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

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 https://qxcalc.app.link/vqidis.

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.