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Ann Thorac Surg 2007;83:55-61
© 2007 The Society of Thoracic Surgeons

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Original Articles: Cardiovascular

Simple Risk Models to Predict Surgical Mortality in Acute Type A Aortic Dissection: The International Registry of Acute Aortic Dissection Score

^a Cardiovascular Center "E. Malan," Policlinico S. Donato, S. Donato Milanese, Italy
^b University of Michigan, Coordinating Center for IRAD, Ann Arbor, Michigan
^c University of Rostock, Rostock, Germany
^d Mayo Clinic, Rochester, Minnesota
^e National Research Council, Lecce, Italy
^f Tromsø University Hospital, Tromsø, Norway
^g University Hospital S. Orsola, Bologna, Italy
^h Robert-Bosch Krankenhaus, Stuttgart, Germany
ⁱ Massachusetts General Hospital, Boston, Massachusetts

Accepted for publication August 2, 2006.

^_* Address correspondence to Dr Trimarchi, Cardiovascular Center "E. Malan," Policlinico S. Donato, via Morandi 30, 20097 S. Donato Milanese, Italy (Email: satrimarchi{at}yahoo.it).

	Abstract

Top Abstract Introduction Patients and Methods Results Comment Acknowledgments References

 
BACKGROUND: Surgical mortality for acute type A aortic dissection is frequently related to preoperative clinical conditions. We report a predictive score to identify risk of death that may be helpful to assist surgeons who are considering whether to proceed with surgical correction in the case of patients in extreme clinical risk.

METHODS: Surgical outcome of 682 patients enrolled in the International Registry of Acute Aortic Dissection from 1996 to 2003 was analyzed. Two different models were used. The initial model included only preoperative variables such as demographics, history, symptoms, signs, and diagnostic methods (model 1). The second model also tested intraoperative hemodynamic and surgical variables (model 2). A bedside risk prediction tool to predict operative mortality in individual patients was developed.

RESULTS: The overall in-hospital surgical mortality was 23.9%. Independent preoperative predictors of mortality in model 1 were age greater than 70 years, prior cardiac surgery, hypotension (systolic blood pressure less than 100 mm Hg) or shock at presentation, migrating pain, cardiac tamponade, any pulse deficit, and electrocardiogram with findings of myocardial ischemia or infarction. In model 2, other predictors of surgical death were intraoperative hypotension, a right ventricle dysfunction at surgery, and a necessity to perform coronary revascularization. An independent predictor for favorable surgical outcome was right hemiarch replacement.

CONCLUSIONS: Surgery in unstable patients with acute type A aortic dissection can be highly unsuccessful. The International Registry of Acute Aortic Dissection risk models predict in-hospital mortality using a multivariable risk prediction tool, useful for surgeons and patients as they consider their surgical risk and the pros and cons of embarking on high-risk surgery.

	Introduction

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Acute type A aortic dissection (AAAD) is a cardiovascular emergency with a high potential for death. Rapid surgical treatment is indicated to prevent fatal complications. Despite improved surgical techniques and perioperative care, mortality remains high, between 15% and 30% [1–6]. Predictors of surgical mortality have been variously reported [2, 7–9]; however, a simple risk model developed from a large patient cohort to predict surgical death has not been reported.

The International Registry of Acute Aortic Dissection (IRAD) represents an opportunity to study acute aortic dissections. We completed a comprehensive analysis of 290 clinical variables and their relation to surgical outcomes. The aim of this analysis was to identify independent predictors for surgical outcomes in the largest reported series of consecutive patients affected by AAAD, and to create a simple bedside tool helpful to assist surgeons who are considering whether or not to proceed with surgical correction in patients presenting with extreme clinical conditions. Although the risk analysis is not beneficial in young patients, who generally all go to surgery, in other circumstances in which surgery may be deferred, such as age or major comorbidities, this could be useful. Decision-making by family members may also benefit. Ultimately, these risk models may be useful for evaluation of quality of risk assessment and improvement tools.

