MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03453nam a22003857a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
220926s20222022 xxu||||| |||| 00| 0 eng d |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER |
|---|
| International Standard Serial Number |
0894-7317 |
| 024 ## - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
10.1016/j.echo.2022.07.004 [doi] |
| 024 ## - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
PMC9293371 [pmc] |
| 024 ## - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
S0894-7317(22)00351-0 [pii] |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
Ovid MEDLINE(R) |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) |
|---|
| PMID |
35863542 |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Human vs Artificial Intelligence-Based Echocardiography Analysis as Predictor of Outcomes: An analysis from the World Alliance Societies of Echocardiography COVID study. |
| 251 ## - Source |
|---|
| Source |
Journal of the American Society of Echocardiography. 2022 Jul 18 |
| 252 ## - Abbreviated Source |
|---|
| Abbreviated source |
J Am Soc Echocardiogr. 2022 Jul 18 |
| 253 ## - Journal Name |
|---|
| Journal name |
Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Year |
2022 |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Manufacturer |
FY2023 |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Publication date |
2022 Jul 18 |
| 265 ## - SOURCE FOR ACQUISITION/SUBSCRIPTION ADDRESS [OBSOLETE] |
|---|
| Publication status |
aheadofprint |
| 266 ## - Date added to catalog |
|---|
| Date added to catalog |
2022-09-26 |
| 501 ## - WITH NOTE |
|---|
| Local holdings |
Available online through MWHC library: 2007 - present |
| 520 ## - SUMMARY, ETC. |
|---|
| Abstract |
BACKGROUND: Transthoracic echocardiography (TTE) is the leading cardiac imaging modality for patients admitted with COVID-19 infection, a condition of high short-term mortality. We aimed to test the hypothesis that artificial intelligence (AI) based analysis of echocardiographic images could predict mortality more accurately than conventional analysis by a human expert. |
| 520 ## - SUMMARY, ETC. |
|---|
| Abstract |
CONCLUSIONS: AI-based analysis of LVEF and LVLS had a similar feasibility to manual analysis, minimized variability and consequently increased the statistical power to predict mortality. AI-based analyses, but not manual, were significant predictors of in-hospital and follow-up mortality. Copyright © 2022. Published by Elsevier Inc. |
| 520 ## - SUMMARY, ETC. |
|---|
| Abstract |
METHODS: Patients admitted to 13 hospitals for acute COVID-19 disease who had a TTE were included. Left ventricular (LV) ejection fraction (EF) and LV longitudinal strain (LS) were obtained manually by multiple expert readers and by an automated, AI software. The ability of the manual and AI analyses to predict all-cause mortality was compared. |
| 520 ## - SUMMARY, ETC. |
|---|
| Abstract |
RESULTS: 870 patients were enrolled, mortality was 27.4% at a follow-up of 230+/-115 days. AI analysis had lower variability than manual for both LV EF (p=0.003) and LS (p=0.005). AI-derived LV EF and LS were predictors of mortality in univariable and multivariable regression analysis (OR=0.974, 95% CI= 0.956-0.991, p=0.003 for EF; OR=1.060, 95% CI 1.019-1.105, p=0.004 for LS), but LV EF and LS obtained by manual analysis were not. Direct comparison of predictive value of AI vs manual measurements of LV EF and LS was significantly better for AI (p=0.005 and 0.003 respectively). In addition, AI-derived LV EF and LS had more significant and stronger correlations to other objective biomarkers for acute disease than manual reads. |
| 546 ## - LANGUAGE NOTE |
|---|
| Language note |
English |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
IN PROCESS -- NOT YET INDEXED |
| 651 ## - SUBJECT ADDED ENTRY--GEOGRAPHIC NAME |
|---|
| Institution |
MedStar Health Research Institute |
| 657 ## - INDEX TERM--FUNCTION |
|---|
| Medline publication type |
Journal Article |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Local Authors |
Asch, Federico M |
| 790 ## - Authors |
|---|
| All authors |
Addetia K, Alizadehasl A, Asch FM, Citro R, Descamps T, Karagodin I, Lang RM, Monaghan MJ, Moreo A, Mostafavi A, Narang A, Ordonez Salazar BA, Sarwar R, Singulane CC, Soulat-Dufour L, Tucay ES, Tude Rodrigues AC, Upton R, Vasquez-Ortiz ZY, WASE-COVID Investigators, Woodward GM, Wu C, Xie M |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| DOI |
<a href="https://dx.doi.org/10.1016/j.echo.2022.07.004">https://dx.doi.org/10.1016/j.echo.2022.07.004</a> |
| Public note |
https://dx.doi.org/10.1016/j.echo.2022.07.004 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Journal Article |
| Item type description |
Article |
