Is Artificial Intelligence Going to Predict Patient Blood Management in Cardiac Critical Care in 2026?

INTRODUCTION: FROM PROTOCOLS TO PREDICTION

Patient blood management (PBM) in cardiac critical care has traditionally been a protocol-driven discipline, anchored in transfusion thresholds, coagulation algorithms, and clinician judgment. Yet, as we approach 2026, the question is no longer whether artificial intelligence (AI) will influence PBM, but whether it will fundamentally predict and personalize it.

Over the past decade, cardiac critical care has evolved from reactive transfusion practices to proactive conservation strategies. Today, AI introduces a third paradigm: Anticipatory PBM, where decisions are guided by probabilistic forecasts rather than static thresholds.

THE EMERGENCE OF PREDICTIVE INTELLIGENCE IN CARDIAC CARE

Recent advances in machine learning and clinical informatics have demonstrated that AI can reliably predict outcomes in cardiac surgery and intensive care unit (ICU) settings,including mortality, complications, and resource utilization. AI models are now capable of predicting perioperative bleeding risk, Forecasting transfusion requirements, and identifying coagulopathy patterns before clinical manifestation.

A landmark ICU-based model demonstrated the ability to predict transfusion needs within 24 h with high accuracy (Area Under the Receiver Operating Characteristic Curve ~0.97), signalling a paradigm shift from reactive transfusion to pre-emptive optimization. Simultaneously, AI-driven systems in cardiothoracic surgery can anticipate complications such as postoperative bleeding and arrhythmias, both key drivers of transfusion decisions.

AI AND THE NEW ARCHITECTURE OF PBM

AI is not merely adding efficiency, it is redefining the architecture of PBM into three predictive layers [Table 1].

Emerging literature suggests that AI enables earlier anemia detection and improved resource allocation, directly strengthening PBM pathways. The future operating room will likely feature closed-loop decision support, where AI continuously recalibrates transfusion strategies in real time. Machine learning models using multimodal ICU data (vitals, laboratories, waveforms) are already improving the prediction of clinical deterioration and intervention needs.

FROM DECISION SUPPORT TO AUTONOMOUS SYSTEMS

Perhaps the most transformative development is the emergence of closed-loop and “self-driving” critical care systems. Early prototypes of autonomous cardiovascular support systems, integrating AI with real-time physiological feedback, suggest a future where therapies are titrated continuously without human delay. In the context of PBM, this could mean automated transfusion triggers based on predictive thresholds, Real-time coagulation correction and personalized hemoglobin targets based on oxygen delivery modeling.

DIGITAL TWINS AND THE PERSONALIZATION OF BLOOD MANAGEMENT

The concept of the “digital twin,” a virtual replica of the patient, may become the cornerstone of PBM by 2026. By simulating multiple clinical scenarios, AI can predict the impact of transfusion versus non-transfusion strategies, optimize individualized hemoglobin targets and balance oxygen delivery, viscosity, and microcirculatory flow. Such approaches promise to move PBM from population-based guidelines to patient-specific physiology.

LIMITATIONS: WHY 2026 IS A TRANSITION, NOT A DESTINATION

Despite rapid progress, several barriers remain [Table 2].

THE ROLE OF THE CLINICIAN IN THE AI ERA

AI will not replace the cardiac intensivist, it will redefine the role. The future clinician will interpret AI predictions rather than generate them, Override algorithms when clinical nuance demands it and Integrate machine intelligence with human judgment. In this sense, AI becomes a cognitive partner, not a replacement.

PREDICTION IS INEVITABLE - AUTONOMY IS OPTIONAL

By 2026:

• AI will reliably predict transfusion needs and bleeding risk:

  Transfusion in bleeding (2026) is restrictive and goal-directed: RBCs are usually given when Hb <7–8 g/dL, platelets <50,000/µL (higher in central nervous system bleeding), and fibrinogen <1.5–2 g/L. Bleeding risk is assessed using clinical status plus viscoelastic tests like thromboelastography (TEG)/rotational thromboelastometry (ROTEM). Current practice favors early fibrinogen correction, tranexamic acid use, and factor concentrates over empirical plasma. Minimize transfusion while correcting coagulopathy precisely and avoiding thrombosis.

