Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are ...limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning.
Endurance exercise training studies frequently show modest changes in VO2max with training and very limited responses in some subjects. By contrast, studies using interval training (IT) or combined ...IT and continuous training (CT) have reported mean increases in VO2max of up to ~1.0 L · min(-1). This raises questions about the role of exercise intensity and the trainability of VO2max. To address this topic we analyzed IT and IT/CT studies published in English from 1965-2012. Inclusion criteria were: 1)≥ 3 healthy sedentary/recreationally active humans <45 yrs old, 2) training duration 6-13 weeks, 3) ≥ 3 days/week, 4) ≥ 10 minutes of high intensity work, 5) ≥ 1:1 work/rest ratio, and 6) results reported as mean ± SD or SE, ranges of change, or individual data. Due to heterogeneity (I(2) value of 70), statistical synthesis of the data used a random effects model. The summary statistic of interest was the change in VO2max. A total of 334 subjects (120 women) from 37 studies were identified. Participants were grouped into 40 distinct training groups, so the unit of analysis was 40 rather than 37. An increase in VO2max of 0.51 L · min(-1) (95% CI: 0.43 to 0.60 L · min(-1)) was observed. A subset of 9 studies, with 72 subjects, that featured longer intervals showed even larger (~0.8-0.9 L · min(-1)) changes in VO2max with evidence of a marked response in all subjects. These results suggest that ideas about trainability and VO2max should be further evaluated with standardized IT or IT/CT training programs.
Asymptomatic left ventricular dysfunction (ALVD) is present in 3-6% of the general population, is associated with reduced quality of life and longevity, and is treatable when found
. An inexpensive, ...noninvasive screening tool for ALVD in the doctor's office is not available. We tested the hypothesis that application of artificial intelligence (AI) to the electrocardiogram (ECG), a routine method of measuring the heart's electrical activity, could identify ALVD. Using paired 12-lead ECG and echocardiogram data, including the left ventricular ejection fraction (a measure of contractile function), from 44,959 patients at the Mayo Clinic, we trained a convolutional neural network to identify patients with ventricular dysfunction, defined as ejection fraction ≤35%, using the ECG data alone. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3%, 85.7%, and 85.7%, respectively. In patients without ventricular dysfunction, those with a positive AI screen were at 4 times the risk (hazard ratio, 4.1; 95% confidence interval, 3.3 to 5.0) of developing future ventricular dysfunction compared with those with a negative screen. Application of AI to the ECG-a ubiquitous, low-cost test-permits the ECG to serve as a powerful screening tool in asymptomatic individuals to identify ALVD.
The molecular transducers of benefits from different exercise modalities remain incompletely defined. Here we report that 12 weeks of high-intensity aerobic interval (HIIT), resistance (RT), and ...combined exercise training enhanced insulin sensitivity and lean mass, but only HIIT and combined training improved aerobic capacity and skeletal muscle mitochondrial respiration. HIIT revealed a more robust increase in gene transcripts than other exercise modalities, particularly in older adults, although little overlap with corresponding individual protein abundance was noted. HIIT reversed many age-related differences in the proteome, particularly of mitochondrial proteins in concert with increased mitochondrial protein synthesis. Both RT and HIIT enhanced proteins involved in translational machinery irrespective of age. Only small changes of methylation of DNA promoter regions were observed. We provide evidence for predominant exercise regulation at the translational level, enhancing translational capacity and proteome abundance to explain phenotypic gains in muscle mitochondrial function and hypertrophy in all ages.
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•High-intensity interval training improved age-related decline in muscle mitochondria•Training adaptations occurred with increased gene transcripts and ribosome proteins•Changes to RNA with training had little overlap with corresponding protein abundance•Enhanced ribosomal abundance and protein synthesis explain gains in mitochondria
Robinson et al. assessed the effects of three different exercise modalities on skeletal muscle adaptations in young and older adults. While all enhanced insulin sensitivity, only HIIT and combined training improved aerobic capacity, associated with enhanced translation of mitochondrial proteins. HIIT effectively improved cardio-metabolic health parameters in aging adults.
