Prevalence Of All-cause Mortality Research Articles

Evaluation of a machine learning-based metabolic marker for coronary artery disease in the UK Biobank.

An in silico quantitative score of coronary artery disease (ISCAD), built using machine learning and clinical data from electronic health records, has been shown to result in gradations of risk of subclinical atherosclerosis, coronary artery disease (CAD) sequelae, and mortality. Large-scale metabolite biomarker profiling provides increased portability and objectivity in machine learning for disease prediction and gradation. However, these models have not been fully leveraged. We evaluated a quantitative score of CAD derived from probabilities of a machine learning model trained on metabolomic data. We developed a CAD-predictive learning model using metabolic data from 93,642 individuals from the UK Biobank (median [IQR] age, 57 [14] years; 39,796 [42%] male; 5640 [6%] with diagnosed CAD), and assessed its probabilities as a quantitative metabolic risk score for CAD (M-CAD; range 0 [lowest probability] to 1 [highest probability]) in participants of the UK Biobank. The relationship of M-CAD with arterial stiffness index, ejection fraction, CAD sequelae, and mortality was assessed. The model predicted CAD with an area under the receiver-operating-characteristic curve of 0.712. Arterial Stiffness Index increased by 0.19 and ejection fraction decreased by 0.2% per 0.1 increase in M-CAD. Both incident and recurrent myocardial infarction increased stepwise over M-CAD quartiles (odds ratio (OR) 15.3 [4.2%] and 12.5 [0.2%]) in top quartiles as compared to the first quartile of incident and recurrent MI respectively). Likewise, the hazard ratio and prevalence of all-cause mortality, CVD-associated mortality, and CAD-associated mortality increased stepwise over M-CAD deciles (2.98 [14%], 9.34 [4.3%], 26.7 [2.7%] in the top deciles as compared to the first decile of all-cause, CVD, and CAD mortality respectively). Metabolic-based machine learning can be used to build a quantitative risk score for CAD that is associated with atherosclerotic burden, CAD sequelae and mortality.

Catheter ablation as first-line treatment for ventricular tachycardia in patients with structural heart disease and preserved left ventricular ejection fraction: a systematic review and meta-analysis

In this systematic review and meta-analysis, we aim to evaluate the efficacy and safety of catheter ablation as the first-line treatment of ventricular tachycardia (VT) in patients with structural heart disease (SHD) and preserved left ventricular ejection fraction (LVEF). Patients with SHD are particularly susceptible to VT, a condition that increases the risk of sudden cardiac death (SCD). Implantable cardioverter-defibrillators (ICDs) can terminate VT and prevent SCD but do not prevent VT recurrence. The efficacy and safety of CA as a first-line treatment in SHD patients with preserved LVEF remain unclear. We searched PubMed/Medline, EMBASE, Web of Science, and Cochrane CENTRAL for studies reporting the outcomes of CA therapy in patients with VT and preserved LVEF, published up to January 19, 2023. The primary outcome was the incidence of SCD following catheter ablation as the first-line treatment of VT in patients with SHD and preserved LVEF. Secondary outcomes included all-cause mortality, VT recurrence, procedural complications, CA success rate, and ICD implantation after catheter ablation. We included seven studies in the meta-analysis, encompassing a total of 920 patients. The pooled success rate of catheter ablation was 84.6% (95% CI 67.2–93.6). Complications occurred in 6.4% (95% CI 4.0–9.9) of patients, and 13.9% (95% CI 10.1–18.8) required ICD implantation after ablation. VT recurrence was observed in 23.2% (95% CI 14.8–34.6) of patients, while the rate of sudden cardiac death (SCD) was 3.1% (95% CI 1.7–5.6). The overall prevalence of all-cause mortality in this population was 5% (95% CI 1.8–13). CA appears promising as a first-line VT treatment in patients with SHD and preserved LVEF, especially for monomorphic hemodynamically tolerated VT. However, due to the lack of direct comparisons with ICDs and anti-arrhythmic drugs, further research is needed to confirm these findings.

Comparison of two different frailty screening scales for predicting mortality due to all causes in older inpatients.

