title: Mortality prediction after cardiac surgery: blood lactate is indispensible.
abstract: BACKGROUND
  Blood lactate is accepted as a mortality risk marker in intensive care units (ICUs), especially after cardiac surgery. Unfortunately, most of the commonly used ICU risk stratification scoring systems did not include blood lactate as a variable. We hypothesized that blood lactate alone can predict the risk of mortality after cardiac surgery with an accuracy that is comparable to those of other complex models. We therefore evaluated its accuracy at mortality prediction and compared it with that of other widely used complex scoring models statistically.
  
  
  METHODS
  We prospectively collected data of all consecutive adult patients who underwent cardiac surgery between January 1, 2007, and December 31, 2009. By using χ2 statistics, a blood lactate-based scale (LacScale) with only four cutoff points was constructed in a developmental set of patients (January 1, 2007, and May 31, 2008). LacScale included five categories: 0 (≤ 1.7 mmol/L); 1 (1.8-5.9 mmol/L), 2 (6.0-9.3 mmol/L), 3 (9.4-13.3 mmol/L), and 4 (≥ 13.4 mmol/L). Its accuracy at predicting ICU mortality was evaluated in another independent subset of patients (validation set, June 1, 2008, and December 31, 2009) on both study-population level (calibration analysis, overall correct classification) and individual-patient-risk level (discrimination analysis, ROC statistics). The results were then compared with those obtained from other widely used postoperative models in cardiac surgical ICUs (Sequential Organ Failure Assessment [SOFA] score, Simplified Acute Physiology Score II [SAPS II], and Acute Physiology and Chronic Health Evaluation II [APACHE II] score).
  
  
  RESULTS
  ICU mortality was 5.8% in 4,054 patients. LacScale had a reliable calibration in the validation set (2,087 patients). It was highly accurate in predicting ICU mortality with an area under the ROC curve (area under curve [AUC]; discrimination) of 0.88. This AUC was significantly larger than that of all the other models (SOFA 0.83, SAPS II: 0.79 and APACHE II: 0.76) according to DeLong's comparison. Integrating the LacScale in those scores further improved their accuracy by increasing their AUCs (0.88, 0.81, and 0.80, respectively). This improvement was also highly significant.
  
  
  CONCLUSIONS
  Blood lactate accurately predicts mortality at both individual patient risk and patient cohort levels. Its precision is higher than that of other commonly used "complex" scoring models. The proposed LacScale is a simple and highly reliable model. It can be used (at bedside without electronic calculation) as such or integrated in other models to increase their accuracy.
authors: Cardiothoracic Surgery, CardioClinic, Cologne, NRW, Germany.
authors: Akmal M A
authors: AM
authors: Badreldin
authors: Faculty of Medicine, Friedrich-Schiller-University of Jena, Erlanger Allee 101, Jena, Germany.
authors: Fabian
authors: F
authors: Doerr
authors: Wolfson Medical School, University of Glasgow, University Avenue, Glasgow, United Kingdom.
authors: Sherif
authors: S
authors: Elsobky
authors: Department of Cardiology, Catholic Hospital Koblenz-Marienhof, Koblenz, Germany.
authors: Bernhard R
authors: BR
authors: Brehm
authors: Department of Cardiothoracic Surgery, Kasr-El-Aini School of Medicine, Cairo University, Egypt.
authors: Mohamed
authors: M
authors: Abul-dahab
authors: Department of Medical Statistics, Computer Sciences and Documentation, Friedrich-Schiller-University of Jena, Jena, Germany.
authors: Thomas
authors: T
authors: Lehmann
authors: Ole
authors: O
authors: Bayer
authors: Department of Cardiothoracic Surgery, University of Cologne, Cologne, Germany.
authors: Thorsten
authors: T
authors: Wahlers
authors: Cardiothoracic Surgery, CardioClinic, Cologne, NRW, Germany.
authors: Khosro
authors: K
authors: Hekmat
mesh_terms: D018806:APACHE; D000368:Aged; D019540:Area Under Curve; D015415:Biomarkers / Q000097:blood; D006348:Cardiac Surgical Procedures / Q000009:adverse effects / Q000401:mortality*; D016009:Chi-Square Distribution; D016002:Discriminant Analysis; D005260:Female; D006305:Health Status Indicators; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units; D019344:Lactic Acid / Q000097:blood*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016017:Odds Ratio; D063305:Organ Dysfunction Scores; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012372:ROC Curve; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D016896:Treatment Outcome; D015854:Up-Regulation
chemical_list: D015415:Biomarkers; D019344:Lactic Acid