Factors associated with 30-day readmission for patients hospitalized for seizures

Abstract

Background: We sought to determine the cumulative incidence of readmissions after a seizure-related hospitalization and identify risk factors and readmission diagnoses.

Methods: We performed a retrospective cohort study of adult patients hospitalized with a primary discharge diagnosis of seizure (International Classification of Diseases, Ninth Edition, Clinical Modification codes 345.xx and 780.3x) using the State Inpatient Databases across 11 states from 2009 to 2012. Hospital and community characteristics were obtained from the American Hospital Association and Robert Wood Johnson Foundation. We performed logistic regressions to explore effects of patient, hospital, and community factors on readmissions within 30 days of discharge.

Results: Of 98,712 patients, 13,929 (14%) were readmitted within 30 days. Reasons for readmission included epilepsy/convulsions (30% of readmitted patients), mood disorders (5%), schizophrenia (4%), and septicemia (4%). The strongest predictors of readmission were diagnoses of CNS tumor (odds ratio [OR] 2.1, 95% confidence interval [CI] 1.9-2.4) or psychosis (OR 1.8, 95% CI 1.7-1.8), urgent index admission (OR 2.0, 95% CI 1.8-2.2), transfer to nonacute facilities (OR 1.7, 95% CI 1.6-1.8), long length of stay (OR 1.7, 95% CI 1.6-1.8), and for-profit hospitals (OR 1.7, 95% CI 1.6-1.8). Our main model's c-statistic was 0.66. Predictors of readmission for status epilepticus included index admission for status epilepticus (OR 3.5, 95% CI 2.6-4.7), low hospital epilepsy volume (OR 0.4, 95% CI 0.3-0.7), and rural hospitals (OR 4.8, 95% CI 2.1-10.9).

Conclusion: Readmission is common after hospitalization for seizures. Prevention strategies should focus on recurrent seizures, the most common readmission diagnosis. Many factors were associated with readmission, although readmissions remain challenging to predict.

Figure 1. Patient flowchart

“Inconsistent demographics” refers to those 9,165 patients excluded because of a discrepancy among data regarding sex (more than 1 sex listed across visits within the same patient identifier), or age or hospitalization dates (range of age or hospitalization dates, respectively, across visits within a patient identifier were greater than plausible by the range of calendar years listed for their hospitalizations during the study period). Of these, patients were excluded because of the following combinations of discrepant variables: 1472 sex and age and dates, 168 sex and age, 56 sex and dates, 6 sex only, 1500 age and dates, 5643 age only, and 320 dates only.

Figure 2. Calibration curve

We calculated predicted probabilities of all-cause 30-day readmissions from our main analysis, model 2. Each data point for predicted probabilities above represents the mean for that decile of predicted probability from our main logistic regression. Overlaid is the observed readmission proportion for patients in each decile of predicted probability. Error bars represent SDs.