Predicting reoperation and readmission for head and neck free flap patients using machine learning

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Abstract

Background

To develop machine learning (ML) models predicting unplanned readmission and reoperation among patients undergoing free flap reconstruction for head and neck (HN) surgery.

Methods

Data were extracted from the 2012–2019 NSQIP database. eXtreme Gradient Boosting (XGBoost) was used to develop ML models predicting 30‐day readmission and reoperation based on demographic and perioperative factors. Models were validated using 2019 data and evaluated.

Results

Four‐hundred and sixty‐six (10.7%) of 4333 included patients were readmitted within 30 days of initial surgery. The ML model demonstrated 82% accuracy, 63% sensitivity, 85% specificity, and AUC of 0.78. Nine‐hundred and four (18.3%) of 4931 patients underwent reoperation within 30 days of index surgery. The ML model demonstrated 62% accuracy, 51% sensitivity, 64% specificity, and AUC of 0.58.

Conclusion

XGBoost was used to predict 30‐day readmission and reoperation for HN free flap patients. Findings may be used to assist clinicians and patients in shared decision‐making and improve data collection in future database iterations.

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Most cited references 32

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XGBoost

Tianqi Chen, Carlos Guestrin (2016)

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Variation in surgical-readmission rates and quality of hospital care.

Thomas Tsai, Karen Joynt, E. Orav … (2013)

Reducing hospital-readmission rates is a clinical and policy priority, but little is known about variation in rates of readmission after major surgery and whether these rates at a given hospital are related to other markers of the quality of surgical care. Using national Medicare data, we calculated 30-day readmission rates after hospitalization for coronary-artery bypass grafting, pulmonary lobectomy, endovascular repair of abdominal aortic aneurysm, open repair of abdominal aortic aneurysm, colectomy, and hip replacement. We used bivariate and multivariate techniques to assess the relationships between readmission rates and other measures of surgical quality, including adherence to surgical process measures, procedure volume, and mortality. For the six index procedures, there were 479,471 discharges from 3004 hospitals. The median risk-adjusted composite readmission rate at 30 days was 13.1% (interquartile range, 9.9 to 17.1). In a multivariate model adjusting for hospital characteristics, we found that hospitals in the highest quartile for surgical volume had a significantly lower composite readmission rate than hospitals in the lowest quartile (12.7% vs. 16.8%, P<0.001), and hospitals with the lowest surgical mortality rates had a significantly lower readmission rate than hospitals with the highest mortality rates (13.3% vs. 14.2%, P<0.001). High adherence to reported surgical process measures was only marginally associated with reduced readmission rates (highest quartile vs. lowest quartile, 13.1% vs. 13.6%; P=0.02). Patterns were similar when each of the six major surgical procedures was examined individually. Nearly one in seven patients hospitalized for a major surgical procedure is readmitted to the hospital within 30 days after discharge. Hospitals with high surgical volume and low surgical mortality have lower rates of surgical readmission than other hospitals.

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Is the Readmission Rate a Valid Quality Indicator? A Review of the Evidence

Claudia Fischer, Hester F Lingsma, Perla Marang-van de Mheen … (2014)

Introduction Hospital readmission rates are increasingly used for both quality improvement and cost control. However, the validity of readmission rates as a measure of quality of hospital care is not evident. We aimed to give an overview of the different methodological aspects in the definition and measurement of readmission rates that need to be considered when interpreting readmission rates as a reflection of quality of care. Methods We conducted a systematic literature review, using the bibliographic databases Embase, Medline OvidSP, Web-of-Science, Cochrane central and PubMed for the period of January 2001 to May 2013. Results The search resulted in 102 included papers. We found that definition of the context in which readmissions are used as a quality indicator is crucial. This context includes the patient group and the specific aspects of care of which the quality is aimed to be assessed. Methodological flaws like unreliable data and insufficient case-mix correction may confound the comparison of readmission rates between hospitals. Another problem occurs when the basic distinction between planned and unplanned readmissions cannot be made. Finally, the multi-faceted nature of quality of care and the correlation between readmissions and other outcomes limit the indicator's validity. Conclusions Although readmission rates are a promising quality indicator, several methodological concerns identified in this study need to be addressed, especially when the indicator is intended for accountability or pay for performance. We recommend investing resources in accurate data registration, improved indicator description, and bundling outcome measures to provide a more complete picture of hospital care.

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Author and article information

Contributors

Stephanie Y. Wang: (View ORCID Profile)

Louis‐Xavier Barrette: (View ORCID Profile)

Jinggang J. Ng: (View ORCID Profile)

Neel R. Sangal: (View ORCID Profile)

Robert M. Brody: (View ORCID Profile)

Andrés M. Bur: (View ORCID Profile)

Jason A. Brant: (View ORCID Profile)

Journal

Title: Head & Neck

Abbreviated Title: Head & Neck

Publisher: Wiley

ISSN (Print): 1043-3074

ISSN (Electronic): 1097-0347

Publication date Created: August 2024

Publication date (Electronic): February 15 2024

Publication date (Print): August 2024

Volume: 46

Issue: 8

Pages: 1999-2009

Affiliations

[1 ] Department of Otolaryngology – Head and Neck Surgery University of Pennsylvania Philadelphia Pennsylvania USA

[2 ] Leonard Davis Institute of Health Economics University of Pennsylvania Philadelphia Pennsylvania USA

[3 ] Corporal Michael J. Crescenz VAMC Philadelphia Pennsylvania USA

[4 ] Department of Otolaryngology – Head and Neck Surgery University of Kansas Medical Center Kansas City Kansas USA

Article

DOI: 10.1002/hed.27690

SO-VID: 9f936f02-b370-4879-b49d-854b19f95516

License:

http://creativecommons.org/licenses/by-nc-nd/4.0/

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