• It will augment PBM decision-making in advanced centers

  In advanced centers (2026), PBM is augmented by real-time, data-driven decisions using tools like TEG and ROTEM. AI-integrated systems predict bleeding and transfusion needs, early, enabling personalized, goal-directed therapy. There is increased use of factor concentrates and restrictive transfusion thresholds, reducing unnecessary blood use. Optimize patient outcomes while minimizing transfusion-related risks and costs.

• It will begin to enable personalized, anticipatory blood management

  Advanced PBM now enables personalized, anticipatory blood management by combining continuous monitoring with tools like TEG and ROTEM. AI-driven prediction models identify patients at risk of bleeding or massive transfusion before clinical deterioration. This allows pre-emptive correction of coagulopathy (fibrinogen, factors, platelets) rather than reactive transfusion. However, pre-emptive correction of hemostasis abnormalities has not been shown to reduce transfusion requirements and to improve patients’ outcomes. It may even result in inappropriate blood transfusion and subsequent adverse events. Whether AI-driven personalized, anticipatory PBM results in safer, targeted therapy with reduced blood product exposure and improved outcomes must be shown in future trials.

Collectively, the contemporary body of work reflects a fundamental evolution in our understanding of bleeding and coagulation management, one that moves decisively away from empiricism toward precision, personalization, and real-time, data-driven decision-making. The implementation of PBM programs, although operationally demanding, has consistently translated into improved clinical outcomes while simultaneously optimizing the use of limited blood resources.^[1-4] This reinforces the concept that transfusion should no longer be viewed as a default response to bleeding, but rather as a carefully titrated intervention within a broader, physiology-guided framework.

At the same time, conventional massive transfusion protocols are increasingly being challenged by targeted, viscoelastic-guided strategies. These approaches allow for early identification of specific coagulation derangements and enable clinicians to intervene with focused therapy, thereby reducing unnecessary exposure to allogeneic blood products and their associated risks.^[5]The integration of point-of-care algorithms into clinical practice, particularly in high-risk populations such as pediatric cardiac surgery and cyanotic heart disease, has further demonstrated that structured, algorithm-driven care can significantly enhance hemostatic control and improve patient outcomes.^[6-9]

Importantly, advances in transfusion medicine continue to emphasize the principle of “less is more,” advocating for minimization of allogeneic exposure through evidence-based, protocolized management pathways.^[10] This paradigm becomes especially critical in complex clinical scenarios such as peripartum cardiomyopathy and liver transplantation, where the interplay between bleeding and thrombosis is highly dynamic and often unpredictable. In these settings, viscoelastic hemostatic testing provides a unique window into the patient’s coagulation status, facilitating the detection of both hypocoagulable and hypercoagulable states and enabling timely, targeted intervention.^[11,12]

Emerging meta-analytic data further support the clinical relevance of these approaches, suggesting that the use of viscoelastic testing may contribute not only to reduced transfusion requirements but also to improved survival in high-acuity settings, including cardiovascular surgery, lung transplantation, and extracorporeal life support.^[13,14]

Taken together, these insights underscore the necessity of a global transition toward standardized yet flexible hemostatic management strategies, frameworks that integrate advanced diagnostics, multidisciplinary expertise, and continuous quality improvement. Such an approach represents not merely an incremental advancement, but a paradigm shift in the way we understand and manage bleeding in modern perioperative and critical care medicine.^[15]

CONCLUSION

AI is poised to transform PBM in cardiac critical care from a reactive, protocol-driven practice to a predictive, personalized, and anticipatory model. By integrating real-time clinical data with advanced monitoring tools such as TEG and ROTEM, PBM will increasingly enable early identification of bleeding risk and targeted correction of coagulopathy. However, despite these advances, full autonomy remains unlikely in the near future; human oversight and clinical judgment will continue to be central. Here, it is important to interpret TEG/ROTEM results in the right clinical context and phase of cardiac surgery. The future of PBM will therefore exist as a hybrid model, where predictive intelligence augments, rather than replaces, clinician decision-making, ultimately aiming to deliver safer, more precise, and outcome-driven transfusion strategies.