This study examines the relationship between two frailty screening tools and 90-day all-cause mortality in geriatric inpatients. The study included patients aged ≥60 years who were admitted to the geriatrics unit of a university hospital between June 2021 and August 2022 and whose mortality status and duration of hospitalization data were obtained from the Health Ministry System. During hospitalization, the patients were screened using two different frailty scales: the Simpler Modified Fried Frailty Scale (sMFS) and the Clinical Frailty Scale (CFS). Patients scoring ≥5 on the CFS and ≥3 on the sMFS were considered frail. A total of 84 participants with a mean age of 78.3±7.6 years were included in this study, of which 36.9% were male. Of the total, 60.7% and 89.3% were considered frail according to the CFS and sMFS, respectively, and the prevalence of all-cause mortality within 90 days was 19%. A univariate analysis using the Kaplan-Meier survival method revealed CFS scores to be statistically significantly related to 90-day all-cause mortality (p<0.001), while sMFS scores were not found to be statistically significant (p=0.849). Furthermore, a statistically significant relationship was identified between CFS score and all-cause mortality in multivariate analysis with Cox regression analysis [(p<0.001), hazard ratio (HR): 3.078; (95% confidence interval: 1.746-5.425)]. An evaluation of frailty in hospitalized older adults using two different scales revealed the CFS to be superior to the sMFS in predicting all-cause mortality within 90 days.

Abstract P1179: Evaluation Of A Machine Learning-based Metabolic Marker For Coronary Artery Disease In The UK Biobank

A recent study has shown that an in silico quantitative score of CAD (ISCAD) can be built using machine learning and clinical data from electronic health records. ISCAD was shown to be associated with CAD risk, atherosclerosis, sequela and all-cause mortality. We developed a CAD-predictive learning model using only metabolic data from 93,642 individuals from the UK Biobank (median [IQR] age, 57 [14] years; 39,796 [42%] male; 5640 [6%] with diagnosed CAD), and assessed its probabilities as a quantitative metabolic risk score for CAD (M-CAD; range 0 [lowest probability] to 1 [highest probability]) in participants of the UK Biobank. The relationship of M-CAD with arterial stiffness index, ejection fraction, CAD sequelae, and key mortality measures was assessed. The model predicted CAD with an area under the receiver-operating-characteristic curve of 0.712 in the testing set. Arterial Stiffness Index increased by 0.19 and 0.27 meters/second and ejection fraction decreased by 0.2% and 1% per 0.1 increase in M-CAD in all subjects and CAD cases, respectively. Both incident and recurrent myocardial infarction increased stepwise over M-CAD quartiles when evaluated in all participants (odds ratio (OR) 15.3 [4.2%] and 12.5 [0.2%]) and participants with CAD (OR 1.78 [34%] and 2.32 [1.7%] in top quartiles as compared to the first quartile of incident and recurrent MI respectively). Likewise, the hazard ratio and prevalence of all-cause mortality, CVD-associated mortality, and CAD-associated mortality increased stepwise over M-CAD deciles when assessed in all participants (2.98 [14%], 9.34 [4.3%], 26.7 [2.7%] in the top deciles as compared to the first decile of all-cause, CVD, and CAD mortality respectively) and a subset of participants with CAD (2.32 [24%], 2.78 [14%]], 3.41 [10%] in the top deciles as compared to the first decile of all-cause, CVD, and CAD mortality respectively. Interpretation of the model with SHAP values demonstrated that features known to be CAD risk factors contributed to the model predictions in the direction expected with their documented biological effects. We demonstrate that metabolic-based machine learning can be used to build a quantitative risk score for CAD that is associated with subclinical atherosclerosis, CAD sequala and mortality.

