Comparison of Machine Learning Models Including Preoperative, Intraoperative, and Postoperative Data and Mortality After Cardiac Surgery

Importance: A variety of perioperative risk factors are associated with postoperative mortality risk. However, the relative contribution of routinely collected intraoperative clinical parameters to short-term and long-term mortality remains understudied. Objective: To examine the performance of multiple machine learning models with data from different perioperative periods to predict 30-day, 1-year, and 5-year mortality and investigate factors that contribute to these predictions. Design, Setting, and Participants: In this prognostic study using prospectively collected data, risk prediction models were developed for short-term and long-term mortality after cardiac surgery. Included participants were adult patients undergoing a first-time valve operation, coronary artery bypass grafting, or a combination of both between 1997 and 2017 in a single center, the University Medical Centre Groningen in the Netherlands. Mortality data were obtained in November 2017. Data analysis took place between February 2020 and August 2021. Exposure: Cardiac surgery. Main Outcomes and Measures: Postoperative mortality rates at 30 days, 1 year, and 5 years were the primary outcomes. The area under the receiver operating characteristic curve (AUROC) was used to assess discrimination. The contribution of all preoperative, intraoperative hemodynamic and temperature, and postoperative factors to mortality was investigated using Shapley additive explanations (SHAP) values. Results: Data from 9415 patients who underwent cardiac surgery (median [IQR] age, 68 [60-74] years; 2554 [27.1%] women) were included. Overall mortality rates at 30 days, 1 year, and 5 years were 268 patients (2.8%), 420 patients (4.5%), and 612 patients (6.5%), respectively. Models including preoperative, intraoperative, and postoperative data achieved AUROC values of 0.82 (95% CI, 0.78-0.86), 0.81 (95% CI, 0.77-0.85), and 0.80 (95% CI, 0.75-0.84) for 30-day, 1-year, and 5-year mortality, respectively. Models including only postoperative data performed similarly (30 days: 0.78 [95% CI, 0.73-0.82]; 1 year: 0.79 [95% CI, 0.74-0.83]; 5 years: 0.77 [95% CI, 0.73-0.82]). However, models based on all perioperative data provided less clinically usable predictions, with lower detection rates; for example, postoperative models identified a high-risk group with a 2.8-fold increase in risk for 5-year mortality (4.1 [95% CI, 3.3-5.1]) vs an increase of 11.3 (95% CI, 6.8-18.7) for the high-risk group identified by the full perioperative model. Postoperative markers associated with metabolic dysfunction and decreased kidney function were the main factors contributing to mortality risk. Conclusions and Relevance: This study found that the addition of continuous intraoperative hemodynamic and temperature data to postoperative data was not associated with improved machine learning-based identification of patients at increased risk of short-term and long-term mortality after cardiac operations

Mitral Valve Coaptation Reserve Index:A Model to Localize Individual Resistance to Mitral Regurgitation Caused by Annular Dilation

Objectives: The objective of this study was to develop a mathematical model for mitral annular dilatation simulation and determine its effects on the individualized mitral valve (MV) coaptation reserve index (CRI). Design: A retrospective analysis of intraoperative transesophageal 3-dimensionalechocardiographic MV datasets was performed. A mathematical model was created to assess the mitral CRI for each leaflet segment (A1-P1, A2-P2, A3-P3). Mitral CRI was defined as the ratio between the coaptation reserve (measured coaptation length along the closure line) and an individualized correction factor. Indexing was chosen to correct for MV sphericity and area of largest valve opening. Mathematical models were created to simulate progressive mitral annular dilatation and to predict the effect on the individual mitral CRI. Setting: At a single-center academic hospital. Participants: Twenty-five patients with normally functioning MVs undergoing cardiac surgery. Interventions: None. Measurements and Main Results: Direct measurement of leaflet coaptation along the closure line showed the lowest amount of coaptation (reserve) near the commissures (A1-P1 0.21 ± 0.05 cm and A3-P3 0.22 ± 0.06 cm), and the highest amount of coaptation (reserve) at region A2 to P2 0.25 ± 0.06 cm. After indexing, the A2-to-P2 region was the area with the lowest CRI in the majority of patients, and also the area with the least resistance to mitral regurgitation (MR) occurrence after simulation of progressive annular dilation. Conclusions: Quantification and indexing of mitral coaptation reserve along the closure line are feasible. Indexing and mathematical simulation of progressive annular dilatation consistently showed that indexed coaptation reserve was lowest in the A2-to-P2 region. These results may explain why this area is prone to lose coaptation and is often affected in MR

Individualised perioperative blood pressure and fluid therapy in oesophagectomy:study protocol for a randomised clinical trial

INTRODUCTION: Oesophagectomy is the mainstay of curative treatment for oesophageal cancer, but it is associated with a high risk of major complications. Goal-directed fluid therapy and individualised blood pressure management may prevent complications after surgery. Extending goal-directed fluid therapy after surgery and applying an individual blood pressure target may have substantial benefit in oesophagectomy. This is a protocol for a clinical trial implementing a novel haemodynamic protocol from the start of anaesthesia to the next day with the patient’s own night-time blood pressure as the lower threshold.METHODS: This is a single-centre, single-blind, randomised, clinical trial. Oesophagectomy patients are randomised 1:1 for either perioperative haemodynamic management according to a goal-directed fluid therapy protocol with an individual target blood pressure or for standard care. The primary endpoint is the total burden of morbidity and mortality assessed by the Comprehensive Complication Index 30 days after surgery. Secondary endpoints are complications, reoperations, fluid and vasopressor dosage and quality of life at 90 days after surgery.CONCLUSIONS: The results from this trial provide an objective and easy-to-follow algorithm for fluid administration, which may improve patient-centred outcomes in oesophagectomy patients.</p

Distribution of Cardioembolic Stroke: A Cohort Study

Background: A cardiac origin in ischemic stroke is more frequent than previously assumed, but it is not clear which patients benefit from cardiac work-up if obvious cardiac pathology is absent. We hypothesized that thromboembolic stroke with a cardiac source occurs more frequently in the posterior circulation compared with thromboembolic stroke of another etiology. Methods: We performed a multicenter observational study in 3,311 consecutive patients with ischemic stroke who were enrolled in an ongoing prospective stroke registry of 8 University hospitals between September 2009 and November 2014 in The Netherlands. In thi

Monitoring of the Sublingual Microcirculation During Cardiac Surgery:Current Knowledge and Future Directions

Handheld vital microscopes allow for direct observation of the sublingual microcirculatory perfusion during cardiac surgery. Through the use of handheld vital microscopes, it has been shown that cardiac surgery with cardiopulmonary bypass is associated with reduced and heterogenous microcirculatory perfusion. Microcirculatory impairment can result in inadequate tissue perfusion, leading to perioperative complications and poor outcome. Because microcirculatory impairment can occur despite stable or improved global hemodynamics, there is a yet unmet need for specific monitoring of the microcirculation. Technological advancements may facilitate point-of-care monitoring of microcirculatory perfusion using automated real-time analysis of microcirculatory measurements. Thus, microcirculatory monitoring may create new opportunities for specific microcirculatory treatment as part of hemodynamic management. The implementation of microcirculatory variables into personalized treatment concepts has the potential to improve hemodynamic management during cardiac surgery and thereby improve patient outcomes. Therefore, specific treatment strategies need to be developed to prevent or treat alterations of the microcirculatory perfusion. In the future, the use of handheld vital microscopes for microcirculatory monitoring may help to improve hemodynamic management and outcomes for patients undergoing cardiac surgical procedures