Using a retrospective chart review, researchers developed and validated a risk score for potentially avoidable readmissions by categorizing patients into low, intermediate, or high risk for potentially avoidable hospital readmission 30 days after initial admission, according to a study published in PLoS One.

The researchers analyzed variables assessing specific characteristics that led to an increased risk for potentially avoidable hospital readmission, deriving an equation that stratifies patients into risk categories using a derivation cohort, and then they internally validated the equation using a validation cohort. Chart reviews of administrative databases provided information on demographic data, hospital length of stay, previous admissions, clinical diagnoses, comorbidities, laboratory results, and medication prescription.

Of the 10,374 inpatient hospital stays included in this study, 7317 were in the derivation cohort, and 3057 were in the validation cohort. Using multivariate analysis, 13 variables were significantly and independently associated with increased risk for a potentially avoidable hospital readmission, and 12 of these variables were included in the potentially avoidable readmissions risk score prediction model. These variables included length of hospital stay >4 days, previous hospital admission, anemia, hypertension, hyperkalemia, opioid prescription, heart failure comorbidity, acute myocardial infarction, chronic ischemic heart disease, diabetes with organ damage, cancer, and metastatic carcinoma. Using the potentially avoidable readmissions risk score prediction model, patients were categorized into low-, intermediate-, and high-risk tertiles for potentially avoidable hospital readmissions. The validation of the potentially avoidable readmissions risk score prediction model indicated the derivation cohort had a C-statistic of 0.699 (95% CI, 0.677-0.721) and a Brier score of 0.068, and the validation cohort had a C-statistic of 0.687 (95% CI, 0.654-0.721) and a Brier score of 0.065.

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Limitations of this study include using administrative databases for all data; not accounting for patients transferred to rehabilitation hospitals or being readmitted to a different hospital; not including functional status, social support, socioeconomic condition, or medication adherence in the data collection process; and using a retrospective internal cohort to validate the prediction model.


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The researchers concluded, “The [Potentially Avoidable Readmissions] Risk Score may help to identify high-risk patients before discharge home, and this should help healthcare providers to target complex transitional interventions that improve the coordination of care with the overarching goal of decreasing readmission rates.”

Reference 

Blanc A-L, Fumeaux T, Stirnemann J, et al. Development of a predictive score for potentially avoidable hospital readmissions for general internal medicine patients [published online July 15, 2019]. PLoS One. doi: 10.1371/journal.pone.0219348