Proposed preoperative risk score for patients candidate to cardiac valve surgery

Abstracts

BACKGROUND: To establish a risk score for heart surgery allows the assessment of preoperative risk, informing the patient and defining care during the intervention. OBJECTIVE: To assess preoperative risk factors for death in cardiac valve surgery and construct a simple risk model (score) for in-hospital mortality of patients candidate to surgery at Hospital São Lucas of Pontifícia Universidade Católica do Rio Grande do Sul (HSL-PUCRS). METHODS: The study sample included 1,086 adult patients that underwent cardiac valve surgery between January 1996 and December 2007 at HSL-PUCRS. Logistic regression was used to identify risk and in-hospital mortality factors. The model was developed in 699 patients and its performance was tested in the remaining data (n = 387). The final model was created using the total study sample (n = 1,086). RESULTS: Global mortality was 11.8%: 8.8% of elective cases and 63.8% of emergency cases. At the multivariate analysis, 9 variables remained independent predictors for the outcome: advanced age, surgical priority, female sex, ejection fraction < 45%, concomitant myocardial revascularization (CABG), pulmonary hypertension, NYHA functional class III or IV, creatinine levels (1.5 to 2.49 mg/dl and > 2.5 mg/dl or undergoing dialysis). The area under the ROC curve was 0.83 (95% CI: 0.78-0.86). The risk model showed good capacity for observed/predicted mortality: the Hosmer-Lemeshow test was x² = 5.61; p = 0.691 and r = 0.98 (Pearson's coefficient). CONCLUSION: The variables predictive of in-hospital mortality allowed the construction of a simplified risk score for daily practice, which classifies the patient as having low, moderate, high, very high and extremely high preoperative risk.

Probability; risk; preoperative care; thoracic surgery; heart valves

FUNDAMENTO: Estabelecer escore de risco para cirurgias cardíacas permite avaliar risco pré-operatório, informar o paciente e definir cuidados durante a intervenção. OBJETIVO: Pesquisar fatores de risco pré-operatórios para óbito em cirurgia cardíaca valvar e construir um modelo de risco simples (escore) para mortalidade hospitalar para os pacientes candidatos à cirurgia no Hospital São Lucas da Pontifícia Universidade Católica do Rio Grande do Sul (HSL-PUCRS). MÉTODOS: A amostra do estudo inclui 1.086 pacientes adultos que realizaram cirurgia cardíaca valvar entre Janeiro de 1996 a Dezembro de 2007 no HSL-PUCRS. Regressão logística foi usada para identificar fatores de risco e mortalidade hospitalar. O modelo foi desenvolvido em 699 pacientes e seu desempenho foi testado nos dados restantes (n = 387). O modelo final foi criado com a análise da amostra total (n = 1.086). RESULTADOS: A mortalidade global foi 11,8%: 8,8% casos eletivos e 63,8% cirurgia de emergência. Na análise multivariada, 9 variáveis permaneceram preditores independentes para o desfecho: idade avançada, prioridade cirúrgica, sexo feminino, fração de ejeção < 45%, cirurgia de revascularização miocárdica (CRM) concomitante, hipertensão pulmonar, classe funcional III ou IV da NYHA, creatinina (1,5 a 2,49 mg/dl e > 2,5 mg/dl ou diálise). A área sob a curva ROC foi 0,83 (IC: 95%, 0,78 - 0,86). O modelo de risco mostrou boa habilidade para mortalidade observada/prevista: teste Hosmer-Lemeshow foi x² = 5,61; p = 0,691 e r = 0,98 (coeficiente de Pearson). CONCLUSÃO: As variáveis preditoras de mortalidade hospitalar permitiram construir um escore de risco simplificado para a prática diária, que classifica o paciente de baixo, médio, elevado, muito elevado e extremamente elevado risco pré-operatório.

