History of depression and survival after acute myocardial infarction

Objective: To compare survival in post-myocardial (MI) participants from the Enhancing Recovery In Coronary Heart Disease (ENRICHD) clinical trial with a first episode of major depression (MD) and those with recurrent MID, which is a risk factor for mortality after acute MI. Recent reports suggest that the level of risk may depend on whether the comorbid MD is a first or a recurrent episode. Methods: Survival was compared over a median of 29 months in 370 patients with an initial episode of MD, 550 with recurrent MD, and 408 who were free of depression. Results: After adjusting for an all-cause mortality risk score, initial Beck Depression Inventory score, and the use of selective serotonin reuptake inhibitor antidepressants, patients with a first episode of MD had poorer survival (18.4% all-cause mortality) than those with recurrent MD (11.8%) (hazard ratio (HR)=1.4; 95% Confidence Interval (CI)=1.0-2.0; p=.05). Both first depression (HR=3.1; 95% CI=1.6-6.1; p=.001) and recurrent MD (HR=2.2; 95% CI=1.1-4.4; p=.03) had significantly poorer survival than did the nondepressed patients (3.4%). A secondary analysis of deaths classified as probably due to a cardiovascular cause resulted in similar HRs, but the difference between depression groups was not significant. Conclusions: Both initial and recurrent episodes of MD predict shorter survival after acute MI, but initial MD episodes are more strongly predictive than recurrent episodes. Exploratory analyses suggest that this cannot be explained by more severe heart disease at index, poorer response to depression treatment, or a higher risk of cerebrovascular disease in patients with initial MD episodes

Systematic review and individual patient data meta-analysis of sex differences in depression and prognosis in persons with myocardial infarction: a MINDMAPS study

Objective - Using combined individual patient data (IPD) from prospective studies, we explored sex differences in depression and prognosis post-myocardial infarction (MI), and determined whether disease indices could account for found differences. Methods - Meta-analysis of IPD from 10,175 MI patients who completed diagnostic interviews or depression questionnaires from 16 prospective studies of MI patients, identified by systematic review for the MINDMAPS study. Multilevel logistic and Cox regression models were used to determine sex differences in prevalence of depression and sex-specific effects of depression on subsequent cardiovascular morbidity and all-cause mortality. Results - Combined interview and questionnaire data from observational studies showed that 36% (635/1760) of women and 29% (1575/5526) of men reported elevated levels of depression (age-adjusted OR=0.68, 95% CI 0.60 to 0.77, p (sex*depression interaction p Conclusions - The prevalence of depression post-MI was higher in women than men, but the association between depression and cardiac prognosis was worse for men. LVEF was associated with depression in men only, and accounted for the increased risk of all-cause mortality in depressed men versus women, suggesting that depression in men post-MI may in part reflect cardiovascular disease severity

Adjusted prognostic association of post-myocardial infarction depression withmortality and cardiovascular events: an individual patient data meta-analysis

Background: The association between depression after myocardial infarction (post-MI) and increased risk of mortality and cardiac morbidity may be due to cardiac disease severity. Aims: To combine original data from studies on the association between post-MI depression and prognosis into one database. To investigate to what extent post-MI depression predicts prognosis independently of disease severity. Method: Individual patient data meta-analysis of studies, using multilevel, multivariable Cox regression analyses. Results:Sixteen studies participated, creating a database of 10,175 post-MI patients. HRs for post-MI depression were 1.32 (95%CI 1.26-1.38, p Conclusions: The association between post-MI depression and prognosis is attenuated after adjustment for cardiac disease severity. Still, depression remains independently associated with prognosis, with a 22% increased risk of all-cause mortality and a 13% increased risk of cardiovascular events per standard deviation in depression z-score. Declaration of interest: None

Predictors of one-year mortality at hospital discharge after acute coronary syndromes: A new risk score from the EPICOR (long-tErm follow uP of antithrombotic management patterns In acute CORonary syndrome patients) study.

