.org/10.1016/j.jacc.2007.10.061.CrossRefGoogle Scholar
  61. 61.
    Bhavnani SP, Narula J, Sengupta PP. Mobile technology and the digitization of healthcare. Eur Heart J. 2016;37(18):1428–38.  https://doi.org/10.1093/eurheartj/ehv770.CrossRefGoogle Scholar
  62. 62.
    Mortazavi BJ, Downing NS, Bucholz EM, Dharmarajan K, Manhapra A, Li SX, et al. Analysis of machine learning techniques for heart failure readmissions. Circ Cardiovasc Qual Outcomes. 2016;9(6):629–40.  https://doi.org/10.1161/CIRCOUTCOMES.116.003039.CrossRefGoogle Scholar
  63. 63.
    Chow CK, Ariyarathna N, Islam SM, Thiagalingam A, Redfern J. mHealth in cardiovascular health care. Heart Lung Circ. 2016;25(8):802–7.  https://doi.org/10.1016/j.hlc.2016.04.009.CrossRefGoogle Scholar
  64. 64.
    eHealth WGOf. mHealth: new horizons for health through mobile technologies: second global survey on eHealth. 2011.Google Scholar
  65. 65.
    Smith A. Record shares of Americans now own smartphones, have home broadband. Pew Research Center. 2017. https://www.pewresearch.org/fact-tank/2017/01/12/evolution-of-technology/. Accessed 1 June 2019.
  66. 66.
    Talmor G, Nguyen B, Keibel A, Temelkovska T, Saxon L. Use of software applications to improve medication adherence and achieve more integrated disease management in heart failure. Trends Cardiovasc Med. 2018;28(7):483–8.  https://doi.org/10.1016/j.tcm.2018.04.001.CrossRefGoogle Scholar
  67. 67.
    Apple announces effortless solution bringing health records to iPhone. apple.com. 2018. https://www.apple.com/newsroom/2018/01/apple-announces-effortless-solution-bringing-health-records-to-iPhone/. Accessed 6/7/2019.
  68. 68.
    Apple. Empowering medical researchers, doctors, and you., apple.com. https://www.apple.com/researchkit/. Accessed 6/1/2019.
  69. 69.
    Walsh JA 3rd, Topol EJ, Steinhubl SR. Novel wireless devices for cardiac monitoring. Circulation. 2014;130(7):573–81.  https://doi.org/10.1161/CIRCULATIONAHA.114.009024.CrossRefGoogle Scholar
  70. 70.
    Rudner J, McDougall C, Sailam V, Smith M, Sacchetti A. Interrogation of patient smartphone activity tracker to assist arrhythmia management. Ann Emerg Med. 2016;68(3):292–4.  https://doi.org/10.1016/j.annemergmed.2016.02.039.CrossRefGoogle Scholar
  71. 71.
    Apple. Heart rate notifications on your Apple Watch. 2019. https://support.apple.com/en-us/HT208931#afib. Accessed 6/7/2019.
  72. 72.
    Tarakji KG, Wazni OM, Callahan T, Kanj M, Hakim AH, Wolski K, et al. Using a novel wireless system for monitoring patients after the atrial fibrillation ablation procedure: the iTransmit study. Heart Rhythm. 2015;12(3):554–9.  https://doi.org/10.1016/j.hrthm.2014.11.015.CrossRefGoogle Scholar
  73. 73.
    Tan MKH, Wong JKL, Bakrania K, Abdullahi Y, Harling L, Casula R, et al. Can activity monitors predict outcomes in patients with heart failure? A systematic review. Eur Heart J Qual Care Clin Outcomes. 2019;5(1):11–21.  https://doi.org/10.1093/ehjqcco/qcy038.CrossRefGoogle Scholar
  74. 74.
    Lee HA, Lee HJ, Moon JH, Lee T, Kim MG, In H, et al. Comparison of wearable activity tracker with actigraphy for sleep evaluation and circadian rest-activity rhythm measurement in healthy young adults. Psychiatry Investig. 2017;14(2):179–85.  https://doi.org/10.4306/pi.2017.14.2.179.CrossRefGoogle Scholar
  75. 75.
    Tully MA, McBride C, Heron L, Hunter RF. The validation of Fibit zip physical activity monitor as a measure of free-living physical activity. BMC Res Notes. 2014;7:952.  https://doi.org/10.1186/1756-0500-7-952.CrossRefGoogle Scholar
  76. 76.
