Establishment of a prognostic model based on the Sequential Organ Failure Assessment score for patients with first-time acute myocardial infarction.

OBJECTIVE: This study aimed to identify the prognostic factors of patients with first-time acute myocardial infarction (AMI) and to establish a nomogram for prognostic modeling. METHODS: We studied 985 patients with first-time AMI using data from the Multi-parameter Intelligent Monitoring for Intensive Care database and extracted their demographic data. Cox proportional hazards regression was used to examine outcome-related variables. We also tested a new predictive model that includes the Sequential Organ Failure Assessment (SOFA) score and compared it with the SOFA-only model. RESULTS: An older age, higher SOFA score, and higher Acute Physiology III score were risk factors for the prognosis of AMI. The risk of further cardiovascular events was 1.54-fold higher in women than in men. Patients in the cardiac surgery intensive care unit had a better prognosis than those in the coronary heart disease intensive care unit. Pressurized drug use was a protective factor and the risk of further cardiovascular events was 1.36-fold higher in nonusers. CONCLUSION: The prognosis of AMI is affected by age, the SOFA score, the Acute Physiology III score, sex, admission location, type of care unit, and vasopressin use. Our new predictive model for AMI has better performance than the SOFA model alone.

Risk stratification for mortality in cardiovascular disease survivors: A survival conditional inference tree analysis.

BACKGROUND AND AIMS: Efficient analysis strategies for complex network with cardiovascular disease (CVD) risk stratification remain lacking. We sought to identify an optimized model to study CVD prognosis using survival conditional inference tree (SCTREE), a machine-learning method. METHODS AND RESULTS: We identified 5379 new onset CVD from 2006 (baseline) to May, 2017 in the Kailuan I study including 101,510 participants (the training dataset). The second cohort composing 1,287 CVD survivors was used to validate the algorithm (the Kailuan II study, n = 57,511). All variables (e.g., age, sex, family history of CVD, metabolic risk factors, renal function indexes, heart rate, atrial fibrillation, and high sensitivity C-reactive protein) were measured at baseline and biennially during the follow-up period. Up to December 2017, we documented 1,104 deaths after CVD in the Kailuan I study and 170 deaths in the Kailuan II study. Older age, hyperglycemia and proteinuria were identified by the SCTREE as main predictors of post-CVD mortality. CVD survivors in the high risk group (presence of 2-3 of these top risk factors), had higher mortality risk in the training dataset (hazard ratio (HR): 5.41; 95% confidence Interval (CI): 4.49-6.52) and in the validation dataset (HR: 6.04; 95%CI: 3.59-10.2), than those in the lowest risk group (presence of 0-1 of these factors). CONCLUSION: Older age, hyperglycemia and proteinuria were the main predictors of post-CVD mortality. TRIAL REGISTRATION: ChiCTR-TNRC-11001489.