Probabilidade; risco; cuidados pré-operatórios; cirurgia torácica; valvas cardíacas

FUNDAMENTO: Establecer un escore de riesgo para cirugías cardiacas permite evaluar el riesgo preoperatorio, informar al paciente y definir cuidados durante la intervención. OBJETIVO: Investigar factores de riesgo preoperatorios de muerte en cirugía cardiaca valvular y construir un modelo de riesgo simple (escore) para mortalidad hospitalaria para los pacientes candidatos a cirugía en el Hospital São Lucas de la Pontificia Universidad Católica del Rio Grande do Sul (HSL-PUCRS). MÉTODOS: La muestra del estudio incluyó 1.086 pacientes adultos a los que se realizó cirugía cardiaca valvular entre enero de 1996 y diciembre de 2007 en el HSL-PUCRS. Para identificar factores de riesgo y mortalidad hospitalaria se utilizó regresión logística. El modelo fue desarrollado en 699 pacientes y se probó su desempeño en los datos restantes (n = 387). El modelo final fue creado con el análisis de la muestra total (n = 1.086). RESULTADOS: La mortalidad global fue del 11,8%: un 8,8% de casos electivos y un 63,8% de cirugía de emergencia. En el análisis multivariado, 9 variables permanecieron como predictores independientes para el desenlace: edad avanzada, prioridad quirúrgica, sexo femenino, fracción de eyección < 45%, cirugía de revascularización miocárdica (CRM) concomitante, hipertensión pulmonar, clase funcional III o IV de la NYHA, creatinina (1,5 - 2,49 mg/dl y > 2,5 mg/dl o diálisis). El área bajo la curva ROC fue 0,83 (IC: 95%,0,78-0,86). El modelo de riesgo mostró buena habilidad para mortalidad observada/prevista: el test Hosmer-Lemeshow fue x² = 5,61; p = 0,691 y r = 0,98 (coeficiente de Pearson). CONCLUSIÓN: Las variables predictoras de mortalidad hospitalaria permitieron construir un escore de riesgo simplificado para la práctica diaria, que clasifica al paciente en bajo, medio, elevado, muy elevado y extremadamente elevado riesgo preoperatorio.

Probabilidad; riesgo; cuidados preoperatorios; cirugía torácica; válvulas cardiacas

ORIGINAL ARTICLE

Proposed preoperative risk score for patients candidate to cardiac valve surgery

João Carlos Vieira da Costa Guaragna; Luiz Carlos Bodanese; Fabiana Lucas Bueno; Marco Antonio Goldani

Hospital São Lucas da Pontificia Universidade Católica do Rio Grande do Sul, Porto Alegre, RS - Brazil

Mailing address

ABSTRACT

BACKGROUND: To establish a risk score for heart surgery allows the assessment of preoperative risk, informing the patient and defining care during the intervention.

OBJECTIVE: To assess preoperative risk factors for death in cardiac valve surgery and construct a simple risk model (score) for in-hospital mortality of patients candidate to surgery at Hospital São Lucas of Pontifícia Universidade Católica do Rio Grande do Sul (HSL-PUCRS).

METHODS: The study sample included 1,086 adult patients that underwent cardiac valve surgery between January 1996 and December 2007 at HSL-PUCRS. Logistic regression was used to identify risk and in-hospital mortality factors. The model was developed in 699 patients and its performance was tested in the remaining data (n = 387). The final model was created using the total study sample (n = 1,086).

RESULTS: Global mortality was 11.8%: 8.8% of elective cases and 63.8% of emergency cases. At the multivariate analysis, 9 variables remained independent predictors for the outcome: advanced age, surgical priority, female sex, ejection fraction < 45%, concomitant myocardial revascularization (CABG), pulmonary hypertension, NYHA functional class III or IV, creatinine levels (1.5 - 2.49 mg/dl and > 2.5 mg/dl or undergoing dialysis).The area under the ROC curve was 0.83 (95%CI: 0.78 - 0.86). The risk model showed good capacity for observed/predicted mortality: the Hosmer-Lemeshow test was x² = 5.61; p = 0.691 and r = 0.98 (Pearson's coefficient).

CONCLUSIONS: The variables predictive of in-hospital mortality allowed the construction of a simplified risk score for daily practice, which classifies the patient as having low, moderate, high, very high and extremely high preoperative risk. (Arq Bras Cardiol 2010; 94(4):507-514)

Key Words: Probability; risk; preoperative care; thoracic surgery; heart valves/surgery.

