Algorithm for preoperative risk assessment in patients with carbohydrate metabolism disorders

Objective . Development of a comprehensive algorithm for preoperative risk assessment in patients with carbohydrate metabolism disorders. Materials and methods . The study included patients aged 35–75 years with carbohydrate metabolism disorders scheduled for elective surgery. Acute inflammation, oncopathology and severe heart failure were excluded. Clinical, laboratory parameters and markers of endothelial dysfunction were assessed. Statistical analysis was performed in SPSS 23.0. Results . A comprehensive algorithm for preoperative risk assessment in patients with carbohydrate metabolism disorders has been developed. A total of 150 patients were examined (45 – with type 2 diabetes mellitus, 35 – with impaired glucose tolerance, 70 – with normoglycemia). The main risk factors for complications were identified: increased body mass index, arterial hypertension, dyslipidemia, and increased C-reactive protein. Patients with carbohydrate metabolism disorders had a significant increase in the PAI-1 level and the number of desquamated endothelial cells. The average PAI-1 level in the diabetes group was 39.2 ± 7.8 ng/ml, which was significantly higher than in individuals with normoglycemia (22.7 ± 6.5 ng/ml, p < 0.001). The number of desquamated endothelial cells was also significantly increased: 9,1 ± 2,3 cells/μl in diabetes mellitus versus 4,6 ± 1,8 cells/μl in the control group (p < 0,01). Correlation analysis showed a strong positive relationship between HbA1c and PAI-1 (r=0.68, p<0.001). Multivariate regression analysis confirmed that carbohydrate metabolism disorders are an independent predictor of increased markers of endothelial dysfunction (β=0.41, p<0.01 for PAI-1). Conclusions . A comprehensive algorithm for preoperative risk assessment in patients with carbohydrate metabolism disorders has been developed, taking into account traditional factors and markers of endothelial dysfunction. The algorithm provides a structured approach to risk assessment and management of patients in the perioperative period. The inclusion of PAI-1 and desquamated endothelial cells improved the accuracy of complication prediction by 15%. Validation showed high sensitivity (85%) and specificity (78%). The correlation of PAI-1 with cardiovascular risk factors was revealed.