Prognostic Value of Machine Learning-based Time-to-Event Analysis Using Coronary CT Angiography in Patients with Suspected Coronary Artery Disease.

Purpose: To assess the long-term prognostic value of a machine learning (ML) approach in time-to-event analyses incorporating coronary CT angiography (CCTA)-derived and clinical parameters in patients with suspected coronary artery disease. Materials and Methods: The retrospective analysis included patients with suspected coronary artery disease who underwent CCTA between October 2004 and December 2017. Major adverse cardiovascular events were defined as the composite of all-cause death, myocardial infarction, unstable angina, or late revascularization (>90 days after index scan). Clinical and CCTA-derived parameters were assessed as predictors of major adverse cardiovascular events and incorporated into two models: a Cox proportional hazards model with recursive feature elimination and an ML model based on random survival forests. Both models were trained and validated by employing repeated nested cross-validation. Harrell concordance index (C-index) was used to assess the predictive power. Results: A total of 5457 patients (mean age, 61 years ± 11 [SD]; 3648 male patients) were evaluated. The predictive power of the ML model (C-index, 0.74; 95% CI: 0.71, 0.76) was significantly higher than the Cox model (C-index, 0.71; 95% CI: 0.68, 0.74; P = .02). The ML model also outperformed the segment stenosis score (C-index, 0.69; 95% CI: 0.66, 0.72; P < .001), which was the best performing CCTA-derived parameter, and patient age (C-index, 0.66; 95% CI: 0.63, 0.69; P < .001), the best performing clinical parameter. Conclusion: An ML model for time-to-event analysis based on random survival forests had higher performance in predicting major adverse cardiovascular events compared with established clinical or CCTA-derived metrics and a conventional Cox model.Keywords: Machine Learning, CT Angiography, Cardiac, Arteries, Heart, Arteriosclerosis, Coronary Artery DiseaseSupplemental material is available for this article.© RSNA, 2023.

Convolutional neural networks on risk stratification of patients with suspected coronary artery disease undergoing coronary computed tomography angiography.

To assess the prognostic value of convolutional neural networks (CNN) on coronary computed tomography angiography (CCTA) in comparison to conventional computed tomography (CT) reporting and clinical risk scores. 5468 patients who underwent CCTA with suspected coronary artery disease (CAD) were included. Primary endpoint was defined as a composite of all-cause death, myocardial infarction, unstable angina or late revascularization (> 90 days after CCTA). Early revascularization was additionally included as a training endpoint for the CNN algorithm. Cardiovascular risk stratification was based on Morise score and the extent of CAD (eoCAD) as assessed on CCTA. Semiautomatic post-processing was performed for vessel delineation and annotation of calcified and non-calcified plaque areas. Using a two-step training of a DenseNet-121 CNN the entire network was trained with the training endpoint, followed by training the feature layer with the primary endpoint. During a median follow-up of 7.2 years, the primary endpoint occurred in 334 patients. CNN showed an AUC of 0.631 ± 0.015 for prediction of the combined primary endpoint, while combining it with conventional CT and clinical risk scores showed an improvement of AUC from 0.646 ± 0.014 (based on eoCAD only) to 0.680 ± 0.015 (p < 0.0001) and from 0.619 ± 0.0149 (based on Morise Score only) to 0.6812 ± 0.0145 (p < 0.0001), respectively. In a stepwise model including all prediction methods, it was found an AUC of 0.680 ± 0.0148. CNN analysis showed to improve conventional CCTA-derived and clinical risk stratification when evaluating CCTA of patients with suspected CAD.

Echocardiographic evaluation of left ventricular function using an automated analysis algorithm is feasible for beginners and experts: comparison with invasive and non-invasive methods.