To determine the effect of baseline estimated glomerular filtration rate (eGFR) on the causal association between intravenous iodinated contrast material exposure and subsequent development of acute ...kidney injury (AKI) in propensity score-matched groups of patients who underwent contrast material-enhanced or unenhanced computed tomography (CT).
This retrospective study was HIPAA compliant and institutional review board approved. All patients who underwent contrast-enhanced (contrast material group) or unenhanced (non-contrast material group) CT between 2000 and 2010 were identified and stratified according to baseline eGFR by using Kidney Disease Outcomes Quality Initiative cutoffs for chronic kidney disease into subgroups with eGFR of 90 or greater, 60-89, 30-59, and less than 30 mL/min/1.73 m(2). Propensity score generation and 1:1 matching of patients were performed in each eGFR subgroup. Incidence of AKI (serum creatinine SCr increase of ≥0.5 mg/dL ≥44.2 μmol/L above baseline) was compared in the matched subgroups by using the Fisher exact test.
A total of 12 508 propensity score-matched patients with contrast-enhanced and unenhanced scans met all inclusion criteria. In this predominantly inpatient cohort, the incidence of AKI significantly increased with decreasing baseline eGFR (P < .0001). However, this incidence was not significantly different between contrast material and non-contrast material groups in any eGFR subgroup; for the subgroup with eGFR of 90 or greater (n = 1642), odds ratio (OR) was 0.91 (95% confidence interval CI: 0.38, 2.15), P = .82; for the subgroup with eGFR of 60-89 (n = 3870), OR was 1.03 (95% CI: 0.66, 1.60), P = .99; for the subgroup with eGFR of 30-59 (n = 5510), OR was 0.94 (95% CI: 0.76, 1.18), P = .65; and for the subgroup with eGFR of less than 30 mL/min/1.73 m(2) (n = 1486), OR was 0.97 (95% CI: 0.72, 1.30), P = .89.
Diminished eGFR is associated with an increased risk of SCr-defined AKI following CT examinations. However, the risk of AKI is independent of contrast material exposure, even in patients with eGFR of less than 30 mL/min/1.73 m(2).
Hypertrophic cardiomyopathy (HCM) is an uncommon but important cause of sudden cardiac death.
This study sought to develop an artificial intelligence approach for the detection of HCM based on ...12-lead electrocardiography (ECG).
A convolutional neural network (CNN) was trained and validated using digital 12-lead ECG from 2,448 patients with a verified HCM diagnosis and 51,153 non-HCM age- and sex-matched control subjects. The ability of the CNN to detect HCM was then tested on a different dataset of 612 HCM and 12,788 control subjects.
In the combined datasets, mean age was 54.8 ± 15.9 years for the HCM group and 57.5 ± 15.5 years for the control group. After training and validation, the area under the curve (AUC) of the CNN in the validation dataset was 0.95 (95% confidence interval CI: 0.94 to 0.97) at the optimal probability threshold of 11% for having HCM. When applying this probability threshold to the testing dataset, the CNN’s AUC was 0.96 (95% CI: 0.95 to 0.96) with sensitivity 87% and specificity 90%. In subgroup analyses, the AUC was 0.95 (95% CI: 0.94 to 0.97) among patients with left ventricular hypertrophy by ECG criteria and 0.95 (95% CI: 0.90 to 1.00) among patients with a normal ECG. The model performed particularly well in younger patients (sensitivity 95%, specificity 92%). In patients with HCM with and without sarcomeric mutations, the model-derived median probabilities for having HCM were 97% and 96%, respectively.
ECG-based detection of HCM by an artificial intelligence algorithm can be achieved with high diagnostic performance, particularly in younger patients. This model requires further refinement and external validation, but it may hold promise for HCM screening.
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