Introduction

Currently, a total of 275,000 cardiac valve replacement surgeries are carried out worldwide¹, with operative mortality ranging from 1 to 15%^2,3. In Brazil, at the analysis of more than 115,000 heart surgeries carried out between 2000 and 2003, the reported mortality was 8%. The main risk factors for death during valve replacement surgeries are: advanced age⁴, female sex^5-7, chronic obstructive pulmonary disease (COPD)^8,9, New York Heart Association functional class (NYHA- FC), ventricular dysfunction, surgical priority (urgency/emergency), pulmonary arterial hypertension (PAH)¹⁰, renal dysfunction¹¹, valvular disease associated with ischemic cardiopathy¹², reoperation^13-16 and infectious endocarditis^17-20.

The multivariate analysis of these risk factors, observed in a certain sample, enables the construction of a risk score, with the objective²¹ of obtaining an actual surgical risk estimate, making some variables capable of undergoing intervention in the preoperative phase and monitoring the effect of technical alterations, assistential dynamics and failures in the treatment offered to patients.

Thus, the objective of the present study was to research preoperative factors that could be associated with the occurrence of death in cardiac valve surgery, as well as construct a risk score for in-hospital mortality for patients candidate to cardiac valve surgery in Hospital São Lucas of PUCRS.

Methods

Population and sample

Between Janeiro 1996 and December de 2007, 3,895 patients were submitted to heart surgery in Hospital São Lucas of PUC - RS. Of these, 1,086 underwent isolated cardiac valve surgery or CABG-associated surgery, which was the object of the present study.

Study design

The present was a historical cohort observational study. The data were prospectively collected and inserted in the database of the Cardiac Surgery Postoperative Unit of Hospital São Lucas of PUCRS.

Inclusion criteria

Patients aged 18 or older submitted to cardiac valve replacement surgery (valve replacement or plasty), isolated or in combination with myocardial revascularization surgery (CAGB).

Exclusion criteria

Tricuspid and pulmonary valve surgeries, when isolated surgical procedures, were excluded from the analysis due to the small number of patients submitted to these procedures.

Study variables

The variables included in the analysis were:

• Sex (male/female)

• Age

• Surgical priority: emergency/urgency surgery considered as a single variable and defined as the need to undergo surgical intervention in up to 48 hours, due to imminent risk of death or unstable clinical-hemodynamic condition.

• Heart failure functional class according to NYHA criteria.

• Atrial fibrillation

• Previous cerebrovascular accident

• Previous heart surgery

• Diabetes

• COPD: diagnosed clinically and/or through a radiological study of the thorax and/or spirometry and/or current drug treatment (corticoids, bronchodilators)

• Systemic arterial hypertension (SAH)

• Endocarditis: current or recent history (< 60 days)

• Obesity: defined when the body mass index (BMI) > 30 kg/m²

• Ejection fraction: measured by echocardiography

• Serum creatinine

• Pulmonary arterial hypertension (PAH): detected at the echocardiogram. Defined as systolic pressure in pulmonary artery > 30 mmHg (according to the Brazilian Guideline of Pulmonary Arterial Hypertension of 2005). However, for the construction of the score, there was no stratification regarding the degree of severity of the latter, only detection of its presence or absence.

Outcome

Death - Considered in the transoperative period and throughout the entire hospitalization period.

Procedures

Anesthesia, extracorporeal circulation (ECC) and cardioplegia were carried out according to the standard procedures of Hospital São Lucas of PUC-RS, as previously described²². After the surgery, all patients were transferred to the postoperative intensive care unit (ICU) and mechanically ventilated.

Statistical analysis

The continuous variables were described by means and standard deviations and compared by Student's t test. The categorical variables (or categorized continuous variables) were described by the Chi-square test. To construct the risk score, the database was randomly divided in two portions: 2/3 of the data were used for the modeling and 1/3 for validation.

Obtaining the preliminary risk model - The initial consideration of the variables followed a hierarchical model based on biological plausibility and external information (literature) regarding the relevance and power of the association between these potential risk factors and the occurrence of the outcome being analyzed (in-hospital death).

