Association between postoperative troponin levels and 30-day mortality among patients undergoing noncardiac surgery.

CONTEXT Of the 200 million adults worldwide who undergo noncardiac surgery each year, more than 1 million will die within 30 days. OBJECTIVE To determine the relationship between the peak fourth-generation troponin T (TnT) measurement in the first 3 days after noncardiac surgery and 30-day mortality. DESIGN, SETTING, AND PARTICIPANTS A prospective, international cohort study that enrolled patients from August 6, 2007, to January 11, 2011. Eligible patients were aged 45 years and older and required at least an overnight hospital admission after having noncardiac surgery. MAIN OUTCOME MEASURES Patients' TnT levels were measured 6 to 12 hours after surgery and on days 1, 2, and 3 after surgery. We undertook Cox regression analysis in which the dependent variable was mortality until 30 days after surgery, and the independent variables included 24 preoperative variables. We repeated this analysis, adding the peak TnT measurement during the first 3 postoperative days as an independent variable and used a minimum P value approach to determine if there were TnT thresholds that independently altered patients' risk of death. RESULTS A total of 15,133 patients were included in this study. The 30-day mortality rate was 1.9% (95% CI, 1.7%-2.1%). Multivariable analysis demonstrated that peak TnT values of at least 0.02 ng/mL, occurring in 11.6% of patients, were associated with higher 30-day mortality compared with the reference group (peak TnT ≤ 0.01 ng/mL): peak TnT of 0.02 ng/mL (adjusted hazard ratio [aHR], 2.41; 95% CI, 1.33-3.77); 0.03 to 0.29 ng/mL (aHR, 5.00; 95% CI, 3.72-6.76); and 0.30 ng/mL or greater (aHR, 10.48; 95% CI, 6.25-16.62). Patients with a peak TnT value of 0.01 ng/mL or less, 0.02, 0.03-0.29, and 0.30 or greater had 30-day mortality rates of 1.0%, 4.0%, 9.3%, and 16.9%, respectively. Peak TnT measurement added incremental prognostic value to discriminate those likely to die within 30 days for the model with peak TnT measurement vs without (C index = 0.85 vs 0.81; difference, 0.4; 95% CI, 0.2-0.5; P < .001 for difference between C index values). The net reclassification improvement with TnT was 25.0% (P < .001). CONCLUSION Among patients undergoing noncardiac surgery, the peak postoperative TnT measurement during the first 3 days after surgery was significantly associated with 30-day mortality.

[1]  Denis Xavier,et al.  Characteristics and Short-Term Prognosis of Perioperative Myocardial Infarction in Patients Undergoing Noncardiac Surgery , 2011, Annals of Internal Medicine.

[2]  G. Guyatt,et al.  Prognostic Value of Troponin and Creatine Kinase Muscle and Brain Isoenzyme Measurement after Noncardiac Surgery: A Systematic Review and Meta-analysis , 2011, Anesthesiology.

[3]  J. Eikelboom,et al.  Major Vascular Complications in Patients Undergoing Non‐Cardiac Surgery: Magnitude of the Problem, Risk Prediction, Surveillance, and Prevention , 2010 .

[4]  D. Wijeysundera,et al.  Systematic Review: Prediction of Perioperative Cardiac Complications and Mortality by the Revised Cardiac Risk Index , 2010, Annals of Internal Medicine.

[5]  J. Coresh,et al.  Definition and Classification of Stages of Chronic Kidney Disease: Screening for Chronic Kidney Disease , 2010 .

[6]  C. Schmid,et al.  A new equation to estimate glomerular filtration rate. , 2009, Annals of internal medicine.

[7]  Edward J. Mills,et al.  Primary prevention of cardiovascular mortality and events with statin treatments: a network meta-analysis involving more than 65,000 patients. , 2008, Journal of the American College of Cardiology.

[8]  W. Berry,et al.  An estimation of the global volume of surgery: a modelling strategy based on available data , 2008, The Lancet.

[9]  Denis Xavier,et al.  Effects of extended-release metoprolol succinate in patients undergoing non-cardiac surgery (POISE trial): a randomised controlled trial , 2008, The Lancet.

[10]  M. Pencina,et al.  Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond , 2008, Statistics in medicine.

[11]  J. Alpert,et al.  Joint ESC/ACCF/AHA/WHF Task Force for the Redefinition of Myocardial Infarction , 2008 .

[12]  W. Kremers,et al.  Concordance for Survival Time Data: Fixed and Time-Dependent Covariates and Possible Ties in Predictor and Time , 2007 .

[13]  Fred S Apple,et al.  Universal definition of myocardial infarction. , 2007, Journal of the American College of Cardiology.

[14]  A. Perel,et al.  Early and Delayed Myocardial Infarction after Abdominal Aortic Surgery , 2005, Anesthesiology.

[15]  Michael L. Johnson,et al.  Mortality After Noncardiac Surgery: Prediction From Administrative Versus Clinical Data , 2005, Medical care.

[16]  A. Wu,et al.  Evaluation of imprecision for cardiac troponin assays at low-range concentrations. , 2004, Clinical chemistry.

[17]  M. Blaser,et al.  Population attributable risks of esophageal and gastric cancers. , 2003, Journal of the National Cancer Institute.

[18]  G. Godet,et al.  Cardiac troponin I predicts short-term mortality in vascular surgery patients. , 2003, Circulation.

[19]  Madhu Mazumdar,et al.  Methods for categorizing a prognostic variable in a multivariable setting , 2003, Statistics in medicine.

[20]  B. Lindahl,et al.  Troponin T levels in patients with acute coronary syndromes, with or without renal dysfunction , 2002 .

[21]  Philip Hougaard,et al.  Analysis of Multivariate Survival Data , 2001 .

[22]  J. Habbema,et al.  Prognostic Modeling with Logistic Regression Analysis , 2001, Medical decision making : an international journal of the Society for Medical Decision Making.

[23]  A. Jaffe,et al.  It's time for a change to a troponin standard. , 2000, Circulation.

[24]  S R Lipsitz,et al.  A Global Goodness‐of‐Fit Statistic for Cox Regression Models , 1999, Biometrics.

[25]  D. Hosmer,et al.  A Simplified Method of Calculating an Overall Goodness-of-Fit Test for the Cox Proportional Hazards Model , 1998, Lifetime data analysis.

[26]  C. Moorehead All rights reserved , 1997 .

[27]  H. Keselman,et al.  Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variables , 1992 .

[28]  D P Byar,et al.  Estimating the population attributable risk for multiple risk factors using case-control data. , 1985, American journal of epidemiology.

[29]  John E. Shelton People's Republic of China , 1973 .