	Patients and Methods

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Patient Selection and Data Collection
The International Registry of Acute Aortic Dissection is an observational registry previously described [10]; 1,334 consecutive patients with acute aortic dissection were enrolled at 18 large tertiary centers in six countries between January 1, 1996, and December 31, 2003 (Appendix 1). The aim of the IRAD has been to assess patterns of clinical signs and presentation, diagnostic and therapeutic management, and outcomes of patients with acute aortic dissection, prospectively followed from presentation or retrospectively from hospital records. Data were collected using a standardized data form including patient demographics, history, clinical presentation, physical findings, imaging studies, medical and surgical management, in-hospital mortality, and adverse events. Completed data forms were forwarded to the coordinating center at the University of Michigan. Data forms were reviewed for analytical internal validity and scanned electronically into an Access database. For this analysis, 682 patients undergoing surgery for proximal aortic dissection with were analyzed. The institutional review boards for research at all IRAD centers approved the study protocol. Individual patient consent was adopted for the study.

Data Analysis
Summary statistics are presented as frequencies and percentages, mean ± standard deviation, or as a median and interquartile range. Missing data were not defaulted to negative, and denominators reflect only cases reported. Associations of death among nominal variables were compared using the ² test or two-sided Fisher’s exact test. Bivariate comparisons of continuous variables were compared by use of Student’s t test or Wilcoxon rank-sum test. Iterative logistic modeling was performed for in-hospital mortality using the likelihood ratio test for model selection. Initial modeling implemented elements marginally suggestive of an unadjusted association of in-hospital mortality (p < 0.20). Variables were reviewed for clinical significance before testing. Diagnostic routines (Hosmer-Lemeshow test for lack of fit, change in deviance and residuals, and leverage indicators) were used in model selection. Two different models were created, first evaluating demographics, history, and preoperative variables as symptoms, signs, and diagnostic methods (model 1). The second model also included intraoperative hemodynamic and surgical variables (model 2). Statistical analyses were performed using SAS 8.2 (SAS Institute, Cary, NC) and SPSS 11.5 (SPSS Inc, Chicago, IL).

Simple Bedside Risk Prediction Tool
The variables significantly associated with surgical mortality in the best regression model were assigned a score equal to their coefficients in the fitted model (natural logarithm of their odds ratios rounded to the nearest decimal). The sum of this numerical score could then be used to predict the operative mortality in individual patients. A risk prediction tool that plotted the risk score against the corresponding predicted death rate was developed to assist surgeons who are considering whether or not to proceed with surgical correction in high-risk patients.

	Results

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Patient Population
Of 1,334 consecutive patients with acute aortic dissection enrolled between January 1, 1996, and December 31, 2003, 834 (62.5%) patients had type A dissection. Of these, 148 patients (17.7%) were treated medically for a variety of reasons such as advanced age, severe comorbid illness, intramural hematoma, or refusal of surgical intervention; 4 had a percutaneous approach. A total of 682 patients underwent surgery for AAAD and were included in this analysis (Tables 1–3). Their mean age was 59.9 ± 13.8 years, with male predominance (70.3%). Three fourths of the patients had been transferred to an IRAD center from a referral hospital for definitive treatment. The diagnosis of AAAD was made using transesophageal echocardiography in 77.4% and computed tomography in 70.7%. A proximal intimal tear was identified in the aortic root in 38.4%, the ascending aorta in 56.4%, and the aortic arch in 3.1%. The mean diameter of the aortic annulus was 29 ± 0.8 mm, the aortic root 45 ± 1.1 mm, the sinotubular junction 43 ± 1.1 mm, the ascending aorta 53 ± 1.3 mm, and the aortic arch 37 ± 0.7 mm.

Univariate Predictors of Surgical Mortality
Clinical characteristics associated with death after surgery (p < 0.05) were age older than 70 years, female sex, presence of atherosclerosis, prior cardiac surgery, presentation with severe or worst-ever pain, migrating pain, a widened mediastinum on chest roentgenograph, hypotension (systolic blood pressure <100 mm Hg) or shock (systolic blood pressure <80 mm Hg) before surgery, myocardial ischemia or infarction with new Q waves, preoperative myocardial infarction, presence of a new neurologic deficit, any pulse deficit, cardiac tamponade, periaortic hematoma, limb ischemia, left or right ventricle dysfunction at surgery, a composite aortic-valve graft implant procedure, and the necessity to perform a coronary revascularization. Predictors of survival after surgery were radiating pain, electrocardiogram without abnormalities, normotension at presentation and at surgery, preoperative aortography, site of origin of dissection evidenced in the ascending aorta, and supracoronary aortic graft replacement.

