Trajectory phenotypes of post-induction hypotension based on multicenter cohort analysis: indicating patients’ prognosis and the contributing factors

Background: Post-induction hypotension (PIH) affects 20–40% of surgical patients and is revealed to be associated with adverse outcomes. However, current understanding of PIH and its clinical implications remains limited by a potentially oversimplified description of these longitudinal and complex hemodynamic changes. Methods: We conducted a multicenter, large sample study with the INSPIRE cohort and two Chinese cohorts. Heterogeneous linear mixed effects models were applied to determine the distinct PIH trajectory phenotypes among patients. Relationships among trajectory patterns and postoperative complications were analyzed with the hierarchical logistic regression model by adjusting multiple confounding factors. A trajectory prediction model and a classification model were developed and validated based on the XGBboost model, and the contributing factors were explored with the SHAP (SHapley Additive exPlanations) value approach. Results: With 169 470 patients included in analysis, four distinct PIH trajectory phenotypes were identified and validated: Gradual Decline (66.0%), Sharp Drop (22.9%), Drop and Recovery (8.0%), and Overshoot and Rise (4.1%). The Sharp Drop and Overshoot and Rise phenotypes demonstrated significantly higher risks for adverse outcomes compared to non-PIH patients, including prolonged ICU stay [odds ratio (OR) 2.24 and 2.71], mechanical ventilation (OR 2.47 and 2.94), and postoperative pulmonary complications (OR 1.63 for both). The PIH trajectory prediction models were shown with a macro-average AUROC of 0.8240 [95% confidence interval (CI) 0.7699–0.8927] by the internal validation. ASA (American Society of Anesthesiologists) classification, age, anesthetic agents, body mass index (BMI), coexisting hypertension, and vasopressor were found to be the key determinants of trajectory patterns. We also developed an open-source computerized platform to facilitate the local implementation and validation of PIH trajectory prediction and classification. Conclusions: This study identifies and validates four distinct PIH trajectory phenotypes with different prognostic implications and contributing factors, providing new insights for optimal targets of perioperative hemodynamic management. We propose a methodological framework for blood pressure trajectory analysis that can be adapted to different clinical settings.