As subspecialists within anesthesiology, cardiothoracic anesthesiologists are trained to provide perioperative care during various types of cardiac surgery and organ transplants. They help keep patients safe during high-risk and exceedingly complex surgical procedures. The success of these interventions relies on a team of highly skilled clinicians with both technical expertise and an ability to dynamically address the unique challenges presented by each surgery.

“The aging population is bringing increasingly complex comorbidities to what are already incredibly complicated operations,” explains Louise Sun, MD, MS, chief of the Division of Cardiovascular and Thoracic Anesthesia at Stanford University School of Medicine. As part of their role, cardiothoracic anesthesiologists perform extensive preoperative evaluations to understand patient characteristics in order to optimize the care that they receive. This also includes providing postoperative intensive care in the cardiovascular intensive care unit (CVICU).

“We synthesize all of the information presented by each patient into a personalized plan that maximizes the success of the surgical team before, during, and after the operation,” she says.

Capitalizing on big data to improve healthcare delivery
Prior to her arrival at Stanford Health Care, Dr. Sun was a clinician research chair and Director of Bioinformatics Research at the University of Ottawa Heart Institute at the University of Ottawa, Ontario, Canada. These roles, along with cross-appointment as a faculty member at the Institute for Clinical and Evaluative Sciences (ICES), offered unique opportunities for big data and population health research. They also capitalized on her epidemiology and bioengineering background, as well as experience with informatics and computational approaches.

Dr. Sun’s population-based studies using ICES data offered novel perspectives on cardiac surgery and readmission rates, as well as the impact of sex differences on heart failure, access to care, and patient outcomes.^1-5 “I’m particularly passionate about finding ways to use the enormous volumes of medical information generated by health systems to develop predictive models capable of optimizing care delivery and efficiency,” Dr. Sun explains.

This process requires the clinical experience necessary to understand and identify the value of the raw data. Clinicians also need to determine how to effectively transform that data into something meaningful. In this regard, Dr. Sun acknowledges that having multiple skill sets helps her identify clinically relevant problems and quickly develop potential solutions.

“I’m fortunate that my clinical experience affords a degree of credibility for the models that we develop, which tends to streamline their entry into the clinical workflow,” she says. “An ability to explain how and why these models work also lends transparency to the process and allows a broader understanding of their benefit to both patients and clinicians.”

Predictive modeling to optimize patient outcomes
During the early COVID-19 pandemic, Dr. Sun began a study to address overcrowded ICUs. This research later expanded to address unnecessary CVICU occupancy resulting from extended lengths of stay (LOS) in the hospital following cardiac surgery. Prolonged LOS resulted in large patient waitlists for elective cardiac surgery procedures, with some of those patients ultimately dying or requiring hospitalization while on the list.

The large size and scope of the ICES data help create models that can demonstrate predictive accuracy across diverse patient populations. Using these data, Dr. Sun and her colleagues developed a set of new clinical risk score models. These models could support triage by more efficiently prioritizing patients according to their symptoms and medical profiles. Dr. Sun’s team showed that using this model could potentially streamline procedure scheduling, decrease health-related deterioration on the waitlist, and maximize the availability of both CVICU beds and clinical staff.^3,6-8

“My daily interactions with patients and staff provided the context necessary to understand which variables in the data would contribute most to the accuracy of the predictive model,” Dr. Sun explains. Although developed using data from Canada, the concept also has immediate applicability to stateside hospitals. “We’re currently attempting to validate these models at Stanford Health Care in order to address similar capacity challenges in the emergency department and CVICU.”

Using biomarkers to identify heart failure risk
Another active area of Dr. Sun’s research involves designating patients at high risk of right ventricular failure (RVF) following cardiac surgery. RVF can have devastating consequences in terms of postsurgery mortality and morbidity. Dr. Sun’s team identified biomarkers demonstrating both sensitivity and specificity for predicting RVF across different patient populations.⁹

“We found that changes in these biomarkers before, during, and after surgery offer reliable insight into a patient’s risk profile,” Dr. Sun explains. Observing certain biomarker levels at baseline can also help predict postsurgery RVF risk. “Because prevention is the best treatment in these cases, early identification of a potential problem allows us to intervene appropriately, with the goal of avoiding adverse events during and after the procedure.” Dr. Sun’s multidisciplinary team also published standardized definitions for RVF in the context of cardiac surgery to provide a uniform standard to evaluate clinical practice and conduct research studies.¹⁰

Enabling data-driven management of intraoperative blood pressure
Hypotension occurs in up to 99% of surgeries and is among the few modifiable risk factors for major operative morbidity and mortality. Dr. Sun’s team leveraged informatics techniques to identify critical thresholds and durations of intraoperative hypotension related to the development of important postoperative complications following cardiac and noncardiac surgery.