Once these variables were listed, multiple logistic regression was used in a backward selection process and all variables with a level of significance p < 0.05 were maintained in the model. After that, a weighted risk score was built, based on the magnitude of the b coefficients of the logistic equation. After they were transformed (exp [b]) into odds ratios, the values were rounded to the closest whole number to create the score.

Validation - The preliminary risk score was applied to the validation database and two performance statistics were obtained: c-statistics (area under the ROC curve), the Hosmer-Lemeshow (HL) Chi-square test of goodness-of-fit and the consequent Pearson's coefficient of correlation between the observed events and those predicted by the model. The values for the area under the ROC curve between 0.85 and 0.90 indicate an excellent discriminatory power. A non-significant HL Chi-square test (P > 0.05) shows good model calibration. A Pearson's coefficient of correlation value r > 0.7 indicates a strong correlation between the observed values and the predicted ones.

Obtaining the final risk score - Once an appropriate performance of the model was observed at the validation process, the databases (modeling and validation) were combined to obtain the final risk score. During this process, the variables that had been removed were not included, which simply resulted in the obtaining of more precise estimates for the coefficients that had been previously calculated. The same aforementioned performance statistics were also presented.

The resulting logistic model followed the formula presented below and, differently from the score, it presents direct estimates of outcome occurrence probability. This process is understood by some authors¹⁰⁹ as being more appropriate to obtain event estimates, although it presents a certain degree of mathematical complexity for its use in daily medical practice. The use of the logistic model is more adequate for the prognosis of individual risk, mainly in patients with a very high risk in the additive model²³.

P(event) = 1 / 1 + exp (-(β0 + β1x1 + . . . + βk xk))

The data were processed and analyzed with the help of the Statistical Package for the Social Sciences (SPSS), release 15.0.

Ethical considerations - The research project of the present study was submitted to the Ethics Committee in Research of FAMED PUCRS, registration # 06003478.

Results

Characteristics

Of the total sample (1,086) 128 patients died (11.8%). Considering only the elective surgeries, the mortality rate decreases to 8.8%. In cases where the surgical intervention was an emergency/urgency (5.3%), mortality was very high: 63.8%. These patients contributed with 29% of the total number of deaths. The mean age of the studied population was 55.5 years (± 15.8 years) and 45% of the patients were aged 60 years or older. Regarding gender, 56% of the patients were males. Combined myocardial revascularization (CABG) was necessary in 20% of the patients (Table 1).

Development of the risk model (modeling)

The multiple logistic regression of the predictors was carried out in 699 non-consecutive patients (random selection), which accounted for 2/3 of the total sample. The selected predictors, due to their statistical importance for the risk score construction, were: age (> 60 years), surgical priority, ejection fraction (< 45%), female sex, combined CABG, pulmonary hypertension, functional class III or IV (NYHA), creatinine > 1.5 to 2.49 mg/dl and creatinine > 2.5 mg/dl or dialysis (Table 2). The scoring system, according to what was described in the statistical analysis, is shown in Table 2. The area under the ROC curve of the obtained model was 0.82 (95%CI: 0.77 to 0.87).

Validation of the risk model

The external validation was carried out in 387 patients (1/3 of the total sample), chosen randomly. The risk model accuracy was measured by the area under the ROC curve of 0.84 (95%CI: 0.77 to 0.90) thus presenting good discriminatory capacity. There was also a good correlation between the predicted and the observed mortality: r = 0.93 with x² = 8.68 (p = 0.37) (Hosmer-Lemeshow test).

Risk model in the total sample: (n = 1,086)

The model was then reconstructed based on the integration of the score developed with data from 2/3 of the sample and the validation data. The multiple logistic regression was used with the listed variables, originating the recalibrated risk score based on the magnitude of the β coefficients of the logistic equation (Table 3 and Table 4). The factors associated with higher risk were: surgical priority (emergency/urgency), followed by high creatinine levels (> 2.5 mg/dl), age > 60 years and combined CABG. The area under the ROC curve of the obtained model was 0.83 (95%CI: 0.78 - 0.86) (Figure 1). Table 6 shows the risk of death according to the score and the classification of this risk (additive score). To calculate the logistic score (evaluation of individual risk), the logistic equation inserted in Table 3 must be used. In the total sample, 70.5% of the patients submitted to surgery presented low and moderate risk, that is, mortality estimated by the score at 2% and 7.9%, respectively. The risk was considered extremely high in 6.7% of the patients. To test the calibration of the model, we compared the observed mortality with the predicted mortality among all patients in each of the five intervals of score classification, obtaining a predicted/observed coefficient of correlation of 0.98 with x² = 5.61 (p = 0.691) (Hosmer-Lemeshow test) (Figure 2).

