Hematological Inflammatory Gradient Score (HIGS): A Novel Predictor of Early Mortality After Coronary Artery Bypass...

Early in-hospital mortality remains an important concern after coronary artery bypass grafting. Existing risk scores, such as EuroSCORE and STS, rely mainly on demographic and clinical parameters and do not adequately incorporate routine hematological markers. This study aimed to develop and validate the Hematological Inflammatory Gradient Score (HIGS), a novel model derived from routinely available hematological indices, to predict early postoperative mortality after coronary artery bypass grafting (CABG).

A retrospective, single-center cohort of 202 patients undergoing elective isolated CABG between January 2022 and March 2024 was analyzed. HIGS was calculated using standardized z-scores of red cell distribution width (RDW), platelet distribution width (PDW), and immature granulocyte percentage (IG%). Discrimination was assessed with ROC curve analysis, while logistic regression identified independent predictors of mortality.

In-hospital mortality occurred in 10.9% (22/202) of patients. Compared with survivors, non-survivors had significantly higher HIGS values (1.02 ± 0.74 vs -0.12 ± 0.43, p < 0.001). HIGS demonstrated the highest discriminative ability for mortality prediction among tested parameters (AUC = 0.862, 95% CI: 0.794-0.931), with 86.4% sensitivity and 78.9% specificity at the optimal cut-off (>0.44). When added to the base model consisting of age, ejection fraction, and urea, HIGS provided a modest improvement in discrimination (AUC increase from 0.639 to 0.665). In multivariate analysis, lower ejection fraction, higher IG%, and elevated urea were independent predictors of mortality, and inclusion of HIGS improved model performance.

HIGS is a simple, inexpensive, and biologically plausible score derived from routine blood tests that reliably stratifies early mortality risk after CABG. If confirmed in larger, prospective multicenter studies, HIGS may serve as a practical adjunct to conventional risk models in perioperative decision-making. Given the retrospective, single-center design and limited event count, these findings should be interpreted cautiously, and external validation is required.