Their findings supported a personalized approach to defining blood pressure thresholds according to baseline risk factors presented by each individual patient. Importantly, the results showed that postsurgical risks, including death, stroke, acute kidney injury, and the need for renal replacement therapy, could be modified by precisely managing intraoperative blood pressure.^11-15   

Her research has enabled her to make wide-reaching contributions to perioperative safety.

As a member of the Anesthesia Patient Safety Foundation expert consensus group, Dr. Sun recently provided recommendations on the management of perioperative hemodynamic instability.¹⁶ Her team’s efforts have also led to patented, AI-driven technology supporting the prediction and prevention of hypotension in the operating room and ICU.

Driving emerging leaders and gender equity in cardiothoracic anesthesiology
Dr. Sun acknowledges Stanford Medicine’s track record of innovation and research excellence as a major motivation in her decision to move. She emphasizes that this position offered an unprecedented opportunity to mentor the next generation of leaders in the field. These include faculty engaged in paradigm-shifting research:        

• Kristen Rhee Steffner, MD, is developing deep learning-based approaches to evaluating and classifying transesophageal echocardiography images obtained during cardiac surgery.¹⁷
• Albert H. Tsai, MD, is evaluating the use of augmented reality as a medical simulation modality for applications in cardiovascular anesthesiology training.¹⁸
• Vikram Fielding-Singh, MD, is using population-based repositories to evaluate the outcomes of patients with pre-existing renal dysfunction.¹⁹

As one of the few female division chiefs of cardiothoracic anesthesiology in the nation, Dr. Sun notes that the high proportion of women in the division (55%) marks it as one of the most gender-balanced program of its kind in the nation. Yet as of 2021, only 26.1% of anesthesiologists in the United States are female.²⁰

The lack of female anesthesiologists nationwide has a significant impact on patient welfare. Decades of research offer evidence that physician-patient sex discordance can adversely affect patient surgical outcomes, particularly when the patient is female.²¹ Dr. Sun’s research has also demonstrated how surgeon-anesthesiologist sex discordance (mixed-sex teams) can improve outcomes in cardiac and noncardiac surgery settings.²²

“The gender equity in our division is certainly a point of emphasis during the recruitment of faculty and fellowship candidates,” explains Dr. Sun. “Because we operate in a setting where effective communication is a matter of life and death, fostering a culture of openness and inclusion is essential to delivering the best care possible.”  

Learn more about the Division of Cardiovascular and Thoracic Anesthesia and the spectrum of care offered by Stanford Health Care.

References:

