Dataset Information

Bile Acids and Risk of Adverse Cardiovascular Events and All-Cause Mortality in Patients with Acute Coronary Syndrome.

ABSTRACT: The relationship between bile acids (BAs) and adverse cardiovascular events following acute coronary syndrome (ACS) have been little investigated. We aimed to examine the associations of BAs with the risk of cardiovascular events and all-cause mortality in ACS. We conducted a prospective study on 309 ACS patients who were followed for 10 years. Plasma BAs were quantified by liquid chromatography coupled to tandem mass spectrometry. Cox regression analyses with elastic net penalties were performed to associate BAs with MACE and all-cause mortality. Weighted scores were computed using the 100 iterated coefficients corresponding to each selected BA, and the associations of these scores with these adverse outcomes were assessed using multivariable Cox regression models. A panel of 10 BAs was significantly associated with the increased risk of MACE. The hazard ratio of MACE per SD increase in the estimated BA score was 1.35 (95% CI 1.12-1.63). Furthermore, four BAs were selected from the elastic net model for all-cause mortality, although their weighted score was not independently associated with mortality. Our findings indicate that primary and secondary BAs may play a significant role in the development of MACE. This insight holds potential for developing strategies to manage ACS and prevent adverse outcomes.

SUBMITTER: Mateu-Fabregat J

PROVIDER: S-EPMC11013079 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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Bile Acids and Risk of Adverse Cardiovascular Events and All-Cause Mortality in Patients with Acute Coronary Syndrome.

Mateu-Fabregat Javier J Mostafa Hamza H Sanchez-Gimenez Raul R Peiró Óscar M ÓM Bonet Gil G Carrasquer Anna A Fragkiadakis Georgios A GA Bardaji Alfredo A Bulló Mònica M Papandreou Christopher C

Nutrients 20240405 7

The relationship between bile acids (BAs) and adverse cardiovascular events following acute coronary syndrome (ACS) have been little investigated. We aimed to examine the associations of BAs with the risk of cardiovascular events and all-cause mortality in ACS. We conducted a prospective study on 309 ACS patients who were followed for 10 years. Plasma BAs were quantified by liquid chromatography coupled to tandem mass spectrometry. Cox regression analyses with elastic net penalties were performe ...[more]

PMID: 38613095

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Project description:BackgroundAcute coronary syndrome is the topmost cause of death worldwide; therefore, it is necessary to predict major adverse cardiovascular events and cardiovascular deaths in patients with acute coronary syndrome to make correct and timely clinical decisions.ObjectiveThe current review aimed to highlight algorithms and important predictor variables through examining those studies which used machine learning algorithms for predicting major adverse cardiovascular events in patients with acute coronary syndrome.MethodsTo predict major adverse cardiovascular events in patients with acute coronary syndrome, the preferred reporting items for scoping reviews guidelines were used. In doing so, PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases were searched for articles published between 2005 and 2021. The checklist "Quality assessment of machine learning studies" was used to assess the quality of eligible studies. The findings of the studies are presented in the form of a narrative synthesis of evidence.ResultsIn total, among 2,558 retrieved articles, 22 studies were qualified for analysis. Major adverse cardiovascular events and mortality were predicted in 5 and 17 studies, respectively. According to the results, 14 (63.64%) studies did not perform external validation and only used registry data. The algorithms used in this study comprised, inter alia, Regression Logistic, Random Forest, Boosting Ensemble, Non-Boosting Ensemble, Decision Trees, and Naive Bayes. Multiple studies (N = 20) achieved a high area under the ROC curve between 0.8 and 0.99 in predicting mortality and major adverse cardiovascular events. The predictor variables used in these studies were divided into demographic, clinical, and therapeutic features. However, no study reported the integration of machine learning model into clinical practice.ConclusionMachine learning algorithms rendered acceptable results to predict major adverse cardiovascular events and mortality outcomes in patients with acute coronary syndrome. However, these approaches have never been integrated into clinical practice. Further research is required to develop feasible and effective machine learning prediction models to measure their potentially important implications for optimizing the quality of care in patients with acute coronary syndrome.

Project description:BackgroundEmergency general surgery (EGS) is associated with increased mortality, with kidney failure a contributing risk, but comparative outcomes between patients with kidney failure and the general population are lacking.MethodsIn this retrospective population-cohort study, data were analysed for all EGS procedures performed in England between 1 April 2004 and 31 March 2019. EGS was defined as partial colectomy, small bowel resection, cholecystectomy, appendicectomy, lysis of peritoneal adhesions, surgery for peptic ulcer, or laparotomy. The main outcome measure was major adverse cardiovascular events (MACEs) and all-cause mortality after surgery.ResultsFrom 691 064 procedures, 0.16 per cent (n = 1097) and 0.23 per cent (n = 1567) were performed on kidney transplant and dialysis recipients respectively. Laparotomy was the most frequent EGS procedure for kidney transplant (46 per cent of procedures, n = 507) and dialysis (45 per cent of procedures, n = 704) recipients, with the highest 30-day and 1-year mortality. In logistic regression analysis, both kidney failure cohorts had higher risk for experiencing MACEs in the postoperative interval after emergency laparotomy; within 3 months (dialysis; OR 2.44 (95 per cent c.i. 2.08 to 2.87), P < 0.001 and transplant; OR 2.05 (95 per cent c.i. 1.57 to 2.68), P < 0.001) and within 1 year (dialysis; OR 2.39 (95 per cent c.i. 2.06 to 2.77), P < 0.001 and transplant; OR 2.21 (95 per cent c.i. 1.76 to 2.77), P < 0.001); however, in a propensity-score-matched cohort, increased risk for MACEs was observed among dialysis patients after emergency laparotomy (HR 2.10 (95 per cent c.i. 1.82 to 2.43), P < 0.001) but not kidney transplant recipients (HR 1.17 (95 per cent c.i. 0.97 to 1.41), P = 0.096).ConclusionMortality after emergency surgery is higher for patients with kidney failure and dialysis is worse than kidney transplantation, with cardiovascular deaths more common than the general population.

Dataset Information

Bile Acids and Risk of Adverse Cardiovascular Events and All-Cause Mortality in Patients with Acute Coronary Syndrome.

Publications

Bile Acids and Risk of Adverse Cardiovascular Events and All-Cause Mortality in Patients with Acute Coronary Syndrome.

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