AIMS: A reliable prediction tool is needed to identify acute coronary syndrome (ACS) patients with high mortality risk after their initial hospitalization. METHODS: EPICOR (long-tErm follow uP of antithrombotic management patterns In acute CORonary syndrome patients: NCT01171404) is a prospective cohort study of 10,568 consecutive hospital survivors after an ACS event (4943 ST-segment elevation myocardial infarction (STEMI) and 5625 non-ST-elevation ACS (NSTE-ACS)). Of these cases, 65.1% underwent percutaneous coronary intervention (PCI) and 2.5% coronary artery bypass graft (CABG). Post-discharge mortality was recorded for up to two years. From over 50 potential predictor variables a new risk score for one-year mortality was developed using forward stepwise Cox regression, and examined for goodness-of-fit, discriminatory power, and external validation. RESULTS: A total of 407 patients (3.9%) died within one year of discharge. We identified 12 highly significant independent predictors of mortality (in order of predictive strength): age, lower ejection fraction, poorer EQ-5D quality of life, elevated serum creatinine, in-hospital cardiac complications, chronic obstructive pulmonary disease, elevated blood glucose, male gender, no PCI/CABG after NSTE-ACS, low hemoglobin, peripheral artery disease, on diuretics at discharge. When combined into a new risk score excellent discrimination was achieved (c-statistic=0.81) and this was also validated on a large similar cohort (9907 patients) in Asia (c=0.78). For both STEMI and NSTE-ACS there was a steep gradient in one-year mortality ranging from 0.5% in the lowest quintile to 18.2% in the highest decile. NSTE-ACS contributes over twice as many high-risk patients as STEMI. CONCLUSIONS: Post-discharge mortality for ACS patients remains of concern. Our new user-friendly risk score available on www.acsrisk.org can readily identify who is at high risk

Is Depression After an Acute Coronary Syndrome Simply a Marker of Known Prognostic Factors for Mortality?

Objective: Controversy remains over whether the association between depression and mortality in patients with acute coronary syndrome (ACS) is confounded by incomplete adjustment for measures of known prognostic markers. We assessed a) whether depression was associated with the most comprehensive empirically derived index of clinical mortality predictors: the Global Registry of Acute Coronary Events (GRACE) risk score for predicting 6-month mortality after discharge for ACS; and b) whether depression remained an independent predictor of all-cause mortality after adjustment for the GRACE score and left ventricular dysfunction. Methods: We surveyed prospectively 457 patients with ACS (aged 25–92 years; 41% women, 13% black, and 11% Hispanic), hospitalized between May 2003 and June 2005. Depressive symptoms were assessed with the Beck Depression Inventory (BDI) and diagnosis of major depressive disorder (MDD) was made by a structured psychiatric interview, within 1 week of hospitalization. Results: Despite differences in individual components of the GRACE score between depressed and nondepressed participants, neither depression measure was associated with overall GRACE score. For participants with MDD, the mean ± standard deviation GRACE score was 84 ± 33, compared with 92 ± 31 for those without MDD (p = .09). Using Cox proportional hazards regression analysis, MDD and depressive symptom severity each predicted mortality after controlling for GRACE score and left ventricular dysfunction (adjusted hazard ratio for MDD = 2.51; 95% Confidence Interval = 1.45–4.37). Conclusion: Depression is not simply a marker of clinical indicators that predict all-cause mortality after ACS. This strengthens the assertion that there is something unique in the association between depression and post-ACS prognosis, independent of known prognostic markers

Biomarkers and Bioassays for Cardiovascular Diseases: Present and Future

Stratification of cardiac patients arriving at the emergency department is now being made according to the levels of acute cardiac biomarkers (i.e. cardiac troponin (cTn) or creatine kinase myocardial band (CK-MB)). Ongoing efforts are undertaken in an attempt to identify and validate additional cardiac biomarkers, for example, interleukin-6, soluble CD40L, and C-reactive protein, in order to further risk stratify patients with acute coronary syndrome. Several studies have also now shown an association of platelet transcriptome and genomic single nucleotide polymorphisms with myocardial infarction by using advanced genomic tools. A number of markers, such as myeloid-related protein 14 (MRP-14), cyclooxygenase-1 (COX-1), 5-lipoxygenase activating protein (FLAP), leukotriene A4 hydrolase (LTA4H) and myocyte enhancing factor 2A (MEF2A), have been linked to acute coronary syndromes, including myocardial infarction. In the future, these novel markers may pave the way toward personalized disease-prevention programs based on a person’s genomic, thrombotic and cardiovascular profiles. Current and future biomarkers and bioassays for identifying at-risk patients will be discussed in this review