1. Sun LY, Chu A, Tam DY, et al. Derivation and validation of predictive indices for cardiac readmission after coronary and valvular surgery - A multicenter study. Am Heart J Plus. 2023;28:100285. https://pubmed.ncbi.nlm.nih.gov/38511073/
2. Rubens FD, Clarke AE, Lee DS, Wells GA, Sun LY. Population study of sex-based outcomes after surgical aortic valve replacement. CJC Open. 2022;5(3):220-229.  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10066438/
3. Sun LY, Wijeysundera HC, Lee DS, et al. Derivation and validation of a clinical risk score to predict death among patients awaiting cardiac surgery in Ontario, Canada: A population-based study. CMAJ Open. 2022;10(1):E173-E182. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9259465/
4. Sun LY, Zghebi SS, Eddeen AB, et al. Derivation and external validation of a clinical model to predict heart failure onset in patients with incident diabetes. Diabetes Care. 2022;45(11):2737-2745. https://pubmed.ncbi.nlm.nih.gov/36107673/
5. Sun LY, Tu JV, Coutinho T, et al. Sex differences in outcomes of heart failure in an ambulatory, population-based cohort from 2009 to 2013. CMAJ. 2018;190(28):E848-E854. https://pubmed.ncbi.nlm.nih.gov/30012800/
6. Sun LY, Eddeen AB, Wijeysundera HC, Mamas MA, Tam DY, Mesana TG. Derivation and validation of a clinical model to predict death or cardiac hospitalizations while on the cardiac surgery waitlist. CMAJ. 2021;193(34):E1333-E1340. https://pubmed.ncbi.nlm.nih.gov/34462293/
7. Fottinger A, Eddeen AB, Lee DS, Woodward G, Sun LY. Derivation and validation of pragmatic clinical models to predict hospital length of stay after cardiac surgery in Ontario, Canada: a population-based cohort study. CMAJ Open. 2023;11(1):E180-E190. https://pubmed.ncbi.nlm.nih.gov/36854454/
8. Sun LY, Bader Eddeen A, Ruel M, MacPhee E, Mesana TG. Derivation and validation of a clinical model to predict intensive care unit length of stay after cardiac surgery. J Am Heart Assoc. 2020;9(21):e017847. https://pubmed.ncbi.nlm.nih.gov/32990156/
9. MacMillan YS, Mamas MA, Sun LY. IGFBP7 as a preoperative predictor of congestive acute kidney injury after cardiac surgery. Open Heart. 2022;9(1):e002027. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9226986/
10. Jabagi H, Nantsios A, Ruel M, Mielniczuk LM, Denault AY, Sun LY. A standardized definition for right ventricular failure in cardiac surgery patients. ESC Heart Fail. 2022;9(3):1542-1552. https://pubmed.ncbi.nlm.nih.gov/35266332/
11. Sun LY, Chung AM, Farkouh ME, et al. Defining an intraoperative hypotension threshold in association with stroke in cardiac surgery. Anesthesiology. 2018;129(3):440-447. https://pubmed.ncbi.nlm.nih.gov/29889106/
12. Sun LY, Wijeysundera DN, Tait GA, Beattie WS. Association of intraoperative hypotension with acute kidney injury after elective noncardiac surgery. Anesthesiology. 2015;123(3):515-523. https://pubmed.ncbi.nlm.nih.gov/26181335/
13. Ngu JMC, Jabagi H, Chung AM, Sun LY. Defining an intraoperative hypotension threshold in association with de novo renal replacement therapy after cardiac surgery. Anesthesiology. 2020;132(6):1447-1457. https://pubmed.ncbi.nlm.nih.gov/32205546/
14. Sun LY. Preoperative risk, blood pressure, and acute kidney injury. Anesthesiology. 2020;132(3):416-417. https://pubmed.ncbi.nlm.nih.gov/31929330/
15. Ristovic V, de Roock S, Mesana TG, van Diepen S, Sun LY. The impact of preoperative risk on the association between hypotension and mortality after cardiac surgery: An observational study. J Clin Med. 2020;9(7):2057. https://pubmed.ncbi.nlm.nih.gov/32629948/
16. Scott MJ; APSF Hemodynamic Instability Writing Group. Perioperative patients with hemodynamic instability: Consensus recommendations of the Anesthesia Patient Safety Foundation. Anesth Analg. 2024;138(4):713-724. https://pubmed.ncbi.nlm.nih.gov/38153876/
17. Steffner KR, Christensen M, Gill G, et al. Deep learning for transesophageal echocardiography view classification. Sci Rep. 2024;14(1):11. https://pubmed.ncbi.nlm.nih.gov/38167849/
18. Tsai A, Bodmer N, Hong T, et al. Participant perceptions of augmented reality simulation for cardiac anesthesiology training: A prospective, mixed-methods study. J Educ Perioper Med. 2023;25(3):E712. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10502607/
19. Fielding-Singh V, Vanneman MW, Lin E. Preoperative hemodialysis timing and postoperative mortality in patients with end-stage kidney disease-reply. JAMA. 2023;329(11):939-940. https://pubmed.ncbi.nlm.nih.gov/36943217/
20. Active Physicians by Sex and Specialty, 2021. Association of American Medical Colleges; 2021. https://www.aamc.org/data-reports/workforce/data/active-physicians-sex-specialty-2021
21. Wallis, CJD, Jerath, A, Coburn N, et al. Association of surgeon-patient sex concordance with postoperative outcomes. JAMA Surg. 2022;157(2):146-156. https://jamanetwork.com/journals/jamasurgery/fullarticle/2786671
22. Etherington C, Boet S, Chen I, Sun LY. Association between surgeon/anesthesiologist sex discordance and 1-year mortality among adults undergoing noncardiac surgery: A population-based retrospective cohort study. Ann Surg. 2024;279(4):563-568. https://pubmed.ncbi.nlm.nih.gov/37791498/
23. Sun LY, Boet S, Chan V, Lee DS, Mesana TG, Bader Eddeen A, Etherington C. Impact of surgeon and anaesthesiologist sex on patient outcomes after cardiac surgery: a population-based study. BMJ Open. 2021 Aug 25;11(8):e051192. doi: 10.1136/bmjopen-2021-051192.