Myocardial Fibrosis in Hypertensive Heart Disease: Correlation with Serum Biomarkers and Perioperative Cardiovascular Outcome

Myocardial fibrosis is a common but underdiagnosed complication of hypertensive heart disease (HHD). It contributes significantly to adverse perioperative cardiovascular outcomes. Cardiac MRI remains the gold standard for detection; however, accessibility is limited in low-resource settings, such as Pakistan. This study aimed to assess the correlation between serum biomarkers—NT-proBNP, high-sensitivity troponin T (hsTnT), and galectin 3—and MRI-detected myocardial fibrosis, and to evaluate their predictive value for perioperative complications. Methods: A prospective observational study was conducted at a tertiary care hospital in Multan between November 2022 and September 2024. A total of 84 hypertensive patients undergoing elective non-cardiac surgery were enrolled. Cardiac MRI was used to detect myocardial fibrosis. Serum levels of NT-proBNP, hs-TnT, and galectin-3 were measured preoperatively. Patients were monitored for perioperative cardiovascular events. Statistical analyses included t-tests, chi-square tests, and Pearson correlation. Results: MRI-confirmed myocardial fibrosis was present in 37 patients (44.0%). Patients with fibrosis had significantly higher mean levels of NT proBNP (583.2 ± 104.7 pg/mL vs 321.5 ± 89.3 pg/mL, p < 0.001), hs TnT (0.032 ± 0.010 ng/mL vs 0.019 ± 0.008 ng/mL, p < 0.001), and galectin 3 (19.7 ± 3.6 ng/mL vs 13.5 ± 2.8 ng/mL, p < 0.001). The incidence of perioperative cardiovascular complications was significantly higher in the fibrosis group (32.4% vs 9.6%, p = 0.005). Positive correlations were observed between biomarker levels and the extent of myocardial fibrosis. Conclusion: Serum biomarkers NT proBNP, hs TnT, and galectin three correlate strongly with MRI-detected myocardial fibrosis and are predictive of perioperative cardiovascular complications in hypertensive patients. These markers may serve as effective, low-cost alternatives for preoperative risk stratification in resource-limited settings.