Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Novel Method for Determining Fibrin/Fibrinogen Degradation Product and Fibrinogen Threshold Criteria by Artificial Intelligence in Cases of Massive Hemorrhage during Delivery with Hematuria

Yasunari Miyagi
* ORCID logo ,
Katsuhiko Tada
,
Ichiro Yasuhi
,
Keisuke Tsumura
,
Yuka Maegawa
,
Norifumi Tanaka
,
Tomoya Mizunoe
,
Ikuko Emoto
,
Kazuhisa Maeda
,
Kosuke Kawakami
Version 1 : Received: 25 February 2024 / Approved: 26 February 2024 / Online: 27 February 2024 (08:01:48 CET)

A peer-reviewed article of this Preprint also exists.

Miyagi, Y.; Tada, K.; Yasuhi, I.; Tsumura, K.; Maegawa, Y.; Tanaka, N.; Mizunoe, T.; Emoto, I.; Maeda, K.; Kawakami, K.; on behalf of the Collaborative Research in National Hospital Organization Network Pediatric and Perinatal Group. A Novel Method for Determining Fibrin/Fibrinogen Degradation Products and Fibrinogen Threshold Criteria via Artificial Intelligence in Massive Hemorrhage during Delivery with Hematuria. J. Clin. Med. 2024, 13, 1826. Miyagi, Y.; Tada, K.; Yasuhi, I.; Tsumura, K.; Maegawa, Y.; Tanaka, N.; Mizunoe, T.; Emoto, I.; Maeda, K.; Kawakami, K.; on behalf of the Collaborative Research in National Hospital Organization Network Pediatric and Perinatal Group. A Novel Method for Determining Fibrin/Fibrinogen Degradation Products and Fibrinogen Threshold Criteria via Artificial Intelligence in Massive Hemorrhage during Delivery with Hematuria. J. Clin. Med. 2024, 13, 1826.

Abstract

(1) Background: Although diagnostic criteria for massive hemorrhage with organ dysfunction such as disseminated intravascular coagulation associated with delivery have been empirically established based on clinical findings, strict logic has yet to be used to establish numerical criteria. (2) Methods: A dataset of 107 deliveries with ≥2,000 g of blood loss, among 13,368 deliveries from nine national perinatal centers in Japan between 2020 and 2023 was obtained. Twenty-three patients had fibrinogen levels <170 mg/dl, which is the initiation of coagulation system failure per our previous reports. Three of these patients had hematuria. We used six machine learning methods to identify the borderline criteria dividing the fibrinogen/fibrin/fibrinogen degradation product (FDP) planes, using 15 coagulation fibrinolytic factors. (3) Results: The boundaries of hematuria development on a two-dimensional plane of fibrinogen and FDP were obtained. If the FDP–fibrinogen/3–60 (mg/dl) value is positive, this indicates hematuria; otherwise, the case is non-hematuria, as demonstrated by the support vector machine method that seemed the most appropriate. (4) Conclusions: The borderline criterion dividing the fibrinogen/FDP plane for patients with hematuria that could be considered organ dysfunction in massive hemorrhage during delivery was obtained using artificial intelligence, and this method seemed to be useful.

Keywords

artificial intelligence; delivery; DIC; hemorrhage; machine learning

Subject

Medicine and Pharmacology, Obstetrics and Gynaecology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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