Risk stratification of perioperative myocardial infarction/injury following noncardiac surgery in high risk patients. (3rd October 2022) - Puelacher, C; Gualandro, D; Glarner, N; Lurati Buse, G; Lampart, A; Bolliger, D; Steiner, L; Gerhard, H; Clerc, O; Kindler, C; Cardozo, F A; Caramelli, B; et al.
Abstract: Background: Perioperative myocardial infarction/injury (PMI) screening is increasingly recommended by current guidelines. PMI is often caused by type 2 myocardial infarction, and risk stratification tools for these patients are lacking. Purpose: To derive and internally validate a risk prognostication model for PMI of likely type 2 infarction (lT2MI) Methods: We included consecutive high-risk patients undergoing noncardiac surgery into this prospective multicenter study. Patients received a systematic routine PMI screening with pre- and postoperative measurement of cardiac troponin (cTn). PMI was prospectively defined as an absolute increase of cTn above the preoperative value. PMI etiology was centrally adjudicated and hierarchically classified by 2 independent physicians based on all clinical information obtained during index hospitalization and selected those with lT2MI for further analyses. To identify risk factors and allow risk stratification in lT2MI, we prespecified that only perioperative variables should be included into the model. We constructed a logistic binary regression model for major adverse cardiac events (MACE) within 120 days, including variables available at time of clinical evaluation: additional symptoms or ECG-criteria required according to the Universal Definition of Myocardial Infarction, absolute increase in cTn (categorized according to level of absolute increase 1–<2 times the 99th percentile, ≥2 to <4 times, and ≥4 times), urgency of procedure, perioperative bleeding (drop in haemoglobin >30g/L or deemed relevant for PMI by adjudicator), and ESC/ESA surgery risk (low, medium, high risk of cardiac events). Variables were omitted from the final model if the p-value was >0.05. Variable levels with similar odds ratios were grouped for simplification of the prognostic model. We constructed a calibration plot and calculated the area under the receiver-operating characteristics curve (AUC) and Brier Score. For internal validation we calculated the predicted probabilities and classified patients into low-risk (predicted event rate <10%), intermediate range (10–20%), and high-risk (>20%), and compared the predicted with the observed event rate. Results: PMI occurred in 1016/7754 patients (13.1%) of which 750/1016 (73.8%) were adjudicated as lT2MI. MACE within 120 days occurred in 118/750 (15.7%) patients. The initial and final logistic prognostic model for 120-day MACE or death is shown in the table. Internal validation found a good fit of predicted and observed event rate following bootstrapping of 1000 iterations (Figure 1), a good AUC of 0.71 and a Brier score of 0.12. Conclusion: The derived risk prognostication model for PMI of lT2MI can aid in the stratification of patients and support clinical decision making following noncardiac surgery. Funding Acknowledgement: Type of funding sources: Foundation. Main funding source(s): Swiss National Science FoundationSwiss Heart Foundation