A statistical algorithm based on routine inpatient data performed only moderately in identifying patients at risk of readmission within 12 months, achieving a sensitivity of 44.7% at a 50% risk threshold.
Observational (n=17,699)
Yes
Can a statistical algorithm based on routine inpatient data accurately identify patients with chronic medical conditions at risk of hospital readmission within 12 months?
A statistical algorithm based on routine inpatient data showed only modest discriminatory power (ROC c=0.65) for predicting 12-month hospital readmission, limited by a high rate of false negatives.
BACKGROUND: A relatively small percentage of patients with chronic medical conditions account for a much larger percentage of inpatient costs. There is some evidence that case-management can improve health and quality-of-life and reduce the number of times these patients are readmitted. To assess whether a statistical algorithm, based on routine inpatient data, can be used to identify patients at risk of readmission and who would therefore benefit from case-management. METHODS: Queensland database study of public-hospital patients, who had at least one emergency admission for a chronic medical condition (e.g., congestive heart failure, chronic obstructive pulmonary disease, diabetes or dementia) during 2005/2006. Multivariate logistic regression was used to develop an algorithm to predict readmission within 12 months. The performance of the algorithm was tested against recorded readmissions using sensitivity, specificity, and Likelihood Ratios (positive and negative). RESULTS: Several factors were identified that predicted readmission (i.e., age, co-morbidities, economic disadvantage, number of previous admissions). The discriminatory power of the model was modest as determined by area under the receiver operating characteristic (ROC) curve (c = 0.65). At a risk score threshold of 50, the algorithm identified only 44.7% (95% CI: 42.5%, 46.9%) of patients admitted with a reference condition who had an admission in the next 12 months; 37.5% (95% CI: 35.0%, 40.0%) of patients were flagged incorrectly (they did not have a subsequent admission). CONCLUSION: A statistical algorithm based on Queensland hospital inpatient data, performed only moderately in identifying patients at risk of readmission. The main problem is that there are too many false negatives, which means that many patients who might benefit would not be offered case-management.
Howell et al. (Tue,) conducted a observational in Chronic medical conditions (n=17,699). Statistical predictive algorithm was evaluated on Sensitivity for predicting readmission within 12 months at a 50% risk threshold (95% CI 42.5-46.9). A statistical algorithm based on routine inpatient data performed only moderately in identifying patients at risk of readmission within 12 months, achieving a sensitivity of 44.7% at a 50% risk threshold.