Machine learning prediction of mortality in acute myocardial infarction

© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.Background: Acute Myocardial Infarction (AMI) is the leading cause of death in Portugal and globally. The present investigation created a model based on machine learning for predictive analysis of mortality in patients with AMI upon admission, using different variables to analyse their impact on predictive models. Methods: Three experiments were built for mortality in AMI in a Portuguese hospital between 2013 and 2015 using various machine learning techniques. The three experiments differed in the number and type of variables used. We used a discharged patients' episodes database, including administrative data, laboratory data, and cardiac and physiologic test results, whose primary diagnosis was AMI. Results: Results show that for Experiment 1, Stochastic Gradient Descent was more suitable than the other classification models, with a classification accuracy of 80%, a recall of 77%, and a discriminatory capacity with an AUC of 79%. Adding new variables to the models increased AUC in Experiment 2 to 81% for the Support Vector Machine method. In Experiment 3, we obtained an AUC, in Stochastic Gradient Descent, of 88% and a recall of 80%. These results were obtained when applying feature selection and the SMOTE technique to overcome imbalanced data. Conclusions: Our results show that the introduction of new variables, namely laboratory data, impacts the performance of the methods, reinforcing the premise that no single approach is adapted to all situations regarding AMI mortality prediction. Instead, they must be selected, considering the context and the information available. Integrating Artificial Intelligence (AI) and machine learning with clinical decision-making can transform care, making clinical practice more efficient, faster, personalised, and effective. AI emerges as an alternative to traditional models since it has the potential to explore large amounts of information automatically and systematically.The present publication was funded by Fundação Ciência e Tecnologia, IP national support through CHRC (UIDP/04923/2020).info:eu-repo/semantics/publishedVersio

Myocardial infarction - Risk stratification and evaluation of therapies

Background. Myocardial infarction (MI) remains the leading cause of death worldwide, despite several advances in acute coronary care during the last decades. This thesis assessed different risk stratification tools and evaluated interventional and pharmacological treatment strategies in high-risk patients with MI. Methods. This work comprises four studies. The first and the fourth study extracted data from national registries. The first study evaluated the prognostic value of early percutaneous coronary intervention (PCI) on mortality in 2896 patients with cardiac arrest and no signs of ST-elevation MI (STEMI) undergoing coronary angiography, while the fourth study validated the novel PRECISE-DAPT score for the prediction of post-discharge bleeding in 66295 patients with MI treated with PCI and dual antiplatelet therapy (DAPT). The second and the third study were prespecified subgroup analyses of a recent trial that randomly assigned MI patients to an anticoagulation strategy with bivalirudin or heparin during PCI in a contemporary setting, including routine radial artery access, potent P2Y12 inhibition, and rare use of glycoprotein IIb/IIIa inhibitors. The second study investigated the impact of baseline anemia on clinical outcomes in 5482 of these patients, whereas the third study compared bivalirudin to heparin monotherapy regarding clinical outcomes in 1592 elderly patients (≥75 years). Results. A total of 1271 (43.9%) of resuscitated cardiac arrest patients without STEMI had severe coronary artery stenosis (≥90%) on coronary angiography, of whom 753 (59.2%) underwent PCI but experienced a higher 30-day mortality rate compared to patients undergoing only diagnostic coronary angiography (40.9% vs 32.7%; p=0.011). After adjustments for confounders, there was no association between PCI and mortality (hazard ratio [HR] 1.07; 95% confidence interval [CI] 0.84-1.36). Baseline anemia identified a subset of MI patients undergoing PCI with a higher comorbidity burden. Anemia was associated with increased 180-day rates of death (6.9% vs 2.1%; p<0.001), myocardial reinfarction (4.3% vs 1.9%; p<0.001), major bleeding (13.4% vs 8.2%), and stroke (2.0% vs 0.7%). Results were particularly evident in patients with a hemoglobin value below 100 g/L, who had a tenfold higher mortality rate, sixfold higher MI rate, and threefold higher bleeding rate, compared to patients without anemia. Results were similar after adjustments for confounders. Elderly patients (≥75 years) had a markedly increased risk of adverse outcomes within 180 days after MI and PCI compared to younger patients (<75 years). Elderly patients who received bivalirudin or heparin had similar baseline characteristics. Bivalirudin did not reveal any benefit over heparin monotherapy, regarding 180-day mortality, myocardial reinfarction, major bleeding, stroke, or stent thrombosis. A high PRECISE-DAPT score (≥25) identified a high-risk subset of MI patients with more comorbidities and higher bleeding rates during DAPT. However, the predictive performance for major bleeding was moderate (c-statistic 0.64; 95% CI 0.63-0.66). Furthermore, the discriminatory power of the score was even more limited in patients with pre-existing risk factors for bleeding, especially for patients with advanced age (c-statistic 0.57; 95% CI 0.55-0.60), low body weight (c-statistic 0.56; 95% CI 0.51-0.61), anemia (c-statistic 0.60; 95% CI 0.58-0.63), or cancer (c-statistic 0.59; 95% CI 0.53-0.66). Conclusion. The reported findings in this research on risk stratification tools and therapies have potential implications for a more patient-tailored acute coronary care that may further improve outcomes for patients with MI

