Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data

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.

Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review

“…Overall, 56 of the 60 included studies reported model discriminative ability (C-statistic), ranging from 0.21 46 to 0.88. 63 The area under curve for validation studies ranged from 0.53 30 to 0.83, 63 being slightly lower than those for the derivation study, 0.21 46 to 0.88. 63 For all-cause unplanned hospital readmission models, the C-statistic was reported by 14 studies ranging from 0.55 13 to 0.80.…”

“…Of those, only nine developed models had a C-statistic value >0.70. 30 32 34 35 38 40 41 49 50 In particular, 13 of the 17 models (12 developed and 5 existing) from 11 studies with the special focus on heart failure-related readmissions were presented with C-statistic <0.70. 39 40 42–48 For surgical-related readmissions (6 studies), the C-statistic ranged from 0.59 67 to 0.85 69 among 7 developed models.…”

Acute Myocardial Infarction Readmission Risk Prediction Models

“…The median overall observed all-cause 30-day readmission rate across studies was 16.3% (range 10.6–21.0%). The objective of most studies (n=7) was to develop models to identify patients hospitalized for myocardial infarction at high risk for readmission for potential intervention, 19–22, 24, 27, 29 while the objective of three studies of the CMS AMI administrative model was to estimate hospital-level risk-adjusted 30-day readmission rates for hospital profiling. 23, 25, 26 One study focused on identifying patient- versus hospital-level predictors for cardiac disease-related readmission.…”

“…23, 25, 26 One study focused on identifying patient- versus hospital-level predictors for cardiac disease-related readmission. 28 All studies were conducted in the U.S. except for Rana et al, 27, 28 which was conducted at a single community medical center in Australia, and Rodriguez-Padial et al, conducted in Spain using administrative data from the Spanish National Health System.…”

Predicting mortality over different time horizons: which data elements are needed?

“…These results correspond with previous studies using EHR data to develop risk models, illustrating that EHR-based models perform better with nearer-term events. [14][15][16][17][18][19][20][21] Moreover, when comparing the "important" variables over different time horizons, previous work has similarly suggested that more "dynamic" metrics are important for nearer-term outcomes and more "stable" metrics are important for longer-term events. 17 This finding stresses the importance of machine-learning methods capable of handling large numbers of disparate predictor variables.…”