Predicting Potential Adverse Events During a Skilled Nursing Facility Stay: A Skilled Nursing Facility Prognosis Score.

Burke, Robert E; Hess, Edward; Barón, Anna E; Levy, Cari; Donzé, Jacques (2018). Predicting Potential Adverse Events During a Skilled Nursing Facility Stay: A Skilled Nursing Facility Prognosis Score. Journal of the American Geriatrics Society, 66(5), pp. 930-936. Wiley-Blackwell 10.1111/jgs.15324

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OBJECTIVES

To derive a risk prediction score for potential adverse outcomes in older adults transitioning to a skilled nursing facility (SNF) from the hospital.

DESIGN

Retrospective analysis.

SETTING

Medicare Current Beneficiary Survey (2003-11).

PARTICIPANTS

Previously community-dwelling Medicare beneficiaries who were hospitalized and discharged to SNF for postacute care (N=2,043).

MEASUREMENTS

Risk factors included demographic characteristics, comorbidities, health status, hospital length of stay, prior SNF stays, SNF size and ownership, treatments received, physical function, and active signs or symptoms at time of SNF admission. The primary outcome was a composite of undesirable outcomes from the patient perspective, including hospital readmission during the SNF stay, long SNF stay (≥100 days), and death during the SNF stay.

RESULTS

Of the 2,043 previously community-dwelling beneficiaries hospitalized and discharged to a SNF for post-acute care, 589 (28.8%) experienced one of the three outcomes, with readmission (19.4%) most common, followed by mortality (10.5%) and long SNF stay (3.5%). A risk score including 5 factors (Barthel Index, Charlson-Deyo comorbidity score, hospital length of stay, heart failure diagnosis, presence of an indwelling catheter) demonstrated very good discrimination (C-statistic=0.75), accuracy (Brier score=0.17), and calibration for observed and expected events.

CONCLUSION

Older adults frequently experience potentially adverse outcomes in transitions to a SNF from the hospital; this novel score could be used to better match resources with patient risk.

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5 Citations in Web of Science ®
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