Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data
Santu Rana A , Truyen Tran A , Wei Luo A , Dinh Phung A , Richard L. Kennedy B and Svetha Venkatesh A C
+ Author Affiliations
- Author Affiliations
A Centre for Pattern Recognition and Data Analytics, Deakin University, Locked Bag 20000, Geelong, Vic. 3220, Australia. Email: santu.rana@deakin.edu.au; Truyen.tran@deakin.edu.au; wei.luo@deakin.edu.au; dinh.phung@deakin.edu.au
B School of Medicine, Deakin University, Locked Bag 20000, Geelong, Vic. 3220, Australia. Email: lee.kennedy@deakin.edu.au
C Corresponding author. Email: Svetha.venkatesh@deakin.edu.au
Australian Health Review 38(4) 377-382 https://doi.org/10.1071/AH14059
Submitted: 16 December 2013 Accepted: 18 April 2014 Published: 8 July 2014
Journal Compilation © AHHA 2014
Abstract
Objective Readmission rates are high following acute myocardial infarction (AMI), but risk stratification has proved difficult because known risk factors are only weakly predictive. In the present study, we applied hospital data to identify the risk of unplanned admission following AMI hospitalisations.
Methods The study included 1660 consecutive AMI admissions. Predictive models were derived from 1107 randomly selected records and tested on the remaining 553 records. The electronic medical record (EMR) model was compared with a seven-factor predictive score known as the HOSPITAL score and a model derived from Elixhauser comorbidities. All models were evaluated for the ability to identify patients at high risk of 30-day ischaemic heart disease readmission and those at risk of all-cause readmission within 12 months following the initial AMI hospitalisation.
Results The EMR model has higher discrimination than other models in predicting ischaemic heart disease readmissions (area under the curve (AUC) 0.78; 95% confidence interval (CI) 0.71–0.85 for 30-day readmission). The positive predictive value was significantly higher with the EMR model, which identifies cohorts that were up to threefold more likely to be readmitted. Factors associated with readmission included emergency department attendances, cardiac diagnoses and procedures, renal impairment and electrolyte disturbances. The EMR model also performed better than other models (AUC 0.72; 95% CI 0.66–0.78), and with greater positive predictive value, in identifying 12-month risk of all-cause readmission.
Conclusions Routine hospital data can help identify patients at high risk of readmission following AMI. This could lead to decreased readmission rates by identifying patients suitable for targeted clinical interventions.
What is known about the topic? Many clinical and demographic risk factors are known for hospital readmissions following acute myocardial infarction, including multivessel disease, high baseline heart rate, hypertension, diabetes, obesity, chronic obstructive pulmonary disease and psychiatric morbidity. However, combining these risk factors into indices for predicting readmission had limited success. A recent study reported a C-statistic of 0.73 for predicting 30-day readmissions. In a recent American study, a simple seven-factor score was shown to predict hospital readmissions among medical patients.
What does this paper add? This paper presents a way to predict readmissions following myocardial infarction using routinely collected administrative data. The model performed better than the recently described HOSPITAL score and a model derived from Elixhauser comorbidities. Moreover, the model uses only data generally available in most hospitals.
What are the implications for practitioners? Routine hospital data available at discharges can be used to tailor preventative care for AMI patients, to improve institutional performance and to decrease the cost burden associated with AMI.
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