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Chang CH, Chen SW, Fan PC, Lee CC, Yang HY, Chang SW, Pan HC, Tsai FC, Yang CW, Chen YC. Sequential organ failure assessment score predicts mortality after coronary artery bypass grafting. BMC Surg 2017; 17:22. [PMID: 28264675 PMCID: PMC5339985 DOI: 10.1186/s12893-017-0219-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Accepted: 02/25/2017] [Indexed: 01/14/2023] Open
Abstract
Background Mortality after coronary artery bypass grafting (CABG) is generally associated with underlying disease and surgical factors overlooked in preoperative prognostic models. Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE II) scores are widely used in intensive care units for outcome prediction. This study investigated the accuracy of these models in predicting mortality. Methods Between January 2010 and April 2013, 483 patients who underwent isolated CABG were enrolled. The clinical characteristics, outcomes, and prognostic model scores of the patients were collected. Discrimination was assessed using the area under the curve approach. Results Both SOFA and APACHE II scores were effective for predicting in-hospital mortality. Among the organ systems examined in the SOFA, the cardiac and renal systems were the strongest predictors of CABG mortality. Multivariate analysis identified only the SOFA score as being an independent risk factor for mortality. Conclusion In summary, the SOFA score can be used to accurately identify mortality after isolated CABG.
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Affiliation(s)
- Chih-Hsiang Chang
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan.,School of medicine, College of Medicine, Chang Gung University, 199 Tung Hwa North Road, Taoyuan, Taipei, 105, Taiwan
| | - Shao-Wei Chen
- Department of Cardiothoracic and Vascular Surgery, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan.,School of medicine, College of Medicine, Chang Gung University, 199 Tung Hwa North Road, Taoyuan, Taipei, 105, Taiwan
| | - Pei-Chun Fan
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan.,School of medicine, College of Medicine, Chang Gung University, 199 Tung Hwa North Road, Taoyuan, Taipei, 105, Taiwan
| | - Cheng-Chia Lee
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan.,School of medicine, College of Medicine, Chang Gung University, 199 Tung Hwa North Road, Taoyuan, Taipei, 105, Taiwan
| | - Huang-Yu Yang
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan.,School of medicine, College of Medicine, Chang Gung University, 199 Tung Hwa North Road, Taoyuan, Taipei, 105, Taiwan
| | - Su-Wei Chang
- Clinical Informatics and Medical Statistics Research Center, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Heng-Chih Pan
- Department of Nephrology, Chang Gung Memorial Hospital, Keelung branch, New Taipei City, Taiwan.,School of medicine, College of Medicine, Chang Gung University, 199 Tung Hwa North Road, Taoyuan, Taipei, 105, Taiwan
| | - Feng-Chun Tsai
- Department of Cardiothoracic and Vascular Surgery, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan.,School of medicine, College of Medicine, Chang Gung University, 199 Tung Hwa North Road, Taoyuan, Taipei, 105, Taiwan
| | - Chih-Wei Yang
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan.,School of medicine, College of Medicine, Chang Gung University, 199 Tung Hwa North Road, Taoyuan, Taipei, 105, Taiwan
| | - Yung-Chang Chen
- Kidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan. .,School of medicine, College of Medicine, Chang Gung University, 199 Tung Hwa North Road, Taoyuan, Taipei, 105, Taiwan.
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Tamayo E, Fierro I, Bustamante-Munguira J, Heredia-Rodríguez M, Jorge-Monjas P, Maroto L, Gómez-Sánchez E, Bermejo-Martín F, Alvarez F, Gómez-Herreras J. Development of the Post Cardiac Surgery (POCAS) prognostic score. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2013; 17:R209. [PMID: 24063719 PMCID: PMC4057191 DOI: 10.1186/cc13017] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2013] [Accepted: 09/24/2013] [Indexed: 01/29/2023]
Abstract
Introduction The risk of mortality in cardiac surgery is generally evaluated using preoperative risk-scale models. However, intraoperative factors may change the risk factors of patients, and the organism functionality parameters determined upon ICU admittance could therefore be more relevant in deciding operative mortality. The goals of this study were to find associations between the general parameters of organism functionality upon ICU admission and the operative mortality following cardiac operations, to develop a Post Cardiac Surgery (POCAS) Scale to define operative risk categories and to validate an operative mortality risk score. Methods We conducted a prospective study, including 920 patients who had undergone cardiac surgery with cardiopulmonary bypass. Several parameters recorded on their ICU admission were explored, looking for a univariate and multivariate association with in-hospital mortality (90 days). In-hospital mortality was 9%. Four independent factors were included in the POCAS mortality risk model: mean arterial pressure, bicarbonate, lactate and the International Normalized Ratio (INR). The POCAS scale was compared with four other risk scores in the validation series. Results In-hospital mortality (90 days) was 9%. Four independent factors were included in the POCAS mortality risk model: mean arterial pressure, bicarbonate ratio, lactate ratio and the INR. The POCAS scale was compared with four other risk scores in the validation series. Discriminatory power (accuracy) was defined with a receiver-operating characteristics (ROC) analysis. The best accuracy in predicting in-hospital mortality (90 days) was achieved by POCAS. The areas under the ROC curves of the different systems analyzed were 0.890 (POCAS), followed by 0.847 (Simplified Acute Physiology Score (SAP II)), 0.825 (Sepsis-related Organ Failure Assessment (SOFA)), 0.768 (Acute Physiology and Chronic Health Evaluation (APACHE II)), 0.754 (logistic EuroSCORE), 0.714 (standard EuroSCORE) and 0.699 (Age, Creatinine, Ejection Fraction (ACEF) score). Conclusions Our new system to predict the operative mortality risk of patients undergoing cardiac surgery is better than others used for this purpose (SAP II, SOFA, APACHE II, logistic EuroSCORE, standard EuroSCORE, and ACEF score). Moreover, it is an easy-to-use tool since it only requires four risk factors for its calculation.
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