Deep Learning and Multivariable Models Select EVAR Patients for Short-Stay Discharge

Vasc Endovascular Surg. 2021 Jan;55(1):18-25. doi: 10.1177/1538574420954299. Epub 2020 Sep 10.


Objectives: We sought to develop a prediction score with data from the Vascular Quality Initiative (VQI) EVAR in efforts to assist endovascular specialists in deciding whether or not a patient is appropriate for short-stay discharge.

Background: Small series describe short-stay discharge following elective EVAR. Our study aims to quantify characteristics associated with this decision.

Methods: The VQI EVAR and NSQIP datasets were queried. Patients who underwent elective EVAR recorded in VQI, between 1/2010-5/2017 were split 2:1 into test and analytic cohorts via random number assignment. Cross-reference with the Medicare claims database confirmed all-cause mortality data. Bootstrap sampling was employed in model. Deep learning algorithms independently evaluated each dataset as a sensitivity test.

Results: Univariate outcomes, including 30-day survival, were statistically worse in the DD group when compared to the SD group (all P < 0.05). A prediction score, SD-EVAR, derived from the VQI EVAR dataset including pre- and intra-op variables that discriminate between SD and DD was externally validated in NSQIP (Pearson correlation coefficient = 0.79, P < 0.001); deep learning analysis concurred. This score suggests 66% of EVAR patients may be appropriate for short-stay discharge. A free smart phone app calculating short-stay discharge potential is available through QxMD Calculate

Conclusions: Selecting patients for short-stay discharge after EVAR is possible without increasing harm. The majority of infrarenal AAA patients treated with EVAR in the United States fit a risk profile consistent with short-stay discharge, representing a significant cost-savings potential to the healthcare system.

Keywords: EVAR; aneurysm; care pathways; health services research.

Publication types

  • Validation Study

MeSH terms

  • Aged
  • Aged, 80 and over
  • Aortic Aneurysm / diagnosis
  • Aortic Aneurysm / mortality
  • Aortic Aneurysm / surgery*
  • Clinical Decision-Making*
  • Databases, Factual
  • Decision Support Techniques*
  • Deep Learning*
  • Endovascular Procedures* / adverse effects
  • Endovascular Procedures* / mortality
  • Enhanced Recovery After Surgery*
  • Female
  • Health Services Research
  • Humans
  • Length of Stay*
  • Male
  • Mobile Applications
  • Multivariate Analysis
  • Patient Discharge*
  • Patient Selection
  • Risk Assessment
  • Risk Factors
  • Smartphone
  • Time Factors
  • Treatment Outcome