Implications of artificial intelligence for hemodynamic monitoring

In many medical settings, including operating rooms, critical care units, and interventional procedures, hemodynamic monitoring is essential for patient care. It entails the ongoing evaluation and monitoring of cardiovascular parameters in order to comprehend the dynamics of blood circulation and guarantee ideal organ perfusion. Artificial intelligence (AI) has the ability to completely transform hemodynamic monitoring by providing real-time insights, eliminating the drawbacks of conventional methods, and improving patient outcomes. A narrative review that was recently published delves into the progressive function of artificial intelligence (AI) in the realm of hemodynamic monitoring, with a particular focus on its capacity to deliver a paradigm shift in patient care.

Non-invasive cardiac output monitoring (NOM) might become more accurate and reliable with the use of artificial intelligence (AI). Complex data from a variety of non-invasive sensors may be processed by machine learning algorithms, which enhances the accuracy of cardiac output estimations and offers insightful information for clinical decision-making. The objective of this narrative review is to examine and evaluate the developing role of artificial intelligence (AI) in hemodynamic monitoring, highlighting developments, difficulties, and possible consequences for clinical practice.

Due to limitations in current diagnostic and prognostic tools, physicians need assistance in properly predicting the development of circulatory shock and selecting the best treatment regimens for particular patients. Comprehending the physiological principles behind circulatory shock is essential for directing suitable treatment measures. The reactive nature of today's hypotension care, which depends on tracking patterns of falling blood pressure, often causes therapies to be delayed. Research has shown a clear link between higher death rates in non-cardiac patient groups and the length of time when mean arterial pressure (MAP) is below 65 mm Hg.

Artificial intelligence has the potential to expedite, bolster, and simplify the process of treatment planning in order to lower mortality and prevent shock-related irreversible repercussions. The hypotension prediction index (HPI), which employs a logistic regression model to forecast hypotension several minutes prior to a decrease in blood pressure, is one use of hemodynamic monitoring. Using the AcumenTM Hypotension Prediction Index software, the EU HYPROTECT Registry observed 749 patients having major non-cardiac surgery. The results showed a significantly low median time-weighted average MAP of <65 mmHg (0.03 mmHg).

Alarm fatigue is a condition in which healthcare personnel become insensitive to safety signals and neglect to react correctly. Artificial intelligence (AI) has the potential to prevent this from happening. AI may lessen alarm fatigue by offering real-time monitoring, which often includes alert systems that are set off by predetermined criteria. The technology instantly notifies healthcare practitioners when certain haemodynamic values depart from the usual range. For time-sensitive actions in critical care circumstances, this proactive notification system is essential.

Artificial Intelligence is also applicable to early warning systems, such CircEWS and CircEWS-lite, which notify physicians of impending circulatory failure events within eight hours. These methods create continuous risk ratings with 90% prediction accuracy for circulatory failure events based on patient data from the High Time Resolution ICU dataset.

Artificial neural networks (ANN) are used to generate rules for patients experiencing septic shock, and fuzzy decision support systems (DDS) are used to manage patients in post-surgical cardiac critical care. These models have been developed for post-cardiac surgery and septic shock. With an ANN-based septic shock diagnosis system, machine learning techniques have also been used to forecast sepsis-related death.

In critical care settings, AI-assisted ultrasonography has shown promise in increasing accuracy and reducing unpredictability, which may make it easier to assess how treatments are affecting hemodynamic responses. In order to rectify values in a closed-loop system, closed-loop systems that combine treatment and monitoring of many systems have been created. These systems include blood pressure control, fluid delivery based on dynamic predictors of fluid responsiveness, closed-loop control of blood glucose levels, and the use of electroencephalograms for propofol administration.

Regular titration, regular monitoring, and protocol adherence may all help to reduce errors, improve safety, lighten the effort for nurses and staff, and shorten hospital stays and death rates. But in order to create and test clinically useful sensors, create clinically validated management algorithms, validate the whole closed-loop system, and show the usefulness of these closed-loop systems in clinical trials, physicians must first choose the parameters of interest.

With a range of advantages and particular difficulties, the incorporation of artificial intelligence (AI) into hemodynamic monitoring represents a revolutionary frontier in healthcare. The state of the art illustrates how AI has the ability to transform the accuracy and individualization of hemodynamic evaluations, giving physicians timely insights and improving patient outcomes. In the future, physiological data and information from electronic medical records (EMR) will be integrated by monitors, which will utilise bioinformatics to detect patterns in illness, forecast occurrences, choose the best course of treatment, and assist in prognostication. Intelligent monitoring will eliminate needless alerts, assist physicians in making decisions, and free them up to concentrate on the patient. Closed-loop systems will automate the integration of monitoring and treatment, improving adherence to guidelines, removing the possibility of human mistake, improving patient safety, and producing superior results. To fully use AI in this field, however, issues like data quality, model interpretability, and smooth integration into current healthcare infrastructures must be carefully resolved.

It is necessary to address ethical issues such as patient privacy, consent, and the proper management of private medical data. To guarantee their dependability and generalizability, AI models created for hemodynamic monitoring must undergo extensive validation across a range of patient groups and healthcare environments. With continuous advancements in real-time adaptive models, tailored monitoring strategies, and the integration of wearable devices, the field of artificial intelligence in hemodynamic monitoring has a lot of potential for the future. These developments point to a path towards more effective and patient-centered treatment.

We must strike a balance between the strength of AI and the critical role that healthcare professionals play as we manage these breakthroughs. Successful integration depends on human-machine cooperation, where AI enhances clinical judgement rather than takes its place. The ethical implications of data security and privacy also need close attention, highlighting the significance of well-defined rules and guidelines.

In summary, the use of AI to hemodynamic monitoring is a revolutionary development in healthcare that poses both special problems and a range of advantages. In order to prevent treatment of the illness and patient safety from being jeopardised by patient outliers or system issues, clinicians must continue to use their clinical acumen.

Reference –

Myatra, Sheila N.; Jagiasi, Bharat G.1; Singh, Neeraj P.; Divatia, Jigeeshu V.2. Role of artificial intelligence in haemodynamic monitoring. Indian Journal of Anaesthesia 68(1):p 93-99, January 2024. | DOI: 10.4103/ija.ija_1260_23.