(1) Background: Although the 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 >2000 mL of blood loss, among 13,368 deliveries, was obtained from nine national perinatal centers in Japan between 2020 and 2023. Twenty-three patients had fibrinogen levels <170 mg/dL, which is the initiation of coagulation system failure, according to 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. A positive FDP–fibrinogen/3–60 (mg/dL) value indicates hematuria; otherwise, the case is nonhematuria, as demonstrated by the support vector machine method that seemed the most appropriate. (4) Conclusions: Using artificial intelligence, the borderline criterion was obtained, which divides the fibrinogen/FDP plane for patients with hematuria that could be considered organ dysfunction in massive hemorrhage during delivery; this method appears to be useful.

(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.

Background: Understanding patients’ underlying pathologies is essential for appropriate treatment during postpartum hemorrhage (PPH) management. The objective of this study is to develop an algorithm that quantitatively classifies the underlying pathologies in PPH using clinical parameters. Methods: A multicenter case series study involving nine perinatal centers in Japan was conducted on patients with severe PPH (blood loss > 2000 mL) excluding placental abruption, and placental abruption irrespective of blood loss spanning August 2020 to June 2024. The primary outcome was the development of an algorithm for classifying the underlying pathologies in PPH, based on bleeding rate, fibrinogen, and fibrin/fibrinogen degradation products (FDP). We used collected data to develop an algorithm by which cases with abnormally high bleeding rate (≥ 61 mL/min) were classified as whole blood loss with high bleeding rate (WBLH). Of cases with bleeding rate < 61 mL/min, those with fibrinogen ≥ 237 mg/dL were classified as non-coagulopathy (NCP). Of cases with fibrinogen < 237 mg/dL, those with FDP < 2 mg/dL and FDP ≥ 2 mg/dL were classified as whole blood loss with low bleeding rate (WBLL) and coagulation factor consumption (CFC), respectively. In CFC cases, a predictive formula distinguished between disseminated intravascular coagulation (DIC) from locally initiated and progressive consumptive coagulopathy for hemostasis (LIPC). We used the Mann–Whitney test and Fisher’s exact test for the statistical analysis. Results: Among 171 participants, comprising 131 severe PPH cases and 40 placental abruption cases, the algorithm classified cases into WBLH ( n = 9), NCP ( n = 88), WBLL ( n = 12), and CFC ( n = 62). Of the CFC cases, 3 had DIC and 59 had LIPC. FDP levels did not differ between WBLH (median, 1.19 mg/dL) and WBLL (median, 0.91 mg/dL). Hemoglobin (median, 6.3 g/dL) in WBLL and fibrinogen (median, 168.5 mg/dL) in CFC were significantly lower than for the other three categories ( P < 0.05). Coagulation-fibrinolytic activation in DIC was significantly increased compared with LIPC cases. Conclusions: Our algorithm, using bleeding rate, fibrinogen, and FDP as clinical parameters, classified PPH cases into four underlying pathologies. This pilot study provides a foundation for pathophysiology-based clinical management of PPH.