Deep Learning and Multivariable Models Select EVAR Patients for Short-Stay Discharge
Devin S Zarkowsky1, Besma Nejim2, Itay Hubara3
1Division of Vascular and Endovascular Surgery, 1878University of Colorado, Aurora, CO, USA.
Vascular and Endovascular Surgery
|September 10, 2020
Summary
A new prediction score, SD-EVAR, can identify patients suitable for short-stay discharge after endovascular aneurysm repair (EVAR) without increasing harm. This tool suggests most EVAR patients may be candidates for shorter hospital stays, offering significant cost savings.
Area of Science:
- Vascular Surgery
- Health Services Research
- Predictive Analytics
Background:
- Short-stay discharge after elective endovascular aneurysm repair (EVAR) is increasingly described.
- Characteristics associated with short-stay discharge decisions require quantification.
Purpose of the Study:
- To develop a prediction score using Vascular Quality Initiative (VQI) EVAR data.
- To assist endovascular specialists in determining patient appropriateness for short-stay discharge.
Main Methods:
- Utilized VQI EVAR and NSQIP datasets (1/2010-5/2017).
- Split patients 2:1 into test and analytic cohorts; confirmed mortality via Medicare claims.
- Employed bootstrap sampling and deep learning for model validation.
Main Results:
- Developed the SD-EVAR score from VQI EVAR data, validated in NSQIP (Pearson r=0.79, P<0.001).
- The score indicates 66% of EVAR patients may be suitable for short-stay discharge.
- A smartphone app is available for calculating short-stay discharge potential.
Conclusions:
- Patient selection for short-stay discharge after EVAR is feasible without adverse outcomes.
- Most infrarenal abdominal aortic aneurysm patients treated with EVAR meet criteria for short-stay discharge.
- This approach offers substantial cost-saving potential for the healthcare system.
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