Advanced artificial intelligence-guided hemodynamic management within cardiac enhanced recovery after surgery pathways: A multi-institution review

The Emerging and Important Role of Artificial Intelligence in Cardiac Surgery

Artificial Intelligence (AI) has greatly affected our everyday lives and holds great promise to change the landscape of medicine. AI is particularly positioned to improve care for the increasingly complex patients undergoing cardiac surgery using the immense amount of data generated in the course of their care. When deployed, AI can be used to analyze this information at the patient's bedside more expediently and accurately, all while providing new insights. This review summarizes the current applications of AI in cardiac surgery from the vantage point of a patient's journey. Applications of AI include preoperative risk assessment, intraoperative planning, postoperative patient care, and outpatient telemonitoring, encompassing the spectrum of cardiac surgical care. Offloading of administrative processes and enhanced experience with information gathering also represent a unique and under-represented avenue for future use of AI. As clinicians, understanding the nomenclature and applications of AI is important to contextualize issues, to ensure problem-driven solutions, and for clinical benefit. Precision medicine, and thus clinically relevant AI, remains dependent on data curation and warehousing to gather insights from large multicentre repositories while treating privacy with the utmost importance. AI tasks should not be siloed but rather holistically integrated into clinical workflow to retain context and relevance. As cardiac surgeons, AI allows us to look forward to a bright future of more efficient use of our clinical expertise toward high-level decision making and technical prowess.

The premise, promise, and perils of artificial intelligence in critical care cardiology

Artificial intelligence (AI) is an emerging technology with numerous healthcare applications. AI could prove particularly useful in the cardiac intensive care unit (CICU) where its capacity to analyze large datasets in real-time would assist clinicians in making more informed decisions. This systematic review aimed to explore current research on AI as it pertains to the CICU. A PRISMA search strategy was carried out to identify the pertinent literature on topics including vascular access, heart failure care, circulatory support, cardiogenic shock, ultrasound, and mechanical ventilation. Thirty-eight studies were included. Although AI is still in its early stages of development, this review illustrates its potential to yield numerous benefits in the CICU.

AcumenTM hypotension prediction index guidance for prevention and treatment of hypotension in noncardiac surgery: a prospective, single-arm, multicenter trial

BACKGROUND

Intraoperative hypotension is common during noncardiac surgery and is associated with postoperative myocardial infarction, acute kidney injury, stroke, and severe infection. The Hypotension Prediction Index software is an algorithm based on arterial waveform analysis that alerts clinicians of the patient's likelihood of experiencing a future hypotensive event, defined as mean arterial pressure < 65 mmHg for at least 1 min.

METHODS

Two analyses included (1) a prospective, single-arm trial, with continuous blood pressure measurements from study monitors, compared to a historical comparison cohort. (2) A post hoc analysis of a subset of trial participants versus a propensity score-weighted contemporaneous comparison group, using external data from the Multicenter Perioperative Outcomes Group (MPOG). The trial included 485 subjects in 11 sites; 406 were in the final effectiveness analysis. The post hoc analysis included 457 trial participants and 15,796 comparison patients. Patients were eligible if aged 18 years or older, American Society of Anesthesiologists (ASA) physical status 3 or 4, and scheduled for moderate- to high-risk noncardiac surgery expected to last at least 3 h.

MEASUREMENTS

minutes of mean arterial pressure (MAP) below 65 mmHg and area under MAP < 65 mmHg.

RESULTS

Analysis 1: Trial subjects (n = 406) experienced a mean of 9 ± 13 min of MAP below 65 mmHg, compared with the MPOG historical control mean of 25 ± 41 min, a 65% reduction (p < 0.001). Subjects with at least one episode of hypotension (n = 293) had a mean of 12 ± 14 min of MAP below 65 mmHg compared with the MPOG historical control mean of 28 ± 43 min, a 58% reduction (p< 0.001). Analysis 2: In the post hoc inverse probability treatment weighting model, patients in the trial demonstrated a 35% reduction in minutes of hypotension compared to a contemporaneous comparison group [exponentiated coefficient: - 0.35 (95%CI - 0.43, - 0.27); p < 0.001].

CONCLUSIONS

The use of prediction software for blood pressure management was associated with a clinically meaningful reduction in the duration of intraoperative hypotension. Further studies must investigate whether predictive algorithms to prevent hypotension can reduce adverse outcomes.

TRIAL REGISTRATION

Clinical trial number: NCT03805217. Registry URL: https://clinicaltrials.gov/ct2/show/NCT03805217 . Principal investigator: Xiaodong Bao, MD, PhD. Date of registration: January 15, 2019.