Discussion

This study identified nine predictors for death at cardiac valve surgery, which according to their risk, formed the score: age > 60 years, urgency/emergency surgery, ejection fraction < 45%, female sex, concomitant myocardial revascularization surgery, pulmonary hypertension, functional class III or IV (NYHA) and renal failure (2 variables). A clinical usefulness tool was then developed, which is easy to apply to calculate the preoperative risk of death in patients candidate to cardiac valve surgery. The choice of variables was based on the experience of the postoperative cardiac surgery service of Hospital São Lucas of PUC-RS, as well as from the previous literature studies^{3,12,13,24,25}. One must bear in mind, however, that when using a predictive model of risk at the bedside, we are evaluating the possibility of death of a population and not of that particular patient²⁶.

The mortality rate in the present study was 11.8%. When the urgency/emergency surgeries were not considered, the mortality rate was 8.8% (isolated cardiac valve surgery or with combined CABG). Although higher than the rates reported in most European and North-American centers, the mortality rate was similar to that reported in Brazil, according to data from the DATASUS, that is, 8.9% for valvular surgeries^27,28. Considering that both the STS register and the UK Cardiac Surgical Register are voluntary, whereas DATASUS is administrative, the comparison between the obtained surgical results is inappropriate. Pons et al²⁹ from the Catalan Study Group on Open Surgery Heart developed a risk model for death based on the analysis of 1,309 cardiac surgeries, where 47% were valvular procedures. The mortality reported by the authors, global as well as for elective cases, was similar to ours: 10.9% and 8%, respectively. In the risk model developed by Ambler et al³, the mortality for elective surgeries was 5%. Nowicki et al¹², from the Northern New England Cardiovascular Disease Study Group, reported 6.2% of deaths for aortic valve surgeries and 9.4% for mitral valve procedures. In Brazil, Brandao et al³⁰, in a study of double-leaflet mechanical prosthesis implant, reported a mitral mortality of 13.5% and an aortic mortality of 7.5%. De Bacco et al¹⁵, also in our country, in a retrospective study of 703 patients that were submitted to surgery for the implant of bovine pericardial bioprosthesis, reported a mortality rate of 14.3% of in-hospital deaths and 12.1% when the surgery was elective. What literature demonstrates, therefore, is a broad oscillation in the mortality rate, which stimulates the search for factors that can contribute to in-hospital mortality.

Age older than 60 years was an important predictor of death in the present study, worth 3 score points. Age, as a predictor of death, is a part of all risk scores found in the literature^{3,12,13,24,25}. What is noteworthy in each score is the difference in the cutoff based on which the surgical risk is established. The study by Hannan et al²⁵ verified that patients submitted to surgery when they were at least 50 years of age presented higher in-hospital mortality, regardless of the performed valve intervention: aortic, mitral or multivalve replacement, with or without revascularization surgery. The EuroSCORE²⁴ was able to determine that after 60 years of age, there is an increase in the risk of death and adds a point to the score for every 5 years thereafter.

In the present study, the mortality was higher among women: 14.4% vs 9.8% among men, with the female sex being an independent risk factor for in-hospital death (OR; 1.9 95%CI: 1.2 - 3.0). It added 2 points to the risk score. However, it must be observed that the female patient in the absence of another risk factor has a low mortality, estimated at 2%, similar to the male patient in the same situation. The increased risk for women is a controversial issue in literature^3,12,24. Patients with NYHA-FC III or IV constitute 44% of the cases in our sample and presented an in-hospital mortality rate of 18.1% vs 6.8% in those with NYHA-FC I or II. That added 2 points to the score. This finding demonstrates that the surgery in patients with valvulopathies must be carried out before the development of symptoms that can significantly impair the physical capacity. Thus, the functional class, which is a strictly clinical parameter, is an important prognostic factor, which, in spite of its subjective nature, is easily registered at the bedside, taking the patient's symptom into account.

