Hospital Readmission in General Medicine Patients: A Prediction Model

Using data from the MCH Study, we were able to identify key patient-level predictors of early hospital readmission and derive and internally validate a parsimonious and easy-to-use model for assessing readmission risk in general medicine patients hospitalized for a variety of medical conditions and discharged home. Using seven easily available predictors, our model was able to identify 5% of patients with an approximately 30% risk of readmission within 30 days of discharge. Although the discriminative ability of our model is only fair, it still provides a useful and easily applicable tool for identifying high-risk patients who may require more intensive use of hospital resources designed to reduce readmission rates.

Several patient-level factors identified as significant predictors were known from the published literature, such as the number of hospital admissions in the preceding year and the Charlson comorbidity index16‐26. It was somewhat surprising that marital status and having a regular physician were both positively associated with readmission risk. It is possible that the presence of social supports, such as a spouse, allows some frail patients to be discharged home who would otherwise be transferred to a subacute care facility (of note, when our model was applied to patients discharged to facilities, being married was a negligible predictor of readmission). Similarly, having a regular physician may be a marker of illness severity not captured by other predictors in our model. Another possibility is that having a spouse or regular physician may lead to earlier detection of clinical deterioration and/or a lower threshold for readmission, although the lack of a significant association between having help at home or living with someone and early readmission appears to argue against this. Nevertheless, these explanations do not diminish the usefulness of these factors as predictors in our model. Future studies should validate these findings and compare model discrimination with and without these social factors.

Age is one predictor that has been noted to have a significant association with readmission risk in several studies but was non-significant in our final model. However, Medicare as primary insurance was a significant predictor in our final model and this variable incorporates age, which may partly explain these findings. Also, other predictive factors more commonly found in the elderly may already have been captured in our model, including Charlson comorbidity index and hospitalizations during the preceding one year.

In a supplementary analysis of patients discharged to sub-acute and long-term care facilities, we found that sociodemographic and social support variables were much less predictive of readmission. This may be because these factors mediate readmission through access to care and performance of self-care activities, and these factors are much less important when access to care is essentially continuous.

The performance characteristics of our model are only fair. However, they are comparable to the discriminative ability of other commonly cited readmission risk prediction models. Our AUC of 0.61 in the validation cohort is identical to the AUC of the P_ra (probability of repeated admission) model21,38. Similar to our model, the P_ra model was able to identify a small group (7.2%) of patients at high risk of readmission (41.8% with two or more admissions over 4 years). The one well-known model with a high AUC (0.83) included 20 variables with eight interaction terms, raising the possibility that it was overfit for the population used to derive it and limiting its usefulness as a practical clinical tool26. Two other high performing but similarly complicated models with AUC ranging from 0.68 to 0.75 were derived from the United Kingdom’s National Health Service database and are not usable in the U.S. due to their reliance on data from national electronic medical records24,25.

Why is it that so few statistical models derived to date are capable of reliably predicting readmission risk in a diverse population of medical inpatients? There are several possibilities. First, several important and previously unknown predictors may be missing from existing models. For example, we now know that adverse drug events (ADEs) are an important patient safety problem following hospital discharge, and existing models—including the one derived for this study—do not include many of the recently identified predictors of post-discharge ADEs (such as the number and classes of preadmission medications and patients’ knowledge of their medications)39,40. Second, generic markers of illness severity may be less predictive when evaluating populations with diverse medical conditions in contrast to disease-specific markers such as those for congestive heart failure12‐14. Third, it is plausible that readmission risk has a weaker correlation with patients’ clinical characteristics and social circumstances than it does with the processes of care during hospitalization and discharge and with post-discharge care41. That adjusted readmission rate varied by site is one piece of evidence in favor of this hypothesis. Thus, rather than identifying a single group of patients at high risk of readmission and focusing interventions on them, it may be more efficient to ensure that all patients receive a standardized set of discharge processes11. Alternatively, it may be worth identifying different types of high-risk patients and customizing interventions accordingly (e.g., a focused pharmacist intervention for patients at high risk for ADEs; close follow-up for patients with certain high-risk medical conditions).

Our study has several limitations. Although it was conducted at six academic medical centers in different states and included detailed information on a sizeable and diverse patient population, caution should be exercised in generalizing its findings to small, rural, and/or community hospitals. A sizeable proportion of screened patients could not be included in our study, further limiting generalizability; analyzing readmissions in this population, especially in patients who were discharged before they could be interviewed, may yield additional insights into the reasons behind early hospital readmission in patients with short lengths of stay. Furthermore, we excluded patients who died within 30 days of discharge because predictors of death may be somewhat different than predictors of readmission. Since we did not adjudicate whether each readmission was elective versus unplanned, we could not exclude purely elective readmissions; however, based on our collective experience we would expect the rate of elective readmissions on general medicine services to be low. Lastly, we were unable to confirm readmissions to non-study hospitals, and patients are known to underreport hospital readmissions 42. However, the short time-frame (only 30 days) for measuring readmissions and use of administrative data to confirm readmissions to study hospitals minimizes the potential impact of recall bias.

In summary, a prediction model derived and internally validated in a large multi-center cohort of general medicine inpatients successfully identified a small proportion of patients at elevated risk of hospital readmission within 30 days of home discharge. While interventions could be designed and tested on this population, more work is needed to identify additional factors that impact post-discharge health outcomes, optimize the discharge process for all patients, and create interventions tailored to patients’ needs in order to prevent potentially avoidable readmissions.