Simple Model for Surgical In-Hospital Death
Independent predictors are summarized in a risk-adjusted model (Tables 5, 6). The C statistics were 0.76 for model 1 and 0.81 for model 2, giving good model discrimination. For model 1, the deviance probability value was 0.28 and the Hosmer-Lemeshow statistic was not significant, indicating little departure from perfect fit (² = 8.16; degrees of freedom, 6; p = 0.23). Figures 1 and 2 plot expected deaths versus observed deaths. There was good agreement between observed and predicted death for score categories (Figs 3, 4). For model 2, the deviance probability value was 0.04 and the Hosmer-Lemeshow statistic was not significant, indicating little departure from perfect fit (² = 8.59; degrees of freedom, 8; p = 0.38).

View larger version (15K): [in this window] [in a new window]   Fig 1. Model 1: observed versus predicted death by score. Model 1 included only preoperative variables from demographics, history, symptoms, signs, and diagnostic methods. Logarithmic odds of death were calculated as –3.20 + 0.68 x age > 70 + 1.44 x history of aortic valve replacement + 1.17 x hypotension (systolic blood pressure < 100 mm Hg) or shock at presentation + 0.88 x migrating chest pain + 0.97 x preoperative cardiac tamponade + 0.56 x any pulse deficit + 0.57 x electrocardiogram with findings of myocardial ischemia or infarction. Hosmer-Lemeshow 2 (6 degrees of freedom) = 8.16. Probability by 2 = 0.23.

View larger version (14K): [in this window] [in a new window]   Fig 2. Model 2: observed versus predicted death by score. Model 2 also included intraoperative hemodynamic and surgical variables. Logarithmic odds of death were calculated as –3.35 + 0.58 x age > 70 + 1.78 x history of aortic valve replacement + 0.92 x hypotension (systolic blood pressure < 100 mm Hg) or shock at presentation + 0.70 x migrating chest pain + 0.64 x any pulse deficit + 1.34 x intraoperative hypotension + 1.59 x right ventricle dysfunction at surgery + 0.93 x a necessity to perform a coronary artery bypass graft. An independent predictor for favorable surgical outcome was –0.65 x partial arch replacement. Hosmer-Lemeshow 2 (8 degrees of freedom) = 8.59. Probability by 2 = 0.38.

View larger version (30K): [in this window] [in a new window]   Fig 3. Model 1: observed versus model probabilities of death by score. Example: 77-year-old woman with migrating chest pain, preoperative cardiac tamponade, a pulse deficit, and ST elevation. Her model score is 0.7 (age > 70) + 0.9 (migrating chest pain) + 1.0 (preoperative cardiac tamponade) + 0.6 (pulse deficit) + 0.6 (ST elevation). Total score = 3.8. Drawing a line straight up from her risk score, the estimate of her surgical mortality risk is 61%.

View larger version (30K): [in this window] [in a new window]   Fig 4. Model 2: observed versus model probabilities of death by score. Example: 54-year-old man presenting with shock, both at symptom presentation and at his arrival in the operating room, who needed a coronary artery bypass graft and right hemiarch replacement. His model score is 0.9 (shock at presentation) + 1.3 (shock at his arrival in the operating room) + 0.9 (needing coronary artery bypass graft) – 0.7 (partial arch replacement). Total score = 2.4. Drawing a line straight up from his risk score, the estimate of his mortality risk is 31%.