In the present study we verified that EF < 45% was an important risk factor for death with an OR of 2.1; 95%CI: 1.2 - 3.7 at the logistic regression, adding 2 points to the score, which demonstrates the importance of ventricular dysfunction, even in the absence of symptoms.

Pulmonary hypertension, which was considered when PASP > 30 mmHg obtained by echocardiogram, was present in 25% of the patients submitted to surgery and was an independent risk factor for death in our series: OR 2.0; 95%CI: 1.3 - 3.2, adding 2 points to the score. Although it was not evaluated in most studies^12,29,31, the presence of PAH showed to be an important predictor of death in some series^13,24.

The study demonstrated that patients candidate to valve replacement associated with myocardial revascularization present a 3-fold higher risk of death in the postoperative period, adding 3 points to the score. The high occurrence of death among these patients - 25.2% vs 8.5% for isolated valve replacement - demonstrates that other comorbidities are associated.

The presence of high creatinine levels is an important predictor of death risk in the present study. Patients with creatinine levels > 2.5 mg/dl (undergoing dialysis or not) present a six-fold higher risk (OR 6.00; 95%CI: 2.12 - 16.99). We included patients undergoing dialysis in this group, due to the small number of patients in the sample (only 9 patients).

The highest impact on the score developed in the present study was the performance of cardiac valve surgery in patients presenting imminent risk of death. This situation was observed in 5.3% of the cases in the sample and the rate of mortality was 64%, being responsible for 29% of the deaths. A study published by De Bacco showed a similar rate of mortality⁴. Recently, in our country, a new risk score for cardiac valve surgery (VMPC) was published, which was able to predict a longer period of hospitalization. However, the risk of death could not be predicted at the multivariate analysis³¹.

Score accuracy

The discrimination of the model developed in the present study according to the ROC curve was 0.83 (95%CI: 0.78 - 0.86). The calibration of the present score, that is, the degree of concordance between the observed mortality and the predicted risk at the H-L test (Hosmer-Lemeshow test) was r = 0.98, x² = 5.61 (p = 0.691), which indicates a good model performance. In most mortality scores, the area under the ROC curve is between 0.70 and 0.86^32,33 (Table 6).

Limitations

Our risk model was constructed and validated in a single institution. Several studies have demonstrated that the scores present a lower performance when applied to groups of patients that are different from the ones for which the score was developed²⁶. Therefore, the validation in an external population with new data from other institutions is important for the score to have broad clinical use.

As in all scores found in the literature, the present score does not present a perfect discrimination, although it is considered good (area under the ROC curve: 0.83; 95%CI: 0.78 - 0.86). It is probable that unknown mechanisms of physiopathological response to the surgery or of factors that can influence the individual reserve of each patient can contribute to the fact that the score does not have a high predictive value.

It is likely that the model will lose its calibration with the continuous improvement in medical care. This loss must be counterbalanced by recalibrating the risk index, using more recent data from new patient cohorts. The presence of PAH was not categorized in degrees of severity, which could aggregate a higher proportional risk to its increase. Perhaps this will be possible with a larger sample.

Implications

As the score is based on a clinical database, the system offers an estimate of surgical risk in the "real world". The score can be used to monitor deficiencies of the hospital facility, the multidisciplinary team (surgeon, anesthesiologist, and postoperative team) and of the surgical indication. The model presents enough accuracy to be routinely employed at Hospital São Lucas of PUC - RS and to be tested with data from other institutions.

Conclusions

The risk factors associated with the occurrence of in-hospital death after cardiac valve surgery were: age > 60 years, surgical priority, female sex, ejection fraction (EF) < 45%, concomitant cardiac revascularization surgery, pulmonary hypertension, NYHA functional class III or IV and high creatinine levels. Based on the identified variables that were predictors of in-hospital mortality, a risk score was constructed that classified the patients as presenting low, medium, high, very high and extremely high preoperative risk.

Potential Conflict of Interest

No potential conflict of interest relevant to this article was reported.

