We present a hierarchical logistic regression model for 30-day readmission after AMI hospitalization that is based on administrative data and is suitable for public reporting. The model is a strong surrogate for a similar model based on medical record data. The approach uses a group of >15 000 ICD-9-CM codes that are in the public domain and yield clinically coherent variables. The model does not adjust for variables that may represent complications rather than comorbidities. There is a standardized period of follow-up, and the study sample is appropriately defined. The statistical approach takes into account the clustering of patients within hospitals and differences in sample size across hospitals.
AMI was chosen because it is among the most common principal hospital discharge diagnoses given for Medicare beneficiaries; in 2005, it was the fourth most expensive condition billed to Medicare.14
Readmission rates following discharge for AMI are high; for example, rates of all-cause readmission at 30 days have been found to range from 11.3%15
Readmission rates are influenced by the quality of inpatient and outpatient care, the availability and use of effective disease management programs, and the bed capacity of the local healthcare system. Some of the variation in readmissions may be attributable to delivery system characteristics.17
Additionally, interventions during and after a hospitalization can be effective in reducing readmission rates in geriatric populations generally18-20
and for patients with AMI specifically. 21-24
Moreover, such interventions can be cost saving. 19-21
Tracking readmissions also emphasizes improvement in care transitions and care coordination. Although discharge planning is required by Medicare as a condition of hospital participation, transitional care focuses more broadly on “hand-offs” of care from one setting to another and may have implications for quality and costs.25
The patient-level discrimination and the explained variation of the model are consistent with those observed in the development of the heart failure 30-day all-cause readmission measure, which was recently approved by the National Quality Forum.26
The model performs as expected given that the risk of readmission much more likely depends on the quality of care and system characteristics than on the patient severity and comorbidity characteristics. The readiness for discharge, the proper medications, and the proper transition to the outpatient setting may be even more important for readmission than for mortality. Results of intervention studies underscore this potential.21-24
Our approach to risk adjustment is tailored to and appropriate for a publicly reported outcome measure. Adjusting for patient characteristics improved model performance. The receiver operating characteristic of 0.63 is higher than that of a model with just age and sex (0.54) and similar to that of a model with all candidate variables. However, we excluded covariates for which we would not want to adjust in a quality measure, such as potential complications, certain patient demographics (eg, race, socioeconomic status), and patient admission path and discharge disposition (eg, admitted from or discharged to a skilled nursing facility). These characteristics may be associated with readmission and, thus, could increase the model performance to predict patient readmissions. However, these variables may be related to quality or supply factors that should not be included in an adjustment that seeks to control for patient clinical characteristics while illuminating important quality differences. For example, if hospitals with a higher share of a certain ethnic group have higher readmission rates, then including ethnic group in the model will attenuate this difference and obscure differences that are important to identify. Although a high C statistic usually is desirable, an extremely high C statistic for a model including patient characteristics would imply that the outcome is largely determined by patients and that physicians and hospitals do not matter. We believe that much of the unexplained variation derives from the latent variable of quality. Hospitals and medical communities have had no incentive to focus on improving the transition from inpatient to outpatient status. Another explanation is the design of the model in which we adjust for factors that are present or known at admission but do not include any factors that describe events in the hospital as they could be related to quality. As a result, the predictors and the start of follow-up are separated in time.
The model has some notable features. We use all-cause readmission because from the patient perspective, readmission for any cause is a key concern. Second, limiting the measure to AMI-related readmissions may make it susceptible to gaming. We recognize that it would be preferable to report preventable readmissions, but that approach is not advisable given that there are no codes that would definitively identify a readmission as preventable. To determine whether a readmission were preventable might require a root-cause analysis with a thorough evaluation of medical records and interviews with clinicians. In addition, if certain codes were considered to signal a nonpreventable readmission, there would be an incentive to preferentially use these codes and perhaps influence the measurement of quality. Consequently, the decision to use all-cause readmission was considered the best alternative.
Another notable feature is our attempt to exclude staged revascularization procedures, which represent planned admissions for an interventional procedure after an initial procedure during the index hospitalization. There is debate about the indications for these hospitalizations, but we opted to exclude them because some experts consider them an extension of the index admission. The measure does not currently exclude readmissions with other procedures that could have been planned, such as pacemaker insertion. Future iterations of the measure may expand the number of procedures considered as potentially planned.
The agreement between the estimates from the claims model and those from the medical record model suggests that despite the known limitations of administrative codes, the proposed model can stand in place of a model with more detailed clinical information for hospital-level profiling. The AUC and the explained variation of the model are modest, but the purpose is to profile hospital performance based on patient status on admission, not to predict outcomes for individual patients. Important considerations for profiling relate to the degree of between-hospital variation and the amount of information each hospital provides. After adjusting for patient presenting factors, a patient has a 35% higher risk of being readmitted within 30 days of discharge when discharged alive from a hospital 1 SD below national quality relative to discharge from a hospital of higher quality. The differences in risk-standardized rates among hospitals are substantial.
The approach has several limitations. The approach to calculating risk-standardized readmission rates is only validated with Medicare data. However, about 60% of the patients hospitalized with an AMI are aged ≥65 years. Additionally, we were unable to test the model with a Medicare managed care population, for which data are not currently available. In addition, we do not use time-to-event analysis because of the difficulty in fitting such a model to a large national data set. Fortunately, there is no strong relationship between mortality and readmission. Finally, our approach focuses on 30-day readmission and not death. If a patient died within 30 days postdischarge without a readmission, we coded the outcome as no readmission, recognizing that this has the effect of counting such a death as a no-event readmission outcome.
In conclusion, this article presents a model to produce hospital-specific risk-standardized estimates of 30-day readmission rates after discharge for an AMI. This model is being used to publicly report the variation in readmission rates among hospitals across the United States.