Preoperative sarcopenia as a predictor of post-induction hypotension in elderly patients undergoing cardiac surgery - A Retrospective Cohort Study

Purpose The effect of sarcopenia on post-induction hypotension(PIH) of cardiac surgery remains unclear. In this study, we aimed to assess whether preoperative sarcopenia could be used to predict the outcome of post-induction hypotension in elderly cardiac surgery patients. Methods 212 elderly patients undergoing elective open heart surgery who had an abdominal CT scan within 120 days before surgery. The primary predictor was Sarcopenia, defined as an age-related loss of skeletal muscle mass plus loss of muscle strength and/or reduced physical performance. The primary outcome was the incidence of post-induction hypotension. The univariate and multivariate regression analyses were used to identify risk factors for PIH. Results Sixteen percent of the patients (n = 34) had sarcopenia before cardiac surgery. The sarcopenic group had significantly lower BMI, hemoglobin levels, and base MAP, and higher ASA class, NT-ProBNP concentration, PIH, and major morbidity compared to non-sarcopenic individuals. In multivariate analysis, age (aOR = 1.07, 95% CI1.00-1.14; p = 0.036), ASA class(aOR = 2.15,95% CI 1.02–4.52; p = 0.043), Base MAP (aOR = 0.97,95% CI 0.94-1.00; p = 0.042) and sarcopenia (aOR 4.78, 95% CI 1.90-12.06; p = 0.001) were associated with PIH. The area under the ROC curve (95% CI) for sarcopenia score, ASA class, and both predicting PIH were 0.729(0.672-0781), 0.649(0.589–0.706), and 0.758(0.702–0.808), respectively. The difference in AUC between the ASA class and both combined was statistically significant (Z = 4.27, P < 0.001) and a statistically significant difference in AUC between sarcopenia and both combined (Z = 2.30, P = 0.021). Conclusions Sarcopenia is an independent predictor of post-induction hypotension of cardiac surgery in the elderly and can be used for preoperative risk assessment. In addition, its combination with traditional risk assessment tools (ASA classification) can improve the predictive accuracy of PIH.