Sources of Funding

There were no external funding sources for this study.

Study Association

This article is part of the thesis of doctoral submitted by João Carlos Vieira da Costa Guaragna, from Hospital São Lucas da PUCRS.

References

• 1. Rabkin E, Schoen FJ. Cardiovascular tissue engineering. Cardiovasc Pathol. 2002;11 (6): 305-17.
• 2. Brandão CMA. Avaliação do risco em cirurgia cardíaca valvar. In: Grinberg M, Sampaio RO. (editores). Doença valvar. Barueri: Manole; 2006. p. 199-201.
• 3. Ambler G, Omar RZ, Royston P, Kinsman R, Keogh BE, Taylor KM. Generic simple risk stratification model for heart valve surgery. Circulation. 2005; 112 (2): 224-31.
• 4. De Bacco MW, Sant'Anna JRM, De Bacco G, Sant'Anna RT, Santos MF, Pereira E, et al. Fatores de risco hospitalar para implante de bioprótese valvar de pericárdio bovino. Arq Bras Cardiol. 2007; 89 (2): 125-30.
• 5. Brandrup-Wognsen G, Berggren H, Hartford M, Hjalmarson A, Karlsson T, Herlitz J. Female sex is associated with increased mortality and morbity early, but no late, after coronary artery bypass grafting. Eur Heart J. 1996; 17 (9): 1426-31.
• 6. Risum O, Abddelnoor M, Nitter-Hauges, Levorstad K, Svennevig JL. Coronary artery bypass surgery in women and in men; early and long-term results: a study of the Norwegian population adjusted by age and sex. Eur J Cardiothorac Surg. 1997; 11 (3): 539-46.
• 7. Findlay IN. Coronary bypass surgery in women. Curr Opin Cardiol. 1994; 9 (6): 650-7.
• 8. Cohen A, Katz M, Katz R, Hauptman E, Schachner A. Chronic obstructive pulmonary disease in patients undergoing coronary artery bypass grafting. J Thorac Cardiovasc Surg. 1995; 109 (3): 574-81.
• 9. Samuels LE, Kaufman MS, Morris RJ, Promisloff R, Brockman SK. Coronary artery bypass grafting in patients with COPD. Chest. 1998; 113 (4): 878-82.
• 10. Vincens JJ, Temizer D, Post JR, Edmunds LH Jr, Herrmann HC. Long-term outcome of cardiac surgery in patients with mitral stenosis and severe pulmonary hypertension. Circulation. 1995; 92 (Suppl 9:II): 137-42.
• 11. Anderson RJ, O'Brien M, Mawhinney S, VillaNueva CB, Moritz TE, Sethi GK, et al. Mild renal failure is associated with adverse outcome after cardiac valve surgery Am J Kidney Dis. 2000; 35 (6): 1127-34.
• 12. Nowicki ER, Birkmeyer NJO, Weintranb RW, Leavitt BJ, Sanders JH, Dacey LJ, et al. Multivariable prediction of in-hospital mortality associated with aortic and mitral valve surgery in Northern New England. Ann Thorac Surg. 2004; 77 (6): 1966-77.
• 13. Parsonnet V, Dean D, Bernstein AD. A method of uniform stratification of risk for evaluating the results of surgery in acquired adult heart disease. Circulation. 1989; 79 (6 Pt 2): I3-12.
• 14. Florath I, Rosendhal UP, Mortasawi A, Bauer SF, Dalladaku F, Ennker IC. Current determinants of operative mortality in 1400 patients requiring aortic valve replacement. Ann Thorac Surg. 2003; 76 (1): 75-83.
• 15. De Bacco G, De Bacco MW, Sant'anna JRM, Santos MF, Sant'anna RT, Prates PR, et al. Aplicabilidade do escore de risco de Ambler para pacientes com substituição valvar por bioprótese de pericárdio bovino. Rev Bras Cir Cardiovasc. 2008; 23 (3): 336-43.
• 16. Vogt A, Grube E, Glunz HG, Hauptmann KE, Sechtem U, Maurer W, et al. Determinants of mortality after cardiac surgery: results of the Registry of de Arbeitsgemelinshaft Leitender Kardiologischer Krankengausärz.(ALKK) on 10.525 patients. Eur Heart J. 2000; 21 (1): 28-32.
