Development of a Risk Score to Predict 90-Day Readmission After Coronary Artery Bypass Graft.
Aftercare
/ statistics & numerical data
Aged
Coronary Artery Bypass
/ adverse effects
Coronary Artery Disease
/ surgery
Female
Follow-Up Studies
Humans
Incidence
Male
Middle Aged
Patient Readmission
/ trends
Postoperative Complications
/ epidemiology
Prognosis
Registries
Retrospective Studies
Risk Assessment
/ methods
Risk Factors
Survival Rate
/ trends
United States
/ epidemiology
Journal
The Annals of thoracic surgery
ISSN: 1552-6259
Titre abrégé: Ann Thorac Surg
Pays: Netherlands
ID NLM: 15030100R
Informations de publication
Date de publication:
02 2021
02 2021
Historique:
received:
09
11
2019
revised:
20
03
2020
accepted:
24
04
2020
pubmed:
26
6
2020
medline:
9
2
2021
entrez:
26
6
2020
Statut:
ppublish
Résumé
Readmission after coronary artery bypass grafting (CABG) is used for quality metrics and may negatively affect hospital reimbursement. Our objective was to develop a risk score system from a national cohort that can predict 90-day readmission risk for CABG patients. Using the National Readmission Database between 2013 and 2014, we identified 104,930 patients discharged after CABG, for a total of 234,483 patients after weighted analysis. Using structured random sampling, patients were divided into a training set (60%) and test data set (40%). In the training data set, we used multivariable analysis to identify risk factors. A point system risk score was developed based on the odds ratios. Variables with odds ratio less than 1.3 were excluded from the final model to reduce noise. Performance was assessed in the test data set using receiver operator characteristics and accuracy. In the United States, overall 90-day readmission rate after CABG was 19% (n = 44,559 of 234,483). Nine demographic and clinical variables were identified as important in the training data set. The final risk score ranged from 0 to 52; the 2 largest risks were associated with length of stay greater than 10 days (score = +10) and Medicaid insurance (score = +7). The final model's C-statistic was 0.67. Using an optimal cutoff of 18 points, the accuracy of the risk score was 77%. Ninety-day readmission after CABG surgery is frequent. A readmission risk score higher than 18 points predicts readmission in 77% of patients. Based on 9 demographic and clinical factors, this risk score can be used to target high-risk patients for additional postdischarge resources to reduce readmission.
Sections du résumé
BACKGROUND
Readmission after coronary artery bypass grafting (CABG) is used for quality metrics and may negatively affect hospital reimbursement. Our objective was to develop a risk score system from a national cohort that can predict 90-day readmission risk for CABG patients.
METHODS
Using the National Readmission Database between 2013 and 2014, we identified 104,930 patients discharged after CABG, for a total of 234,483 patients after weighted analysis. Using structured random sampling, patients were divided into a training set (60%) and test data set (40%). In the training data set, we used multivariable analysis to identify risk factors. A point system risk score was developed based on the odds ratios. Variables with odds ratio less than 1.3 were excluded from the final model to reduce noise. Performance was assessed in the test data set using receiver operator characteristics and accuracy.
RESULTS
In the United States, overall 90-day readmission rate after CABG was 19% (n = 44,559 of 234,483). Nine demographic and clinical variables were identified as important in the training data set. The final risk score ranged from 0 to 52; the 2 largest risks were associated with length of stay greater than 10 days (score = +10) and Medicaid insurance (score = +7). The final model's C-statistic was 0.67. Using an optimal cutoff of 18 points, the accuracy of the risk score was 77%.
CONCLUSIONS
Ninety-day readmission after CABG surgery is frequent. A readmission risk score higher than 18 points predicts readmission in 77% of patients. Based on 9 demographic and clinical factors, this risk score can be used to target high-risk patients for additional postdischarge resources to reduce readmission.
Identifiants
pubmed: 32585200
pii: S0003-4975(20)30964-4
doi: 10.1016/j.athoracsur.2020.04.142
pii:
doi:
Types de publication
Journal Article
Multicenter Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
488-494Informations de copyright
Copyright © 2021 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.