New method for determining fibrinogen and FDP threshold criteria by artificial intelligence in cases of massive hemorrhage during delivery.

Aim: To investigate the feasibility of a novel method using artificial intelligence (AI), in which the fibrinogen criterion was determined by the quantitative relation between the distributions of fibrin/fibrinogen degradation products (FDPs) and fibrinogen.

Methods: A dataset of 154 deliveries comprising more than 2000 g of blood lost due to hemorrhage, excluding disseminated intravascular coagulation (DIC), among patients from eight national perinatal centers in Japan from 2011 to 2015 were obtained. The fibrinogen threshold criterion was identified by using the function that best fit the distributions of FDP as determined by AI. FDP production was described by differential equations using a dataset containing fibrinogen levels less than the fibrinogen criterion and solved numerically.

Results: A fibrinogen level of 237 mg/dL as the threshold criterion was obtained. The FDP threshold criteria were 2.0 and 8.5 mg/dL for no coagulopathy and a failed coagulation system, respectively.

Conclusion: The fibrinogen threshold criterion for patients with massive hemorrhage excluding DIC at delivery were obtained by selecting the functions that best fit the distributions of FDP data by using AI.