• 17. Delahaye F, Célard M, Roth O, de Gevigney G. Indications and optimal timing for surgery in infective endocarditis. Heart. 2004; 90 (6): 618-20.
• 18. Revilla A, López J, Vilacosta I, Villacorta E, Rollan MJ, Echevarria R, et al. Clinical and prognostic profile of patients with infective endocarditis who need urgent surgery. Eur Heart J. 2007; 28 (1): 65-71.
• 19. Richardson JV, Karp RB, Kirklin JW, Dismukes WE. Treatment of infective endocarditis: a ten-year comparative analysis. Circulation. 1978; 58 (4): 589-97.
• 20. Hasbun R, Vikran HR, Barakat LA, Bueconsejo J, Quagliarello VJ. Complicated left-sided native valve endocarditis in adults: risk classification for mortality. JAMA. 2003; 289 (15): 1933-40.
• 21. Méndez FJM. Estratificación del riesgo en cirugía cardíaca. Arch Cardiol Mex. 2002; 72 (Suppl I): 141-7.
• 22. Guaragna JCVC. Cirurgia cardíaca e hipertensão arterial no pós-operatório imediato: fatores pré e transoperatórios. [Dissertação]. Porto Alegre: Pontificia Universidade Católica do Rio Grande do Sul; 1999.
• 23. Zingone B, Pappalardo A, Dreas L. Logistic versus additive EuroSCORE. A comparative assessment of the two models in an independent population sample. Eur J Cardiothorac Surg. 2004; 26 (6): 1134-40.
• 24. Roques F, Nashef SAM, Michel P, Gauducheau E, de Vincentilis C, Baudet E, et al. Risk factors and outcome in European cardiac surgery: analysis of the EuroSCORE multinational database of 19.030 patients. Eur J Cardiothorac Surg. 1999; 15 (6): 816-23.
• 25. Hannan EL, Racz MJ, Jones RH, Gold JP, Ryan TJ, Hafner JP, et al. Predictors of mortality for patients undergoing cardiac valve replacements in New York State. Ann Thorac Surg. 2000; 70 (4): 1212-8.
• 26. Shahian DM, Blackstone EH, Edwards FH. Cardiac surgery risk models: a position article. Ann Thorac Surg. 2004; 78 (5): 1868-77.
• 27. Ribeiro ALP, Gagliardi SPL, Nogueira JLS, Silveira LM, Colosimo EA, Lopes do Nascimento CA. Mortality related to cardiac surgery in Brazil, 2000 -2003. J Thorac Cardiovasc Surg. 2006; 131 (4): 907-9.
• 28. David TE. Should cardiac surgery be performed in low-volume hospitals. J Thorac Cardiovasc Surg. 2006; 131 (4): 773-4.
• 29. Pons JMV, Granados A, Espinas JA, Borras JM, Martin I, Moreno V. Assessing open heart surgery mortality in Catalonia (Spain) trhough a predictive risk model. Eur J Cardiothorac Surg. 1997; 11 (3): 415-23.
• 30. Brandão CMA, Pomerantzeff PMA, Cunha CR, Morales IIE, Puig LB, Grinberg M, et al. Substituição valvar com próteses mecânicas de duplo folheto. Rev Bras Cir Cardiovasc. 2000; 15 (3): 227-33.
• 31. Grinberg M, Jonke VM, Sampaio RO, Spina G, Tarasoutchi F. Validação de um novo escore de risco cirúrgico para cirurgia valvar. Arq Bras Cardiol (online). 2009; 92 (4): 320-5.
• 32. Kurki RS. Prediction of outcome in cardiac surgery. Mt Sinai J Med. 2002; 69 (1-2): 68-2.
• 33. Dupuis J-Y. Predicting outcomes in cardiac surgery: risk stratification matters? Curr Opin Cardiol. 2008; 23 (6): 560-7.

Publication Dates

• Publication in this collection
  23 Apr 2010
• Date of issue
  Apr 2010

History

• Reviewed
  21 Oct 2009
• Received
  15 Apr 2009
• Accepted
  24 Nov 2009