Prediction of major adverse cardiac, cerebrovascular events in patients with diabetes after acute coronary syndrome

Abstract

Background and objectives: The risk of major adverse cardiac and cerebrovascular events following acute coronary syndrome is increased in people with diabetes. Predicting out-of-hospital outcomes upon follow-up remains difficult, and no simple, well-validated tools exist for this population at present. We aim to evaluate several factors in a competing risks model for actionable evaluation of the incidence of major adverse cardiac and cerebrovascular events in diabetic outpatients following acute coronary syndrome. Methods: Retrospective analysis of consecutive patients admitted for acute coronary syndrome in two centres. A Fine–Gray competing risks model was adjusted to predict major adverse cardiac and cerebrovascular events and all-cause mortality. A point-based score is presented that is based on this model. Results: Out of the 1400 patients, there were 783 (55.9%) with at least one major adverse cardiac and cerebrovascular event (417 deaths). Of them, 143 deaths were due to non-major adverse cardiac and cerebrovascular events. Predictive Fine–Gray models show that the ‘PG-HACKER’ risk factors (gender, age, peripheral arterial disease, left ventricle function, previous congestive heart failure, Killip class and optimal medical therapy) were associated to major adverse cardiac and cerebrovascular events. Conclusion: The PG-HACKER score is a simple and effective tool that is freely available and easily accessible to physicians and patients. The PG-HACKER score can predict major adverse cardiac and cerebrovascular events following acute coronary syndrome in patients with diabetes.

New genetic variants associated with major adverse cardiovascular events in patients with acute coronary syndromes and treated with clopidogrel and aspirin

“…Apart from identifying the novel genotypes and genes associated with MACE, we improved the prediction of MACE by developing a novel classifier for 18-month MACE, when compared with previous studies [ 41 – 44 ]. Previous studies were mainly based on clinical features; no comprehensive and complete genetic makers were available for the prognostic classification of MACE.…”

<p>Nomogram Based on Risk Factors for Type 2 Diabetes Mellitus Patients with Coronary Heart Disease</p>

“…In this system, age is a major indicator, and the risk score is relatively high. 23 According to the American Heart Association scoring system, atherosclerosis is more Figure 2 Demographic and clinical feature selection using the LASSO binary logistic regression model in T2DM patients with CHD based on the training set. Notes: (A) Optimal parameter (lambda) selection in the LASSO model used fivefold cross-validation based on minimum criteria.…”

“…In this system, age is a major indicator, and the risk score is relatively high. 23 According to the American Heart Association scoring system, atherosclerosis is more common in elderly T2DM patients compared with younger patients, 24 and atherosclerosis is a significant cause of CHD.…”

A BERT‐BiGRU‐CRF Model for Entity Recognition of Chinese Electronic Medical Records

“…Secondly, calculate the probability of normalized sequence y through the Softmax function, shown in formula (11). Finally, the label sequence with the highest score is calculated using the Viterbi algorithm, shown in formula (12):…”

“…At present, the scientific research of machine learning in the field of CVD mainly focuses on two aspects: diagnosis and prognosis prediction of cerebrovascular disease: (1) From the perspective of CVD diagnosis, most scholars use structured data to nest machine learning models to complete disease diagnosis. e literature [10][11][12] established a joint diagnosis model based on logitic regression method and XGBoost machine learning method by collecting clinical data of demographic characteristics. (2) From the perspective of prognosis prediction, the use of machine learning methods for risk prediction has gradually become the trend of disease prediction, while machine learning methods such as random forest, decision tree, SVM, and other machine learning methods have achieved certain research results in the prediction of cerebrovascular diseases.…”