Machine Learning-Based Analysis and Prediction of Unplanned 30-Day Readmissions After Pituitary Adenoma Resection: A Multi-Institutional Retrospective Study With External Validation

Brendan T. Crabb, Forrest Hamrick, Justin M. Campbell, Joshua Vignolles-Jeong, Stephen T. Magill, Daniel M. Prevedello, Ricardo L. Carrau, Bradley A. Otto, Douglas A. Hardesty, William T. Couldwell, Michael Karsy

Research output: Contribution to journalArticlepeer-review

Abstract

BACKGROUND: Unplanned readmission after transsphenoidal resection of pituitary adenoma can occur in up to 10% of patients but is unpredictable. OBJECTIVE: To develop a reliable system for predicting unplanned readmission and create a validated method for stratifying patients by risk. METHODS: Data sets were retrospectively collected from the National Surgical Quality Improvement Program and 2 tertiary academic medical centers. Eight machine learning classifiers were fit to the National Surgical Quality Improvement Program data, optimized using Bayesian parameter optimization and evaluated on the external data. Permutation analysis identified the relative importance of predictive variables, and a risk stratification system was built using the trained machine learning models. RESULTS: Readmissions were accurately predicted by several classification models with an area under the receiving operator characteristic curve of 0.76 (95% CI 0.68-0.83) on the external data set. Permutation analysis identified the most important variables for predicting readmission as preoperative sodium level, returning to the operating room, and total operation time. High-risk and medium-risk patients, as identified by the proposed risk stratification system, were more likely to be readmitted than low-risk patients, with relative risks of 12.2 (95% CI 5.9-26.5) and 4.2 (95% CI 2.3-8.7), respectively. Overall risk stratification showed high discriminative capability with a C-statistic of 0.73. CONCLUSION: In this multi-institutional study with outside validation, unplanned readmissions after pituitary adenoma resection were accurately predicted using machine learning techniques. The features identified in this study and the risk stratification system developed could guide clinical and surgical decision making, reduce healthcare costs, and improve the quality of patient care by better identifying high-risk patients for closer perioperative management.

Original languageEnglish (US)
Pages (from-to)263-271
Number of pages9
JournalNeurosurgery
Volume91
Issue number2
DOIs
StatePublished - Aug 1 2022

ASJC Scopus subject areas

  • Medicine(all)

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