scholarly journals Role of Intraoperative Transesophageal Echocardiography in Cardiac Surgery: an Observational Study

2019 ◽  
Vol 7 (15) ◽  
pp. 2480-2483
Author(s):  
Mona Elsherbiny ◽  
Yaser Abdelwahab ◽  
Kareem Nagy ◽  
Asser Mannaa ◽  
Yasmin Hassabelnaby
Keyword(s):  
Decision Making ◽  
Cardiac Surgery ◽  
Transesophageal Echocardiography ◽  
Clinical Decision Making ◽  
Clinical Investigation ◽  
Clinical Decision ◽  
Surgical Decision ◽  
Intraoperative Transesophageal Echocardiography ◽  
Before And After ◽  
Cardiothoracic Surgeon

AIM: This study is based on the hypothesis that the routine use of transesophageal echocardiography in cardiac surgery will influence the surgical decision taken by the surgeon intra-operatively in Kasr-Alainy hospitals. METHODS: Patients were examined with intraoperative transesophageal echocardiography (TEE) before and after cardiopulmonary bypass. Complete and comprehensive intraoperative TEE examinations will be performed by TEE certified cardiac anesthesiologists. Data that will be collected from the intraoperative examination and will be compared with preoperative transthoracic echocardiography, and the surgical decision that was taken preoperatively will be revised again with the cardiothoracic surgeon before the start of surgery. Also, TEE will be used again after weaning from bypass for revision and assessment of our decision. RESULTS: We examined the utility of TEE in 100 patients undergoing different types of cardiac procedures in Kasr Al-Ainy hospital. This prospective clinical investigation found that the pre- and post-CPB TEE examinations influenced surgical decision making in 10% of all evaluated patients. CONCLUSION: Intraoperative TEE has the potential to influence clinical decision making for cardiac surgical patients significantly. It is useful in surgical planning, guiding various hemodynamic interventions, and assessing the immediate results of surgery. Thus, IOTEE should be used routinely in all patients undergoing all types of cardiac surgeries.

scholarly journals Routine Intraoperative Transesophageal Echocardiography: Impact on Intraoperative Surgical Decision Making, a Single Center Interim Analysis

2013 ◽  
Vol 1 (1) ◽  
pp. 16-20 ◽  
Author(s):  
Ashok Kumar Badamali ◽  
J Sethu Madhavan ◽  
BPS Ghuman ◽  
S Subash ◽  
Abhi Mishra ◽  
...  
Keyword(s):  
Decision Making ◽  
Cardiac Surgery ◽  
Transesophageal Echocardiography ◽  
Interim Analysis ◽  
Single Center ◽  
Surgical Decision ◽  
New Findings ◽  
Intraoperative Transesophageal Echocardiography ◽  
Near Future ◽  
Surgical Decision Making

ABSTRACT Transesophageal echocardiography (TEE) has become an important part of armamentarium for anesthesiologists in the management of patients undergoing cardiac surgery. Many studies have demonstrated the safety and utility of TEE in cardiac surgery. With advances in hardware and software, easy availability of resources for learning and optimal understanding of image generation and interpretation, many new findings crop up in the operating room (OR) which may have been missed in preoperative transthoracic echocardiography (TTE), leading to necessary changes in planned surgical procedure. In our retrospective analysis of 726 cases in which TEE was performed over the last 1 year, changes in decision was made in 65 (8.9%) of cases. This included 42 unanticipated findings prior to cardiopulmonary bypass and 23 new findings after CPB, requiring revision in 15 cases. With the increasing use and further impending advances of TEE, the number of cases in which surgical decision will be altered may increase in near future. How to cite this article Badamali AK, Madhavan JS, Ghuman BPS, Subash S, Raj R, Mishra A, Mishra A, Arya VK, Kumar B, Jayant A, Shyam KST, Rana SS, Singh H, Mishra A, Kuthe S, Mahajan S, Prasad S, Mathew S, Arora I, Puri GD. Routine Intraoperative Transesophageal Echocardiography: Impact on Intraoperative Surgical Decision Making, a Single Center Interim Analysis. J Perioper Echocardiogr 2013;1(1):16-20.

