Prediction of post-delivery hemoglobin levels with machine learning algorithms

www.nature.com/scientificreports OPEN Prediction of post‑delivery hemoglobin levels with machine learning algorithms Sepehr Aghajanian 1,2, Kyana Jafarabady 1, Mohammad Abbasi 1, Fateme Mohammadifard 1, Mina Bakhshali Bakhtiari 3, Nasim Shokouhi 4, Soraya Saleh Gargari 3,7* & Mahmood Bakhtiyari 5,6* Predicting postpartum hemorrhage (PPH) before delivery is crucial for enhancing patient outcomes, enabling timely transfer and implementation of prophylactic therapies. We attempted to utilize machine learning (ML) using basic pre-labor clinical data and laboratory measurements to predict postpartum Hemoglobin (Hb) in non-complicated singleton pregnancies. The local databases of two academic care centers on patient delivery were incorporated into the current study. Patients with preexisting coagulopathy, traumatic cases, and allogenic blood transfusion were excluded from all analyses. The association of pre-delivery variables with 24-h post-delivery hemoglobin level was evaluated using feature selection with Elastic Net regression and Random Forest algorithms. A suite of ML algorithms was employed to predict post-delivery Hb levels. Out of 2051 pregnant women, 1974 were included in the final analysis. After data pre-processing and redundant variable removal, the top predictors selected via feature selection for predicting post-delivery Hb were parity (B: 0.09 [0.05– 0.12]), gestational age, pre-delivery hemoglobin (B:0.83 [0.80–0.85]) and fibrinogen levels (B:0.01 [0.01–0.01]), and pre-labor platelet count (B*1000: 0.77 [0.30–1.23]). Among the trained algorithms, artificial neural network provided the most accurate model (Root mean squared error: 0.62), which was subsequently deployed as a web-based calculator: https://predictivecalculators.shinyapps.io/ ANN-HB. The current study shows that ML models could be utilized as accurate predictors of indirect measures of PPH and can be readily incorporated into healthcare systems. Further studies with heterogenous population-based samples may further improve the generalizability of these models. Keywords Postpartum hemorrhage, Machine learning, Multilayer perceptron, Support vector machine, Extreme gradient boosting, Artificial intelligence Postpartum hemorrhage (PPH) is a critical global issue in obstetrics, representing the foremost cause of maternal morbidity and mortality worldwide, contributing to nearly one-third of deaths among pregnant and postpartum women. In the United States, PPH rates are on the rise, complicating almost 3% of d eliveries1. Recent decades have witnessed advancements in PPH treatment, including compression sutures2,3, and changes in fibrinogen and blood transfusion s trategies4,5. However, the limited availability of these advanced treatments in primary and secondary centers impedes widespread use, underscoring the pivotal role of timely intervention. Notwithstanding the utilization of advanced therapeutic modalities, postpartum hemorrhage (PPH) continues to exert a pivotal influence on maternal mortality rates. While maternal death is relatively rare in the United States, blood transfusion following hemorrhage, a condition 50 times more prevalent than mortality, is the primary diagnosis linked to severe maternal m orbidity6,7. The complexity of obstetric care settings, with varying levels of resources and expertise across different healthcare facilities, presents additional challenges in early identification and management of individuals at risk of requiring blood transfusion. In some cases, delayed 1 Student Research Committee, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran. 2Neuroscience Research Center, Iran University of Medical Sciences, Tehran, Iran. 3Department of Obstetrics and Gynecology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 4Yas University Hospital, Tehran University of Medical Sciences, Tehran, Iran. 5Department of Community Medicine, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran. 6Non‑Communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran. 7Men′s health and Reproductive health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. *email: ; Scientific Reports | (2024) 14:13953 | https://doi.org/10.1038/s41598-024-64278-z 1 Vol.:(0123456789) www.nature.com/scientificreports/ recognition of hemorrhage or insufficient access to timely interventions may exacerbate the need for blood transfusion and increase the risk of adverse maternal outcomes. This underscores the imperative need for the development of effective methodologies aimed at identifying high-risk patients. Predicting PPH before delivery is crucial for enhancing patient outcomes, enabling timely transfer to higher levels of care, advanced preparation, and implementation of prophylactic t herapies8. Despite historical studies on risk factors related to PPH, predicting the occurrence of PPH remains challenging. Risk factors such as abnormal placentation, placental abruption, severe preeclampsia, and intrauterine fetal demise have been i dentified9, but predicting a woman’s risk of PPH upon labor admission involves incorporating known risk factors and approximating the probability using a risk strata scheme. In addition, a significant portion of PPH cases involve patients lacking known risk factors, presenting a challenge for traditional models that often fall short in predicting such i nstances10,11. Some studies have developed PPH prediction models based on hemoglobin (Hb) levels and blood transfusion needs. Visual estimates of blood loss are deemed i naccurate12, and the gravimetric method for measuring blood loss has been validated in various s tudies13. Hb levels, especially concentrations below 80 g/L, appear to be a more accurate factor for evaluating and predicting PPH14. Current risk-based stratification guidelines endorsed by The American College of Obstetricians and Gynecologists (ACOG) and California Maternal Quality Care Collaborative (CMQCC) utilize decision tree algorithms based on clinical consensus, expert opinion, and prior observational d ata15–17. However, a validated clinical prediction model suitable for deployment on labor and delivery units for PPH is currently l acking18. Traditional statistical methods historically formed the basis for risk prediction. However, the current literature indicates a shift towards embracing machine learning (ML) driven by advanced computer algorithms, particularly for individuals lacking conventional risk factors19. ML models efficiently automate the processing of non-additive relationships and incorporating complex interaction between factors that otherwise require specialized statistical expertise and time-consuming exploratory data analysis. This holds promising potential for accurately identifying women at the highest risk of PPH, potentially improving obstetric decision-making and clinical o utcomes20–23. In thi (...truncated)