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association of increased N terminal B-type natriuretic propeptide levels with short-term adverse outcomes after noncardiac surgery

Zurro, Carlos Álvarez; Roca, Antonio Planas; Muñoz, Enrique Alday; Hernández, Rosa Méndez

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European Journal of Anaesthesiology: February 2017 - Volume 34 - Issue 2 - p 107-108
doi: 10.1097/EJA.0000000000000554
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We read with interest the comments by Yang et al.1 and would like to thank them for their interest in our study.2

There are several risk indexes published, but the revised cardiac risk index is one of the most useful because of its simplicity and reliability. There is no doubt that adding other variables to this index, such as age or left bundle branch block (LBBB), may improve its accuracy; however, in the study by Rao et al.,3 this addition to the revised cardiac risk index was not analysed. In fact, the authors concluded that the revised cardiac risk index was a useful tool to stratify the postoperative risk in their population. Concerning the addition of other variables, they wrote: ‘We feel that its predictive accuracy increases with the addition of factors like age, general medical condition, urgency of surgery, and LBBB’.3 In our opinion, there is not enough evidence for that assumption at the present time.

The predictive performance of claims-based comorbidity scores depends on several factors, including the endpoint of a study. An acute coronary syndrome risk score may be more powerful but nonspecific to cardiovascular complications and death. P-POSSUM that appears to be better than POSSUM overpredicts mortality in low-risk groups. Therefore, a specialty-specific POSSUM, such as V-POSSUM for elective vascular procedures, has been recommended.4 The Charlson comorbidity index was first designed for 1-year mortality and finally adapted for 10-year mortality. The Deyo adaptation was developed for lumbar spinal surgery similar surgeries. Risk score indices are to a greater or lesser extent useful but can be improved with biomarkers. So, the revised cardiac risk index is still considered by many clinicians and researchers to currently be the best available cardiac-risk predictor index in noncardiac surgery and is recommended for cardiac perioperative risk stratification (Class I, Level B).5

In our study,2 the use of only one score was for methodological reasons. We found 25 events for the primary outcome in 302 patients. Results of a multivariable analysis with more than two variables [N terminal B-type natriuretic propeptide (NT-proBNP) levels and revised cardiac risk index] may be biased in both positive and negative directions. It is a general rule, based on Peduzzi et al.6 simulation, that 10 events of the outcome of interest are required for each variable in the model, including the exposure of interest. Considering potential bias in the analysis of more than two variables, we analysed type of surgery, age, sex, history of ischaemic disease, stroke, diabetes and congestive heart failure, peripheral arterial disease, hypertension, chronic renal failure and dyslipidaemia. We found that three of these factors were related to 30-day cardiovascular complications or death in the univariate analysis: congestive heart failure (P = 0.002), peripheral arterial disease (P = 0.039) and chronic renal failure (P = 0.027). None of them showed a relation with 30-day mortality in the multivariate analysis.

Finally, we also agree with Yang et al. that intraoperative bleeding, hypotension, blood transfusion, use of vasopressors, tachycardia and arterial hypertension are independently associated with postoperative myocardial injury and adverse cardiac outcomes.7 Our study focused on the preoperative identification of high-risk patients in order to implement protective perioperative interventions. There is no doubt that all these intraoperative events may worsen the outcome. Other intraoperative elements such as hypothermia should also be considered; however, it remains unclear whether these variables might be taken as confounding factors or if the incidence and duration of hypotension, and use of vasopressors or bleeding risk, for example, could be increased in patients with high levels of NT-ProBNP due to silent heart disease.

Our findings are consistent with the European Society of Cardiology/European Society of Anaesthesiology Guidelines published in 2014,5 which recommend measuring both preoperative and postoperative NT-proBNP levels for perioperative risk stratification in patients scheduled for noncardiac surgery with one alteration of functional status (less than four metabolic equivalents), or at least one factor on the revised Cardiac Risk Index in the case of vascular surgery, or two factors in the case of nonvascular surgery. Preoperative NT-proBNP measurement will improve risk stratification, whereas postoperative measurement could aid in the early diagnosis of clinically silent disease.

Acknowledgements relating to this article

Assistance with the letter: none.

Financial support and sponsorship: none.

Conflicts of interest: none.


1. Yang G-Z, Xue F-S, Liu G-P, Sun C. Association of increased N terminal B-type natriuretic propeptide levels with short-term adverse outcomes after noncardiac surgery. Eur J Anaesthesiol 2017; 34:105–107.
2. Álvarez Zurro C, Planas Roca A, Alday Muñoz E, et al. High levels of preoperative and postoperative N terminal B-type natriuretic propeptide influence mortality and cardiovascular complications after noncardiac surgery: a prospective cohort study. Eur J Anaesthesiol 2016; 33:444–449.
3. Rao JY, Yeriswamy MC, Santhosh MJ, et al. A look into Lee's score: peri-operative cardiovascular risk assessment in noncardiac surgeries-usefulness of revised cardiac risk index. Indian Heart J 2012; 64:134–138.
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5. Kristensen SD, Knuuti J, Saraste A, et al. 2014 ESC/ESA Guidelines on noncardiac surgery: cardiovascular assessment and management: The Joint Task Force on noncardiac surgery: cardiovascular assessment and management of the European Society of Cardiology (ESC) and the European Society of Anaesthesiology (ESA). Eur J Anaesthesiol 2014; 31:517–573.
6. Peduzzi P, Concato J, Kemper E, et al. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 1996; 49:1373–1379.
7. Kheterpal S, O’Reilly M, Englesbe MJ, et al. Preoperative and intraoperative predictors of cardiac adverse events after general, vascular, and urological surgery. Anesthesiology 2009; 110:58–66.
© 2017 European Society of Anaesthesiology