Assessment of Patient Ambulation Profiles to Predict Hospital Readmission, Discharge Location, and Length of Stay in a Cardiac Surgery Progressive Care Unit | Zendy

In Cheol Jeong | Zendy; Ryan Healy | Zendy; Benjamin Bao | Zendy; William Xie | Zendy; Tim Madeira | Zendy; Marc Sussman | Zendy; Glenn Whitman | Zendy; Jennifer A. Schrack | Zendy; Nicole Zahradka | Zendy; Erik H. Hoyer | Zendy; Charles H. Brown | Zendy; Peter C. Searson | Zendy

Open Access

Assessment of Patient Ambulation Profiles to Predict Hospital Readmission, Discharge Location, and Length of Stay in a Cardiac Surgery Progressive Care Unit

Author(s) -

In Cheol Jeong,

Ryan Healy,

Benjamin Bao,

William Xie,

Tim Madeira,

Marc Sussman,

Glenn Whitman,

Jennifer A. Schrack,

Nicole Zahradka,

Erik H. Hoyer,

Charles H. Brown,

Peter C. Searson

Publication year - 2020

Publication title -

jama network open

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.278

H-Index - 39

ISSN - 2574-3805

DOI - 10.1001/jamanetworkopen.2020.1074

Subject(s) - medicine , emergency medicine , psychological intervention , cohort , hospital discharge , cardiac surgery , physical therapy , cardiology , psychiatry

Key Points Question Are patient ambulation profiles predictive of hospital readmission, discharge location, and length of stay? Findings In this prognostic cohort study of 100 adults in a cardiac surgery progressive care unit, patient ambulation profiles were predictive of 30-day readmission (C statistic, 0.925), discharge location (C statistic, 0.930), and length of stay (correlation coefficient, 0.927). Meaning Patient ambulation profiles from a real-time location system enable prediction of clinically relevant outcomes.

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