دورية أكاديمية
Deep Learning and Multivariable Models Select EVAR Patients for Short-Stay Discharge
| العنوان: | Deep Learning and Multivariable Models Select EVAR Patients for Short-Stay Discharge |
|---|---|
| المؤلفون: | Zarkowsky, Devin S., Nejim, Besma, Hubara, Itay, Hicks, Caitlin W., Goodney, Philip P., Malas, Mahmoud B. |
| المساهمون: | The Orange County Community Foundation UCSF Vascular Research Fellowship |
| المصدر: | Vascular and Endovascular Surgery ; volume 55, issue 1, page 18-25 ; ISSN 1538-5744 1938-9116 |
| بيانات النشر: | SAGE Publications |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Cardiology and Cardiovascular Medicine, General Medicine, Surgery |
| الوصف: | 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/vqidisTest. 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| DOI: | 10.1177/1538574420954299 |
| الإتاحة: | https://doi.org/10.1177/1538574420954299Test |
| حقوق: | http://journals.sagepub.com/page/policies/text-and-data-mining-licenseTest |
| رقم الانضمام: | edsbas.1BB0530A |
| قاعدة البيانات: | BASE |
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