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Predicting Adverse Outcomes After Myocardial Infarction Among Patients With Diabetes Mellitus.

ABSTRACT: BACKGROUND:Although patients with diabetes mellitus experience high rates of adverse events after acute myocardial infarction (AMI), including death and recurrent ischemia, some diabetic patients are likely at low risk, whereas others are at high risk. We sought to develop prediction models to stratify risk after AMI in patients with diabetes mellitus. METHODS AND RESULTS:We developed prediction models for long-term mortality and angina among 1613 patients with diabetes mellitus discharged alive after AMI from 24 US hospitals and then validated the models in a separate, multicenter registry of 786 patients with diabetes mellitus. Event rates in the derivation cohort were 27% for 5-year mortality and 27% for 1-year angina. Parsimonious prediction models demonstrated good discrimination (c-indices=0.78 and 0.69, respectively) and excellent calibration. Within the context of the predictors we estimated, the strongest predictors for mortality were higher creatinine, not working at the time of the AMI, older age, lower hemoglobin, left ventricular dysfunction, and chronic heart failure. The strongest predictors for angina were angina burden in the 4 weeks before the AMI, younger age, history of prior coronary bypass graft surgery, and non-white race. The lowest and highest deciles of predicted risk ranged from 4% to 80% for mortality and 12% to 59% for angina. The models also performed well in external validation (c-indices=0.78 and 0.73, respectively). CONCLUSIONS:We found a wide range of risk for adverse outcomes after AMI in diabetic patients. Predictive models can identify patients with diabetes mellitus for whom closer follow-up and aggressive secondary prevention strategies should be considered.

SUBMITTER: Arnold SV

PROVIDER: S-EPMC5341790 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Predicting Adverse Outcomes After Myocardial Infarction Among Patients With Diabetes Mellitus.

Arnold Suzanne V SV Spertus John A JA Jones Philip G PG McGuire Darren K DK Lipska Kasia J KJ Xu Yaping Y Stolker Joshua M JM Goyal Abhinav A Kosiborod Mikhail M

Circulation. Cardiovascular quality and outcomes 20160524 4

<h4>Background</h4>Although patients with diabetes mellitus experience high rates of adverse events after acute myocardial infarction (AMI), including death and recurrent ischemia, some diabetic patients are likely at low risk, whereas others are at high risk. We sought to develop prediction models to stratify risk after AMI in patients with diabetes mellitus.<h4>Methods and results</h4>We developed prediction models for long-term mortality and angina among 1613 patients with diabetes mellitus d ...[more]

PMID: 27220369

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Project description:In an era of increasing need for precision medicine, machine learning has shown promise in making accurate acute myocardial infarction outcome predictions. The accurate assessment of high-risk patients is a crucial component of clinical practice. Type 2 diabetes mellitus (T2DM) complicates ST-segment elevation myocardial infarction (STEMI), and currently, there is no practical method for predicting or monitoring patient prognosis. The objective of the study was to compare the ability of machine learning models to predict in-hospital mortality among STEMI patients with T2DM. We compared six machine learning models, including random forest (RF), CatBoost classifier (CatBoost), naive Bayes (NB), extreme gradient boosting (XGBoost), gradient boosting classifier (GBC), and logistic regression (LR), with the Global Registry of Acute Coronary Events (GRACE) risk score. From January 2016 to January 2020, we enrolled patients aged > 18 years with STEMI and T2DM at the Affiliated Hospital of Zunyi Medical University. Overall, 438 patients were enrolled in the study [median age, 62 years; male, 312 (71%); death, 42 (9.5%]). All patients underwent emergency percutaneous coronary intervention (PCI), and 306 patients with STEMI who underwent PCI were enrolled as the training cohort. Six machine learning algorithms were used to establish the best-fit risk model. An additional 132 patients were recruited as a test cohort to validate the model. The ability of the GRACE score and six algorithm models to predict in-hospital mortality was evaluated. Seven models, including the GRACE risk model, showed an area under the curve (AUC) between 0.73 and 0.91. Among all models, with an accuracy of 0.93, AUC of 0.92, precision of 0.79, and F1 value of 0.57, the CatBoost model demonstrated the best predictive performance. A machine learning algorithm, such as the CatBoost model, may prove clinically beneficial and assist clinicians in tailoring precise management of STEMI patients and predicting in-hospital mortality complicated by T2DM.

Project description:BackgroundType 2 diabetes mellitus (T2DM) is a risk factor for acute myocardial infarction (AMI) and a common comorbidity in patients with AMI. T2DM doubles the fatality rate of patients with AMI in the acute phase of AMI and the follow-up period. However, the mechanisms by which T2DM increases the fatality rate remain unknown. This study sought to investigate changes in the gut microbiota of patients with AMI and T2DM (AMIDM) to extend understandings of the relative mechanisms from the aspects of gut microbiota.MethodsPatients were recruited and divided into 2 groups comprising 15 patients with AMIDM and 15 patients with AMI but without T2DM (AMINDM). Their stool samples and clinical information were collected. 16S ribosomal DNA sequencing was used to analyze the structure and composition of the gut microbiota based on the operational taxonomic units.ResultsA significant difference was observed in the gut microbiota β diversity between the 2 groups. At the phylum level, the AMIDM patients showed an increase in the abundance of Firmicutes and a decrease in the abundance of Bacteroidetes compared to the AMINDM patients. At the genus level, the AMIDM patients showed an increase in the abundance of Companilactobacillus, Defluvitaleaceae UCG-011 and UCG-009, and a decrease in the abundance of Phascolarctobacterium and CAG 56 compared to the AMINDM patients. At the species level, the AMIDM patients showed an increase in the abundance of species unclassified NK4A214 group, Bacteroides clarus, Coprococcus comes, unclassified Defluviltaleaceae UCG-011, uncultured rumen bacterium, unclassified CAG 56, Barnesiella intestinihominis, Lachnospiraceae bacterium, Bacteroides nordii, unclassified UCG-009, and the Family XIII AD3011 group compared to the AMINDM patients. The gut microbiota function predictions indicated that the nucleotide metabolism-related pathway was significantly more increase in the patients with AMIDM than those with AMINDM. Additionally, the patients with AMIDM showed an increase in gram-positive bacteria and a decrease in the proportion of gram-negative bacteria. Our correlation analysis results on the gut microbiota and clinical parameters might extend understandings of the progression of AMI.ConclusionsChanges in the gut microbiota composition of patients with AMIDM affect the severity of the metabolic disturbance and may be responsible for poorer clinical outcomes and worse disease progression in patients with AMIDM compared to those with AMINDM.

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Predicting Adverse Outcomes After Myocardial Infarction Among Patients With Diabetes Mellitus.

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Predicting Adverse Outcomes After Myocardial Infarction Among Patients With Diabetes Mellitus.

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