The value of cardiac magnetic resonance imaging in the diagnosis of myocardial infarction with non-occlusive coronary artery: The CRIMINAL prospective registry

The systematic performance of cardiac magnetic resonance imaging (CMR) could play a pivotal role in providing the underlying cause of myocardial infarction with non-occlusive coronary arteries (MINOCA). This study aimed to describe the characteristics of the patients hospitalised for MINOCA and to assess the proportion of patients for whom CMR provided the underlying diagnosis of MINOCA. Between May 2015 and August 2018, 457 patients with MINOCA were prospectively enrolled in an observational cohort study in 15 centres affiliated to the Collège national des cardiologues des hôpitaux (CNCH). Prior to CMR, the cardiologist established one of the following diagnoses: “type 1 MI”, “type 2 MI”, “myocarditis”, “TTS”, “other diagnoses”, and “uncertain diagnosis” (when no diagnosis was established). Using CMR findings, the cardiologist reassigned the patient to one of the 6 subgroups. In total, 336 of 457 patients with MINOCA underwent CMR. CMR was able to identify a diagnosis in 90.5% of cases, and changed the underlying cause in 35.7% of patients; 33.7% of type 1 MI were not diagnosed prior to CMR. Patients with a normal CMR had a significantly longer median time to complete the CMR (17 vs. 7 days, P = 4.2E −7 ) and lower conventional and high sensitivity troponin values (respectively 1.1 vs. 3.6 μG/L, P = 0.002 and 433.3 vs. 707.1 ng/L, P = 0.007). At 1-year follow-up of the CMR cohort, the prevalence of all-cause mortality was 3%, of cardiac mortality 0.6% and of rehospitalisation for cardiac causes 7.8%. No bleeding disorders occurred after increased anti-platelet therapy in patients re-diagnosed with MI ( Fig. 1 ). CMR provided a diagnosis in 9 out of 10 patients presenting with MINOCA. The diagnosis differed from the one prior to CMR in 35.7% of patients. The systematic use of CMR plays a pivotal role in identifying the underlying cause of myocardial injury, impacting both individuals, health professionals and society at large.

Prevalence of All-Cause Mortality and Suicide among Bariatric Surgery Cohorts: A Meta-Analysis.

Introduction: Prior meta-analysis has reported mortality rates among post-operative bariatric patients, but they have not considered psychiatric factors like suicide contributing to mortality. Objectives: The current meta-analysis aims to determine the pooled prevalence for mortality and suicide amongst cohorts using reported suicides post bariatric surgery. It is also the aim of the current meta-analytical study to determine moderators that could account for the heterogeneity found. Results: In our study, the pooled prevalence of mortality in the studies which reported suicidal mortality was 1.8% and the prevalence of suicide was 0.3%. Mean body mass index (BMI) and the duration of follow-up appear to be significant moderators. Conclusions: Given the prevalence of suicide post bariatric surgery, it is highly important for bariatric teams to consider both the medical and psychiatric well-being of individuals pre- and post-operatively.

The Effect of Coffee and Quantity of Consumption on Specific Cardiovascular and All-Cause Mortality: Coffee Consumption Does Not Affect Mortality.

Previous studies have examined whether or not an association exists between the consumption of caffeinated coffee to all-cause and cardiovascular mortality. This study aimed to delineate this association using population representative data from the National Health and Nutrition Examination Survey III. Patients were included in the study if all the following criteria were met: (1) follow-up mortality data were available, (2) age of at least 45 years, and (3) reported amount of average coffee consumption. A total of 8608 patients were included, with patients stratified into the following groups of average daily coffee consumption: (1) no coffee consumption, (2) less than 1 cup, (3) 1 cup a day, (4) 2-3 cups, (5) 4-5 cups, (6) more than 6 cups a day. Odds ratios, 95% confidence intervals, and P values were calculated for univariate analysis to compare the prevalence of all-cause mortality, ischemia-related mortality, congestive heart failure-related mortality, and stroke-related mortality, using the no coffee consumption group as reference. These were then adjusted for confounding factors for a multivariate analysis. P < 0.05 were considered statistically significant. Univariate analysis demonstrated an association between coffee consumption and mortality, although this became insignificant on multivariate analysis. Coffee consumption, thus, does not seem to impact all-cause mortality or specific cardiovascular mortality. These findings do differ from those of recently published studies. Coffee consumption of any quantity seems to be safe without any increased mortality risk. There may be some protective effects but additional data are needed to further delineate this.