Cardiac Surgery in Trisomy 13 and 18: A Guide to Clinical Decision-Making

2020 ◽  
Vol 41 (7) ◽  
pp. 1319-1333 ◽  
Author(s):  
Horacio G. Carvajal ◽  
Connor P. Callahan ◽  
Jacob R. Miller ◽  
Bethany L. Rensink ◽  
Pirooz Eghtesady
Keyword(s):  
Decision Making ◽  
Cardiac Surgery ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Trisomy 13

Transesophageal echocardiography in the awake elderly patient: Its role in the clinical decision-making process

1990 ◽  
Vol 120 (5) ◽  
pp. 1147-1153 ◽  
Author(s):  
Miguel Zabalgoitia ◽  
Dipeshkumar K. Gandhi ◽  
James Evans ◽  
David J. Mehlman ◽  
David D. McPherson ◽  
...  
Keyword(s):  
Decision Making ◽  
Elderly Patient ◽  
Transesophageal Echocardiography ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Decision Making Process

P5706Finding predictors and causes of cardiac surgery ICU readmission using machine learning and causal inference

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
J Lee ◽  
N Ahmidi ◽  
R Srinivasan ◽  
D Alejo ◽  
J Dinatale ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Cardiac Surgery ◽  
Causal Inference ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Health Record ◽  
Machine Learning Model ◽  
Patients At Risk ◽  
Bounce Back

Abstract Background “Bounce-back” to the intensive care unit (ICU) occurs when patients return to the ICU for critical changes in clinical status within the same hospital admission. Bounce-backs post-cardiac surgery increase resource utilisation, total cost of care, are associated with higher mortality and morbidity. However, prediction of bounce-back has proved to be challenging. Previous work addressed the feasibility of predicting bounce-back, but these models required significant physician input to design and calibrate the predictive variables. Purpose We aimed to develop an automated machine learning model that would identify patients at risk of bounce-back by selecting the most relevant variables from those available before onset of bounce-back. Additionally, we highlight the differences between predictive and causal inference, to demonstrate that purely associative methods of prediction can mislead clinical decision-making. Methods Clinical records of adult cardiac surgery patients between 2011 to 2016 were collected from our institutional Society for Thoracic Surgeons (STS) database and our institutional electronic health record (EHR) system. For bounce-back prediction, an L1 regularised logistic regression model was applied, which also automatically determined important variables with highest prediction effect from the initial 151 variables. For causal inference, the g-computation algorithm was used to compare the differences between causal and predictive regression effects. We quantified the performance of our system on clinically relevant metrics such as specificity, sensitivity, and area under the ROC curve (AUC). Results Of the 6189 patients, 357 (5.7%) bounced back to the ICU. The prediction model achieved an AUC score of 0.75 (0.03) and 22% specificity at 95% sensitivity, Further analysis showed 79% of the false positive patients had faced other severe postoperative complications but none of the false negative patients had downstream complications. Subsequent causal analysis revealed that the actual causal effects of treatments differed from the predictive model estimates, e.g. administration of intra-operative tranexamic acid increased the probability of bounce-back by 13% but its causal effect on bounce-back after removing confounders was negligible (an increase of only 0.5%). Conclusions Our predictive machine-learning model can successfully predict patients at risk of ICU bounce-backs, using linked STS registry data with the comprehensive electronic health record. The prediction model automatically detects important subset of variables. In addition, we note that causal and predictive model estimates of the same parameters differed, indicating that reliance on predictive models for interventional clinical decision-making may not be appropriate. Acknowledgement/Funding National Institutes of Health, Office of Naval Research, Defense Advanced Research Projects Agency

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