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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Background: Surgeries represent a mainstay of medical care globally. Patterns of complications are frequently recognized late and place a considerable burden on health care systems. The aim was to develop and test the first deep learning-adjusted CUSUM program (DL-CUSUM) to predict and monitor in-hospital mortality in real time after liver transplantation. Methods: Data from 1066 individuals with 66,092 preoperatively available data point variables from 2004 to 2019 were included. DL-CUSUM is an application to predict in-hospital mortality. The area under the curve for risk adjustment with Model of End-stage Liver Disease (D-MELD), Balance of Risk (BAR) score, and deep learning (DL), as well as the ARL (average run length) and control limit (CL) for an in-control process over 5 years, were calculated. Results: D-MELD AUC was 0.618, BAR AUC was 0.648 and DL model AUC was 0.857. CL with BAR adjustment was 2.3 with an ARL of 326.31. D-MELD reached an ARL of 303.29 with a CL of 2.4. DL prediction resulted in a CL of 1.8 to reach an ARL of 332.67. Conclusions: This work introduces the first use of an automated DL-CUSUM system to monitor postoperative in-hospital mortality after liver transplantation. It allows for the real-time risk-adjusted monitoring of process quality.

Details

Title
Deep Learning-Adjusted Monitoring of In-Hospital Mortality after Liver Transplantation
Author
Börner, Nikolaus 1   VIAFID ORCID Logo  ; Schoenberg, Markus B 2   VIAFID ORCID Logo  ; Pöllmann, Benedikt 3 ; Pöschke, Philipp 4 ; Böhm, Christian 4 ; Koch, Dominik 1 ; Drefs, Moritz 1   VIAFID ORCID Logo  ; Koliogiannis, Dionysios 1   VIAFID ORCID Logo  ; Andrassy, Joachim 1   VIAFID ORCID Logo  ; Werner, Jens 1 ; Markus Otto Guba 1 

 Department of General, Visceral, and Transplant Surgery, LMU, 81377 Munich, Germany; [email protected] (M.B.S.); [email protected] (M.O.G.); Transplantation Center Munich, LMU Munich, Campus Grosshadern, 81377 Munich, Germany 
 Department of General, Visceral, and Transplant Surgery, LMU, 81377 Munich, Germany; [email protected] (M.B.S.); [email protected] (M.O.G.); Transplantation Center Munich, LMU Munich, Campus Grosshadern, 81377 Munich, Germany; Medical Centers Gollierplatz and Nymphenburg, 80339 Munich, Germany 
 Department of General, Visceral, and Transplant Surgery, LMU, 81377 Munich, Germany; [email protected] (M.B.S.); [email protected] (M.O.G.) 
 Institute of Informatics, LMU, 81377 Munich, Germany 
First page
6046
Publication year
2024
Publication date
2024
Publisher
MDPI AG
e-ISSN
20770383
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3120675373
Copyright
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.