Readmission Following Surgical Resection for Intractable Epilepsy: Nationwide Rates, Causes, Predictors, and Outcomes.
Academic Article
Overview
abstract
BACKGROUND: Hospital readmissions can be detrimental to patients and may interfere with the potential benefits of the therapeutic procedure. Government agencies have begun to focus on reducing readmissions; however, the etiology of readmissions is lacking. OBJECTIVE: To report the national rates, risk factors, and outcomes associated with 30- and 90-d readmissions following surgery for intractable epilepsy. METHODS: We queried the Nationwide Readmissions Database from January to September 2013 using International Classification of Diseases, Ninth Edition, Clinical Modification codes to identify all patients with intractable epilepsy, who underwent hemispherectomy (01.52), brain lobectomy (01.53), amydalohippocampectomy, or partial lobectomy (01.59). Predictor variables included epilepsy type, presurgical diagnostic testing, surgery type, medical complications, surgical complications, and discharge disposition. RESULTS: In 1587 patients, the 30- and 90-d readmission rates were 11.5% and 16.8%, respectively. The most common reasons for readmission were persistent epilepsy, video electroencephalography monitoring, postoperative infection, and postoperative central nervous system complication. In multivariable analysis, risk factors associated with both 30- and 90-d readmission were Medicare payer status, lowest quartile of median income, depression, hemispherectomy, and postoperative complications (P < .05). The only unique predictor of 30-d readmission was small bedsize hospital (P = .001). Readmissions within 30 d were associated with longer length of stay (6.8 vs 5.8 d), greater costs ($18 660 vs $15 515), and increased adverse discharges (26.4% vs 21.8%). CONCLUSION: Following epilepsy surgery, most readmissions that occurred within 30 d can be attributed to management of persistent epilepsy and predicted by Medicare payer status, depression, and complications. These data can assist the clinician in preventing readmissions and assist policy makers determine which admissions are potentially avoidable.
