Integrating Neutrophil-To-Albumin Ratio and Triglycerides: A Novel Indicator for Predicting Spontaneous Hemorrhagic Transformation in Acute Ischemic Stroke Patients

Abstract

Background: Hemorrhagic transformation (HT) is a tragic complication of acute ischemic stroke (AIS), with spontaneous HT (sHT) occurring even without reperfusion therapies. Despite evidence suggesting that several inflammation biomarkers are closely related to HT, its utility in sHT risk stratification remains unclear. This study aimed to identify and integrate effective inflammatory biomarkers associated with sHT and to develop a novel nomogram model for the early detection of sHT.

Methods: We conducted a retrospective observational cohort study of AIS patients receiving conventional medical treatment solely from March 2022 to March 2023, using a prospectively maintained database. All patients underwent CT follow-up within 7 days after admission, with sHT occurrence within this period as the outcome. Data on demographics, clinical information, laboratory results, and imaging were collected. The cohort was divided into training and validation sets (7:3). Least absolute shrinkage and selection operator (LASSO) regression selected inflammatory biomarkers for a novel index. Univariable and multivariable logistic regressions were conducted to identify independent sHT risk factors. Receiver operating characteristic (ROC) analysis determined optimal cut-off values for continuous factors. A nomogram was developed and validated internally and externally. Predictive accuracy was assessed using the area under the ROC curve (AUC) and calibration plots. Decision curve analysis (DCA) evaluated clinical usefulness.

Results: Of 803 AIS patients, 325 were included in the final analysis. sHT was found in 9.5% (31 patients). Training (n = 228) and validation (n = 97) cohorts showed no significant demographic or clinical differences. LASSO regression integrated neutrophil-to-albumin ratio (NAR) and triglycerides (TGs) into a novel index-NATG. Independent sHT risk factors included baseline National Institute of Health Stroke Scale (NIHSS) (OR = 1.09, 95% CI (1.02, 1.16), p = 0.0095), NATG (OR = 1534.87, 95% CI (5.02, 469638.44), p = 0.0120), D-dimer (DD) (OR = 1.12, 95% CI (1.01, 1.25), p = 0.0249), and total cholesterol (TC) (OR = 1.01, 95% CI (1.00, 1.01), p = 0.0280), with their respective optimal cut-off values being 13, 0.059, 0.86, and 3.6. These factors were used to develop the nomogram in the training cohort, which achieved an AUC of 0.804 (95% CI, 0.643-0.918) in the training cohort and 0.713 (95% CI, 0.499-0.868) in the validation cohort, demonstrating consistent calibration. DCA confirmed the nomogram's clinical applicability in both cohorts.

Conclusions: A novel indicator combining NAR and TG is positively associated with sHT in AIS patients. The constructed nomogram, integrating this novel indicator with other risk factors, provides a valuable tool for identifying sHT risk, aiding in clinical decision-making.

Keywords: AIS; NAR; NATG; spontaneous hemorrhagic transformation (sHT); triglycerides (TGs).

FIGURE 1

Flow diagram of patients in this study. AIS = acute ischemic stroke.

FIGURE 2

LASSO regression was used to select potential factors and develop a novel laboratory index, while ROC curves were used to evaluate the predictive value and determine the optimal cut‐off value of independent risk factors. (A) Partial likelihood deviance curve (binomial deviance). (B) Coefficient profile plot. (C) ROC curves. (D) Optimal cut‐off value. AUC = area under the ROC curve; DD=D‐dimer; LASSO = Least absolute shrinkage and selection operator; NATG = NAR–triglyceride ratio; NIHSS=National Institute of Health Stroke Scale; ROC = receiver operating characteristic; TC = total cholesterol.

FIGURE 3

The novel indicator integrating NATG and other clinical features was developed into nomogram. NATG = NAR–triglyceride ratio; NIHSS = National Institute of Health Stroke Scale; TOAST = Trial of ORG 10172 in Acute Stroke Treatment.

FIGURE 4

The performance of the nomogram model. (A, B) The training cohort. (C, D) The validation cohort. (A, C) Calibration curves suggest that our nomogram performed well in both derivations. (B, D) The DCA curves show that our nomogram had good practicability in both internal and external validation. AUC = area under the ROC curve; DCA = decision curve analysis; ROC = Receiver operating characteristic.