Predicting major adverse events after cardiac surgery in children

Michael D Seear, Jennifer C Scarfe, Jacques G LeBlanc
Pediatric Critical Care Medicine 2008, 9 (6): 606-11

OBJECTIVES: To develop a reliable predictor of major adverse events after pediatric cardiac surgery, with the aim of reducing mortality of cardiac extracorporeal life support through earlier, more accurate patient selection.

DESIGN: Prospective observational study.

SETTING: Tertiary level pediatric intensive care unit.

PATIENTS: Fifty-two children undergoing open heart surgery considered above-average risk based on preoperative assessment.

INTERVENTIONS: None; strictly observational study.

MEASUREMENTS AND MAIN RESULTS: A wide range of measurements was made at 3, 6, 9, 12, and 24 hrs after surgery, including: oxygen consumption, central venous pressure and oxygen saturation (Scvo2), cardiac output (Fick), heart rate, arterial pressure, arterial lactate, urine output, core-toe temperature gradient, and derived hemodynamic variables. Six children had major adverse events; three needed extracorporeal life support, two died. There were no correlations between routine postoperative measurements (blood pressure, pulse, temperature gradient, central venous pressure) and any measure of cardiac function, and neither group of variables predicted adverse outcomes. Lactate (>8 mmol/L) and Scvo2 (<40%) had high sensitivity (both 73.7%) and specificity (96.3% and 95.4%, respectively), for predicting major adverse event but positive predictive values for both were low (63.6% and 58.3%, respectively). The ratio of the two had better predictive power than the individual values. When the ratio (Scvo2, %)/(lactate, mmol/L) fell below 5, the positive predictive value for major adverse event was 93.8% (sensitivity 78.9%, specificity 90.5%). The effect was present at all postoperative time points.

CONCLUSIONS: Lactate and Scvo2 are the only postoperative measurements with predictive power for major adverse events. Forming a ratio of the two (Scvo2/lactate), seems to improve predictive power, presumably by combining their individual predictive strengths. Both measures have excellent specificities but lower sensitivities. Predictive power of single measures is only fair but can be improved, in high risk patients, by monitoring repeated measures over time.

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