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Life sciences · Observational Study
ClinicalTrials.gov · September 30, 2026
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Observational Study.
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Registry record from ClinicalTrials.gov (NCT07850492). This is a study registration, not published results. Lead sponsor: Bach Mai Hospital. Recruitment status: NOT_YET_RECRUITING. Study type: OBSERVATIONAL. Enrollment: 650 participants (ESTIMATED). Conditions: Myocardial Infarction (MI), Perioperative Myocardial Injury, Sepsis, Digestive System Surgical Procedures, Acute Kidney Injury, Postoperative Complication, Heart Injuries. Interventions: DIAGNOSTIC_TEST: High-sensitivity cardiac troponin T (hs-cTnT) surveillance. Primary outcome measures: Incidence of Perioperative Myocardial Injury (PMI) , Within 48 hours postoperatively; All-Cause Mortality , Within 30 days after surgery. Brief summary: Major abdominal surgery (MAS) carries a high risk of 30-day mortality (\>5%). Perioperative myocardial injury (PMI) and myocardial injury after noncardiac surgery (MINS) frequently develop asymptomatically due to surgical analgesia. The 2022 European Society of Cardiology (ESC) Guidelines recommend routine surveillance with high-sensitivity cardiac troponin (hs-cTn) before and after surgery in high-risk patients, defining PMI by an absolute increase in hs-cTnT \>= 14 ng/L or a peak \> 5x ULN (\> 70 ng/L). However, validation in Asian populations and major abdominal visceral resections is scarce. This prospective observational cohort study (TROP-MAS) recruits \>= 650 consecutive high-cardiovascular-risk patients undergoing major abdominal surgery at Bach Mai Hospital (Hanoi, Vietnam). The study systematically evaluates the incidence and prognostic significance of hs-cTnT kinetics (T0 baseline, T1 24h, T2 48h postoperative) regarding 30-day all-cause mortality, major adverse cardiovascular events (MACE), and severe noncardiac complications (acute kidney injury, sepsis, anastomotic leak, bowel ischemia). Furthermore, it derives and validates population-specific optimal prognostic cut-off values using ROC curve analysis and multivariable Cox modeling.