TY - JOUR
T1 - Development and Validation of a Predictive Model for Short- and Medium-Term Hospital Readmission Following Heart Valve Surgery
AU - Pack, Quinn R.
AU - Priya, Aruna
AU - Lagu, Tara
AU - Pekow, Penelope S.
AU - Engelman, Richard
AU - Kent, David M.
AU - Lindenauer, Peter K.
N1 - Funding Information:
This work was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health
Funding Information:
(NIH), Award No. KL2TR001063. Dr Lagu is supported by the National Heart, Lung and Blood Institute of the NIH, under Award No. K01HL114745. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Publisher Copyright:
© 2016 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.
PY - 2016/9
Y1 - 2016/9
N2 - Background: Although models exist for predicting hospital readmission after coronary artery bypass surgery, no such models exist for predicting readmission after heart valve surgery (HVS). Methods and Results: Using a geographically and structurally diverse sample of US hospitals (Premier Inpatient Database, January 2007-June 2011), we examined patient, hospital, and clinical factors predictive of short- and medium-term hospital readmission post-HVS. We set aside 20% of hospitals for model validation. A generalized estimating equation model accounted for clustering within hospitals. At 219 hospitals, we identified 38 532 patients (67 years, 56% male, 62% aortic valve surgery) who underwent HVS. A total of 3125 (7.8%) and 4943 (12.8%) patients were readmitted to the index hospital within 1 and 3 months, respectively. Our 3-month model predicted readmission rates between 3% and 61% with fair discrimination (C-statistic, 0.67) and good calibration (predicted vs observed differences in validation cohort averaged 1.9% across all deciles of predicted readmission risk). Results were similar for our 1-month model and our simplified 3-month model (suitable for clinical use), which used the 5 strongest predictors of readmission: transfused units of packed Red blood cells, presence of End-stage renal disease, type of Valve surgery, Emergency hospital admission, and hospital Length of stay (REVEaL). Conclusions: We described and validated key factors that predict short- and medium-term hospital readmission post-HVS. These models should enable clinicians to identify individuals with HVS who are at increased risk for hospital readmission and are most likely to benefit from improved postdischarge care and follow-up.
AB - Background: Although models exist for predicting hospital readmission after coronary artery bypass surgery, no such models exist for predicting readmission after heart valve surgery (HVS). Methods and Results: Using a geographically and structurally diverse sample of US hospitals (Premier Inpatient Database, January 2007-June 2011), we examined patient, hospital, and clinical factors predictive of short- and medium-term hospital readmission post-HVS. We set aside 20% of hospitals for model validation. A generalized estimating equation model accounted for clustering within hospitals. At 219 hospitals, we identified 38 532 patients (67 years, 56% male, 62% aortic valve surgery) who underwent HVS. A total of 3125 (7.8%) and 4943 (12.8%) patients were readmitted to the index hospital within 1 and 3 months, respectively. Our 3-month model predicted readmission rates between 3% and 61% with fair discrimination (C-statistic, 0.67) and good calibration (predicted vs observed differences in validation cohort averaged 1.9% across all deciles of predicted readmission risk). Results were similar for our 1-month model and our simplified 3-month model (suitable for clinical use), which used the 5 strongest predictors of readmission: transfused units of packed Red blood cells, presence of End-stage renal disease, type of Valve surgery, Emergency hospital admission, and hospital Length of stay (REVEaL). Conclusions: We described and validated key factors that predict short- and medium-term hospital readmission post-HVS. These models should enable clinicians to identify individuals with HVS who are at increased risk for hospital readmission and are most likely to benefit from improved postdischarge care and follow-up.
KW - Aortic valve
KW - Mitral valve
KW - Model
KW - Prediction statistics
KW - Readmission
KW - Surgery
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U2 - 10.1161/JAHA.116.003544
DO - 10.1161/JAHA.116.003544
M3 - Article
C2 - 27581171
AN - SCOPUS:85028296986
SN - 2047-9980
VL - 5
JO - Journal of the American Heart Association
JF - Journal of the American Heart Association
IS - 9
M1 - e003544
ER -