TY - JOUR
T1 - Validation of Predictive Score of 30-Day Hospital Readmission or Death in Patients With Heart Failure
AU - Huynh, Quan
AU - Negishi, Kazuaki
AU - De Pasquale, Carmine G.
AU - Hare, James L.
AU - Leung, Dominic
AU - Stanton, Tony
AU - Marwick, Thomas H.
N1 - Funding Information:
Supported in part by a partnership grant from the National Health and Medical Research Foundation 1058738 (Canberra), Tasmania Medicare Local (Hobart), the Department of Health and Human Services (Hobart), and National Heart Foundation of Australia (Canberra).
Publisher Copyright:
© 2017 Elsevier Inc.
PY - 2018/2/1
Y1 - 2018/2/1
N2 - Existing prediction algorithms for the identification of patients with heart failure (HF) at high risk of readmission or death after hospital discharge are only modestly effective. We sought to validate a recently developed predictive model of 30-day readmission or death in HF using an Australia-wide sample of patients. This study used data from 1,046 patients with HF at teaching hospitals in 5 Australian capital cities to validate a predictive model of 30-day readmission or death in HF. Besides standard clinical and administrative data, we collected data on individual sociodemographic and socioeconomic status, mental health (Patient Health Questionnaire [PHQ]-9 and Generalized Anxiety Disorder [GAD]-7 scale score), cognitive function (Montreal Cognitive Assessment [MoCA] score), and 2-dimensional echocardiograms. The original sample used to develop the predictive model and the validation sample had similar proportions of patients with an adverse event within 30 days (30% vs 29%, p = 0.35) and 90 days (52% vs 49%, p = 0.36). Applying the predicted risk score to the validation sample provided very good discriminatory power (C-statistic = 0.77) in the prediction of 30-day readmission or death. This discrimination was greater for predicting 30-day death (C-statistic = 0.85) than for predicting 30-day readmission (C-statistic = 0.73). There was a small difference in the performance of the predictive model among patients with either a left ventricular ejection fraction of <40% or a left ventricular ejection fraction of ≥40%, but an attenuation in discrimination when used to predict longer-term adverse outcomes. In conclusion, our findings confirm the generalizability of the predictive model that may be a powerful tool for targeting high-risk patients with HF for intensive management.
AB - Existing prediction algorithms for the identification of patients with heart failure (HF) at high risk of readmission or death after hospital discharge are only modestly effective. We sought to validate a recently developed predictive model of 30-day readmission or death in HF using an Australia-wide sample of patients. This study used data from 1,046 patients with HF at teaching hospitals in 5 Australian capital cities to validate a predictive model of 30-day readmission or death in HF. Besides standard clinical and administrative data, we collected data on individual sociodemographic and socioeconomic status, mental health (Patient Health Questionnaire [PHQ]-9 and Generalized Anxiety Disorder [GAD]-7 scale score), cognitive function (Montreal Cognitive Assessment [MoCA] score), and 2-dimensional echocardiograms. The original sample used to develop the predictive model and the validation sample had similar proportions of patients with an adverse event within 30 days (30% vs 29%, p = 0.35) and 90 days (52% vs 49%, p = 0.36). Applying the predicted risk score to the validation sample provided very good discriminatory power (C-statistic = 0.77) in the prediction of 30-day readmission or death. This discrimination was greater for predicting 30-day death (C-statistic = 0.85) than for predicting 30-day readmission (C-statistic = 0.73). There was a small difference in the performance of the predictive model among patients with either a left ventricular ejection fraction of <40% or a left ventricular ejection fraction of ≥40%, but an attenuation in discrimination when used to predict longer-term adverse outcomes. In conclusion, our findings confirm the generalizability of the predictive model that may be a powerful tool for targeting high-risk patients with HF for intensive management.
UR - http://www.scopus.com/inward/record.url?scp=85039074142&partnerID=8YFLogxK
U2 - 10.1016/j.amjcard.2017.10.031
DO - 10.1016/j.amjcard.2017.10.031
M3 - Article
C2 - 29248155
AN - SCOPUS:85039074142
SN - 0002-9149
VL - 121
SP - 322
EP - 329
JO - The American Journal of Cardiology
JF - The American Journal of Cardiology
IS - 3
ER -