A simple classification of the risk in cardiac surgery

Paiement, B.; Pelletier, C; Dyrda, Ihor; Maillé, Jean-Guy; Boulanger, M.; Taillefer, Jean; Sahab, P.; Delorme, M.; Dupont, Emmanuel

doi:10.1007/bf03007718

Cited by 57 publications

(10 citation statements)

References 15 publications

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“…2 To limit adverse postoperative outcomes, clinical risk prediction models were introduced into cardiac surgical practice more than 30 years ago. 3 These models have been used to risk-stratify patients, facilitate informed consent, and aid clinical decision making, outcome evaluation, and quality improvement.…”

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confidence: 99%

An In-hospital Mortality Risk Model for Patients Undergoing Coronary Artery Bypass Grafting in China

Zhan

Chen

et al. 2020

The Annals of Thoracic Surgery

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Background. To meet the demand of increasing surgical volume and changing of patient's risk profiles of coronary artery bypass grafting in China, we developed a new risk model that predicts in-hospital mortality.Methods. The analysis included patients who underwent coronary artery bypass grafting between January 2013 and December 2016 at 87 hospitals in the Chinese Cardiac Surgery Registry. Patients in years 2013 to 2015 were randomly divided into training (n [ 31,297 [75%]) and test (n [ 10,432 [25%]) samples; 2016 patients (n [ 15047) comprised the validation sample. Demographic and clinical risk factors were identified. The Harrell C statistic was used to evaluate model discrimination, and the Hosmer-Lemeshow goodness-of-fit test was used to assess calibration.Results. The 56,776 patients were a mean age of 61.8 (SD, 8.8) years, and 24.6% were women. Overall, inhospital mortality was 2.1%. The final model included 21 risk factors represented by 16 unique variables. The

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confidence: 99%

An In-hospital Mortality Risk Model for Patients Undergoing Coronary Artery Bypass Grafting in China

Zhan

Chen

et al. 2020

The Annals of Thoracic Surgery

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“…in 1983. [1] Eight parameters were used to risk stratify patients by this method. The authors in a study of five hundred cases validated their scoring system.…”

Section: Introductionmentioning

confidence: 99%

Modifying risks to improve outcome in cardiac surgery: An anesthesiologist's perspective

Chakravarthy

2017

Ann Card Anaesth

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Challenging times are here for cardiac surgical and anesthesia team. The interventional cardiologist seem to have closed the flow of ‘good cases’ coming up for any of the surgery,; successful percutaneous interventions seem to be offering reasonable results in these patients, who therefore do not knock on the doors of the surgeons any more. It is a common experience among the cardiac anesthesiologists and surgeons that the type of the cases that come by now are high risk. That may be presence of comorbidities, ongoing medical therapies, unstable angina, uncontrolled heart failure and rhythm disturbances; and in patients with ischemic heart disease, the target coronaries are far from ideal. Several activities such as institution of preoperative supportive circulatory, ventilatory, and systemic disease control maneuvers seem to have helped improving the outcome of these ‘high risk ‘ patients. This review attempts to look at various interventions and the resulting improvement in outcomes. Several changes have happened in the realm of cardiac surgery and several more are en route. At times, for want of evidence, maximal optimization may not take place and the patient may encounter unfavorable outcomes.. This review is an attempt to bring the focus of the members of the cardiac surgical team on the value of preoperative optimization of risks to improve the outcome. The cardiac surgical patients may broadly be divided into adults undergoing coronary artery bypass graft surgery, valve surgery and pediatric patients undergoing repair/palliation of congenital heart ailments. Optimization of risks appear to be different in each genre of patients. This review also brings less often discussed issues such as anemia, nutritional issues and endocrine problems. The review is an attempt to data on ameliorating modifiable risk factors and altering non modifiable ones.

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“…10 This paved the way for interpreting outcomes as a function of the underlying severity of illness, a concept applied more than 20 years ago in evaluating results from critical care 11 and cardiac surgery. 12 Early classification systems were empirically derived additive point scores, which rapidly gave way to more sophisticated models developed by linear and logistic regression methods. These methods, along with the statistical tests necessary to assess model discrimination and calibration, are nicely summarized by my colleague and frequent coauthor Brian Nathanson in the second article 13 of this issue.…”

Section: Introductionmentioning

confidence: 99%

Metrics That Matter: Can Transparency Fix the Health Care System?

Higgins

2008

Semin Cardiothorac Vasc Anesth

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A simple classification of the risk in cardiac surgery

Cited by 57 publications

References 15 publications

An In-hospital Mortality Risk Model for Patients Undergoing Coronary Artery Bypass Grafting in China

An In-hospital Mortality Risk Model for Patients Undergoing Coronary Artery Bypass Grafting in China

Modifying risks to improve outcome in cardiac surgery: An anesthesiologist's perspective

Metrics That Matter: Can Transparency Fix the Health Care System?

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