New perspectives on depression and heart disease

Ongeveer een op de vijf hartpatiënten krijgt last van een depressie. Onderzoeker Petra Hoen van het UMCG maakte onderscheid tussen verschillende vormen van depressieve klachten bij deze patiënten en stelde vast dat ze samenhangen met een verschillend ziektebeloop. Hartpatiënten bij wie lichamelijke depressieve klachten zoals vermoeidheid en slapeloosheid voorop staan, hebben een slechter ziektebeloop dan patiënten met een depressieve stemming en concentratieproblemen. Hoen pleit voor meer aandacht voor de behandeling van lichamelijke depressieve klachten, bijvoorbeeld met lichaamsbeweging. Op 12 september promoveert Hoen op de resultaten van haar onderzoek aan de Rijksuniversiteit Groningen. Hartpatiënten hebben last van vernauwde kransslagaders, wat zich uit in angina pectoris (pijn op de borst) of een hartaanval. Al langer is bekend dat deze patiënten na behandeling van hun hartziekte een verhoogde kans lopen om depressief te worden. Behandeling van de depressie blijkt nauwelijks effectief te zijn, en ook de hartklachten nemen niet af door de depressiebehandeling. Samenhang depressie en hartklachten Hoen onderzocht een groep van 473 patiënten die een depressie ontwikkelden na een hartaanval. Zij maakte onderscheid tussen lichamelijke depressieve klachten zoals vermoeidheid, eet- en slaapproblemen, en cognitieve depressieve klachten, zoals depressieve stemming en concentratieproblemen. Uit haar onderzoek blijkt dat de lichamelijke depressieve klachten samenhangen met de ernst van de hartziekte en het beloop ervan. Hoen concludeert dat deze lichamelijke klachten een hoger risico op nieuwe hartziekten of een eerder overlijden geven. Positieve emoties De impact van negatieve emoties op het beloop van hartziekte is uitgebreid onderzocht, terwijl maar weinig studies zijn gedaan naar de effecten van positieve emoties. Om in deze leemte te voorzien, heeft Hoen een groep van 1019 patiënten met een stabiele vorm van hartziekte gevolgd over een periode van zeven jaar. Het ervaren van positieve gevoelens werd gemeten aan de hand van hoe enthousiast, energiek en alert een persoon in het leven staat. Hoen stelde vast dat hartpatiënten met positieve gevoelens een toegenomen levensduur hebben. Deze samenhang werd verklaard door de hogere mate van lichamelijke activiteit van de hartpatiënten met een positieve stemming. De bevindingen wijzen erop dat de toename van overleving die samenhangt met het ervaren van positieve gevoelens, mogelijk bereikt kan worden met behandelingen die ook lichamelijke training bevorderen