    Fitzgerald AA, Powers JD, Ho PM, Maddox TM, Peterson PN, Allen LA, et al. Impact of medication nonadherence on hospitalizations and mortality in heart failure. J Card Fail. 2011;17(8):664–9.  https://doi.org/10.1016/j.cardfail.2011.04.011.CrossRefGoogle Scholar
  77. 77.
    Aggarwal B, Pender A, Mosca L, Mochari-Greenberger H. Factors associated with medication adherence among heart failure patients and their caregivers. J Nurs Educ Pract. 2015;5(3):22–7.  https://doi.org/10.5430/jnep.v5n3p22.Google Scholar
  78. 78.
    Checchi KD, Huybrechts KF, Avorn J, Kesselheim AS. Electronic medication packaging devices and medication adherence: a systematic review. JAMA. 2014;312(12):1237–47.  https://doi.org/10.1001/jama.2014.10059.CrossRefGoogle Scholar
  79. 79.
    Murray MD, Young J, Hoke S, Tu W, Weiner M, Morrow D, et al. Pharmacist intervention to improve medication adherence in heart failure: a randomized trial. Ann Intern Med. 2007;146(10):714–25.CrossRefGoogle Scholar
  80. 80.
    Volpp KG, Troxel AB, Mehta SJ, Norton L, Zhu J, Lim R, et al. Effect of electronic reminders, financial incentives, and social support on outcomes after myocardial infarction: the HeartStrong randomized clinical trial. JAMA Intern Med. 2017;177(8):1093–101.  https://doi.org/10.1001/jamainternmed.2017.2449.CrossRefGoogle Scholar
  81. 81.
    Anand IS, Greenberg BH, Fogoros RN, Libbus I, Katra RP, Music I. Design of the Multi-sensor monitoring in congestive heart failure (MUSIC) study: prospective trial to assess the utility of continuous wireless physiologic monitoring in heart failure. J Card Fail. 2011;17(1):11–6.  https://doi.org/10.1016/j.cardfail.2010.08.001.CrossRefGoogle Scholar
  82. 82.
    Anand IS, Tang WH, Greenberg BH, Chakravarthy N, Libbus I, Katra RP, et al. Design and performance of a multisensor heart failure monitoring algorithm: results from the multisensor monitoring in congestive heart failure (MUSIC) study. J Card Fail. 2012;18(4):289–95.  https://doi.org/10.1016/j.cardfail.2012.01.009.CrossRefGoogle Scholar
  83. 83.
    tosense. https://www.tosense.com. Accessed 10 May 2007.
  84. 84.
    medgaget. toSense ™ announces product availability of recently FDA-cleared CoVa™ monitoring system 2. 2018.Google Scholar
  85. 85.
    VitalConnect. Vista Solution. 2019. https://vitalconnect.com/. Accessed 5/10/2019.
  86. 86.
    Josef Stehlik CS, Bozkurt B, Nativi-Nicolau J, Wegerich S, Rose K, Ray R, et al. Continuous wearable monitoring analytics predict heart failure decompensation: the LINK-HF multi-center study. J Am Coll Cardiol. 2018;71(11 Supplement):A646.CrossRefGoogle Scholar
  87. 87.
    Siwicki B VA study of wearables, AI shows promise for heart failure patients. mobihealthnews. 2018.Google Scholar
  88. 88.
    Medical S. REDS™ FOR HOME. http://sensible-medical.com/reds-for-home/. Accessed 1 June 2007
  89. 89.
    Amir O, Azzam ZS, Gaspar T, Faranesh-Abboud S, Andria N, Burkhoff D, et al. Validation of remote dielectric sensing (ReDS) technology for quantification of lung fluid status: comparison to high resolution chest computed tomography in patients with and without acute heart failure. Int J Cardiol. 2016;221:841–6.  https://doi.org/10.1016/j.ijcard.2016.06.323.CrossRefGoogle Scholar
  90. 90.
    Amir O, Ben-Gal T, Weinstein JM, Schliamser J, Burkhoff D, Abbo A, et al. Evaluation of remote dielectric sensing (ReDS) technology-guided therapy for decreasing heart failure re-hospitalizations. Int J Cardiol. 2017;240:279–84.  https://doi.org/10.1016/j.ijcard.2017.02.120.CrossRefGoogle Scholar
  91. 91.
    Desai AS, Stevenson LW. Connecting the circle from home to heart-failure disease management. N Engl J Med. 2010;363(24):2364–7.  https://doi.org/10.1056/NEJMe1011769.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Emergency MedicineThe Icahn School of Medicine at Mount SinaiNew YorkUSA
  2. 2.Department of Emergency MedicineThomas Jefferson UniversityPhiladelphiaUSA

Personalised recommendations