摘要Background:Sepsis-induced coagulopathy(SIC)is a life-threatening complication of sepsis characterized by dysregulated coagulation,hyperinflammation,and microvascular thrombosis.Despite advancements in its pathophysiology,therapeutic strategies remain controversial,and clinical trials have yielded inconsistent outcomes.Methods:This study conducts a bibliometric analysis(1995-2024)to map research trends,identify knowledge gaps,and evaluate the translational challenges in SIC management.A systematic search of the Web of Science Core Collection(6382 articles)and Scopus(8423 articles)retrieved on sepsis-related coagulation dysfunction.VOSviewer and Bibliometrix analyzed publication trends,citation networks,international collaborations,and keyword co-occurrence.Metrics included annual growth rates,total link strength,and relative research interest.We visualized temporal and thematic trends to highlight emerging frontiers and interdisciplinary linkages.Results:Global research output exhibited exponential growth(annual rate:18.4％),peaking during the coronavirus disease 2019 pandemic.The United States and China dominated research contributions,with the University of Texas MD Anderson Cancer Center leading in citation impact.Van Der Poll,Tom(Netherlands),and Toshiaki Iba(Japan)emerged as pivotal figures,focusing on molecular mechanisms and diagnostic standardization,respectively.Keyword clustering revealed 4 pillars:(1)etiology and clinical management,(2)molecular mechanisms,(3)biomarkers and prognostics,and(4)pathophysiology and syndromes.Coronavirus disease 2019-associated coagulopathy and artificial intelligence-driven diagnostics emerged as recent hotspots.Conclusions:This analysis reveals the exponential but heterogeneous expansion of SIC research,driven by mechanistic discoveries and pandemic-related demands.Persistent challenges include the standardization of diagnostic criteria,patient heterogeneity in clinical trials,and geographic disparities in research capacity.Future priorities include integrating precision medicine and adopting artificial intelligence for patient stratification.Bridging mechanistic insights with clinical translation will be critical to improving outcomes in SIC.