ArticleJournal of neurological surgery. Part B, Skull base2025
A Multicenter Study of Unplanned Hospital Readmissions after Transsphenoidal Surgery for Cushing's Disease.
Article in Journal of neurological surgery. Part B, Skull base, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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14 authors.
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Abstract
Objectives: Patients undergoing surgery for Cushing's disease may be more likely to be readmitted to the hospital than other patients with pituitary disorders. We investigated rates, causes, and predictors of unplanned readmission following transsphenoidal surgery for Cushing's disease to identify areas for clinical, financial, and administrative improvements. Design: Retrospective cohort study. Setting: Academic pituitary centers in the United States participating in a multicenter surgical outcome registry. Participants: Five hundred and nineteen patients underwent transsphenoidal surgery for treatment of Cushing's disease by 26 surgeons at nine participating institutions from 2003 to 2023. Main Outcome Measures: Unplanned 90-day readmission rates and causes of readmission. Results: Unplanned readmissions occurred in 57/519 patients (11.0%), with hyponatremia in 12/57 (21%), cerebrospinal fluid leak evaluation in 8/57 (14%), epistaxis in 6/57 (10%), deep vein thrombosis in 4/57 (7%), syncope in 3/57 (5%), and headache in 3/57 (5%). Factors including no tumor on initial magnetic resonance imaging, return to the operating room during the index admission, lack of early remission, and inpatient complications were associated with a greater probability of readmission on univariate analysis. However, none remained predictive on multivariate analysis. Conclusion: Our results show that readmission rates after transsphenoidal surgery for Cushing's disease are comparable to previously reported rates for all pituitary-related disorders, with the most common reason being hyponatremia. High-impact clinical protocols focused on preventing delayed hyponatremia may reduce the risk of readmission. Failure to identify significant predictors of readmission, even in this large clinical dataset, underscores the challenge of identifying high-risk clinical cohorts.
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