Abstract
Existing risk-scoring systems for cardiac surgery include only standard preoperative factors, without considering nutritional and inflammatory status or intraoperative factors. The objective of this study was to develop a comprehensive prediction model for mortality incorporating nutritional, inflammatory, and perioperative factors in patients undergoing valvular heart surgery. In this retrospective review of 2,046 patients who underwent valvular heart surgery, Cox and LASSO regression analyses were performed to identify independent prognostic factors of 1-year postoperative mortality among various perioperative factors known to affect prognosis, including objective nutritional and inflammatory indices. A novel nomogram model incorporating selected prognostic factors was developed, and its discrimination ability was evaluated using the C-index. The model was validated in internal and external cohorts. The 1-year mortality rate after valvular heart surgery was 5.1% (105 of 2,046 patients) and was significantly associated with several preoperative objective inflammatory and nutritional indices. Cox and LASSO analyses identified the following five independent prognostic factors for mortality: monocyte-to-lymphocyte ratio (an objective inflammatory index), EuroSCORE II, Controlling Nutritional Status score, cardiopulmonary bypass time, and number of erythrocyte units transfused intraoperatively. Our nomogram model incorporating these five factors had a C-index of 0.834 (95% CI 0.791-0.877), which was higher than that of EuroSCORE II alone (0.744, 95% CI 0.697-0.791) (P<0.001). The nomogram achieved good discrimination ability, with C-indices of 0.836 (95% CI 0.790-0.878) and 0.727 (95% CI 0.651-0.803) in the internal and external validation cohorts, respectively, and showed well-fitted calibration curves. A nomogram model incorporating five inflammatory, nutritional, and perioperative factors, as well as EuroSCORE II, was a better predictor of 1-year mortality after valvular heart surgery than EuroSCORE II alone, with good discrimination and calibration power for predicting mortality in both internal and external validation cohorts.
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