	Comment

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The International Registry of Acute Aortic Dissection also demonstrates that prior aortic valve replacement is represented in both models as a predictor of surgical mortality. In such patients, greater technical difficulties can be present, often requiring total replacement of the sinotubular junction or coronary artery bypass grafts. Both conditions in this experience were associated with higher risk (p = 0.05 and p = 0.004, respectively), as was a preoperative aortic root diameter greater than 44 mm, which led to more complex interventions (p = 0.03). On the other hand, hemiarch replacement were a predictor of favorable surgical results. This technique, which was used in 25% of surgical interventions, is directly related to the open procedure, which is widely accepted as the standard method to perform a safe distal anastomosis using hypothermic circulatory arrest with or without cerebral perfusion. In the IRAD, open distal anastomosis was performed in 92% of patients, using cerebral perfusion in more than 50% of cases (not significant for both). For accurate risk prediction, simple bedside tools for estimating surgical risk in acute aortic dissection can assist surgeons in advising patients and their families about the realistic chances of the operation, both preoperatively (model 1) and postoperatively (model 2). The IRAD prediction tools provide an accurate method to predict operative results in AAAD patients (Figs 1–4). In particular, model 1 is applicable to all potential surgical candidates affected by AAAD. The model is likely generalizable to most patients, given the number of patients enrolled in different institutions across six countries and the broad spectrum of clinical presentations observed.

Study Limitations
The International Registry of Acute Aortic Dissection is an observational study, reflecting care at a number of aortic centers, and the results cannot be generalized to all patients who undergo surgery in a given institution. In-hospital death was the only outcome that was assessed in this analysis, and, although important, it is not sufficient for full evaluation of patients with type A aortic dissection. Further studies are needed to address the optimal surgical approach for evaluating the predictors of short-term and long-term survival.

Conclusions
Acute type A aortic dissection is a highly lethal disease in which prompt identification and surgery is the best hope for survival. The present study confirms that principal determinants of surgical mortality in AAAD patients are preoperative complications and comorbidities. The IRAD risk prediction tool can provide an accurate prediction of mortality risk and can assist with a decision of whether or not to proceed with surgery. In particular, the tool may be useful in managing the truly moribund patient in whom, regardless of surgery, the likelihood of survival is small.

	Appendix

 
The International Registry of Acute Aortic Dissection (IRAD) Investigators
Co-Principal Investigators: Kim A. Eagle, MD, University of Michigan, Ann Arbor, Michigan USA; Eric M. Isselbacher, MD, Massachusetts General Hospital, Boston, Massachusetts, USA; and Christoph A. Nienaber, MD, University of Rostock, Rostock, Germany.

Co-Investigators: Eduardo Bossone, MD, National Research Council, Lecce, Italy; Arturo Evangelista, MD, Hospital General Universitari Vall d’Hebron, Barcelona, Spain; Rosella Fattori, MD, University Hospital S. Orsola, Bologna, Italy; Dan Gilon, MD, Hadassah University Hospital, Jerusalem, Israel; Stuart Hutchison, MD, St. Michael’s Hospital, Toronto, Ontario, Canada; Alfredo Llovet, MD, Hospital Universitario "12 de Octubre," Madrid, Spain; Truls Myrmel, MD, Tromsø University Hospital, Tromsø, Norway; Patrick O’Gara, MD, Brigham and Women’s Hospital, Boston, Massachusetts, USA; Jae K. Oh, MD, Mayo Clinic, Rochester, Minnesota, USA; Linda A. Pape, MD, University of Massachusetts Hospital, Worcester, Massachusetts, USA; Udo Sechtem, MD, Robert-Bosch Krankenhaus, Stuttgart, Germany; Toru Suzuki, MD, University of Tokyo, Tokyo, Japan; and Santi Trimarchi, MD, Policlinico San Donato, San Donato Milanese, Italy.

	Acknowledgments

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We acknowledge the University of Michigan Faculty Group Practice and the Varbedian Fund for Aortic Research for support. The following companies have provided research funding for the current and past calendar year: St. Jude Medical; W.L. Gore and Associates, Inc; Cryolife, Inc; Medtronic, Inc; Atricure, Inc; Thoratec Corp; Carbomedics/Sorin Group; Jarvik Heart, Inc; Baxter; Edwards Lifesciences; Boston Scientific Corp; Avant Immunotherapeutics, Inc; AstraZeneca; and TransTech Pharma, Inc.

	References

Top Abstract Introduction Patients and Methods Results Comment Acknowledgments References

 

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