AIMS: Echocardiographic measurement of left ventricular function using a user-friendly automated three-dimensional algorithm is highly attractive as it promises quick and accurate diagnosis, circumventing limitations associated with visual estimation or manual biplane measurements. We sought to assess the feasibility and correlation of such automated analysis with clinically established methods. METHODS: A total of 198 patients undergoing transthoracic echocardiography (TTE) with assessment of left ventricular parameters by automated software algorithm (Philips 3D-Heartmodel; 3D-HM) which additionally had either left ventricular angiography (LVA) or cardiac magnetic resonance (CMR) within 24 h of the TTE examination were analyzed. Left ventricular parameters (left ventricular end-diastolic volume, LVEDV, left ventricular end-systolic volume, LVESV as well as left ventricular ejection fraction, LVEF) were compared between 3D-HM, CMR and LVA. RESULTS: Correlation of left ventricular measurements was overall good to excellent and stronger for CMR (EF r = 0.824) than for LVA (EF r = 0.746). Unexperienced and expert clinicians yielded comparable good results. For CMR, highest correlation was detected in patients with BMI < 25 and excellent image quality. High agreement was seen between 3D-HM and CMR or LVA when stratifying patients according to heart failure categories. CONCLUSIONS: Echocardiographic quantification of left ventricular parameters using a software-based algorithm correlated well with established invasive and non-invasive modalities in the clinical setting, even for unexperienced clinicians. Such automated approaches are promising as they allow a reliable, more observer-independent as well as reproducible assessment of left ventricular function.

Prevalence and risk factors associated with syphilis in a cohort of HIV positive individuals in Brazil.

Individuals with syphilis have higher chance of having HIV, and syphilis' genital ulcers increases HIV transmission rate. Nevertheless, there are few well-documented studies about HIV and syphilis co-infection and its risk factors. The study was based on 2262 HIV infected individuals from South Brazilian HIV cohort, which began in 1991, and this analysis included individuals who were included in the cohort until November 2008. Inclusion criteria were having CD4 + T cell count and viral load at baseline, and syphilis serology tests (venereal disease research laboratory [VDRL] > 1:64 or a positive VDRL plus a positive treponemal test). A total of 1012 patients were included; 580 were men (57%); mean age at HIV diagnosis was 33 years; 591 (58%) had previous diagnosis of AIDS; most of the individuals acquired HIV from sexual contact (47.9% heterosexual and 31.7% men who had sex with men [MSM]); and 759 (75%) were on antiretroviral therapy. The prevalence of syphilis was 20.5% (208). After multivariate analysis, being male (2.01; 95% CI, 1.23-3.27; p = 0.005) and MSM (1.91; 95% CI, 1.25-2.90; p = 0.002) were significantly associated to HIV and syphilis co-infection. Males and MSM were associated with higher risk of this co-infection. Our findings may reflect that this particular population is still engaging in unprotected sexual intercourse, and efforts should be made to better target this specific group as they might perpetuate these infections.

S100B levels in the cerebrospinal fluid of rats are sex and anaesthetic dependent.

1. S100B is a calcium-binding protein that acts as a neurotrophic cytokine and is expressed in the central nervous system, predominantly by astrocytes. At nanomolar concentrations, S100B stimulates neurite outgrowth and glial glutamate uptake, as well as protecting neurons against glutamate excitoxicity. 2. Peripheral S100B concentrations, particularly in the serum and cerebrospinal fluid (CSF), have been used as a parameter of glial activation or death in several physiological and pathological conditions. 3. In the present study, we investigated the effect of anaesthetics (thiopental, ketamine and halothane) on CSF concentrations of S100B, as well as a possible sex dependence, because several studies have suggested astrocytes as putative targets for oestrogen. 4. Higher levels of CSF S100B were found when rats were anaesthetized with thiopental; these levels, independently of anaesthetic, were sex dependent. Conversely, no effect of anaesthetic or sex was observed on serum concentrations of S100B. 5. The increase in CSF concentrations of S100B induced by thiopental was confirmed in non-anaesthetized neonatal rats and cortical astrocyte cultures. 6. Assuming CSF S100B as a marker of development, glial activation or even brain damage, investigations regarding the sex dependence of its concentration may be useful in gaining an understanding of sex variations in the behaviour and the pathological course of, as well as susceptibility to, many brain disorders. The findings of the present study reinforce the sex effect on synaptic plasticity and suggest a sex dependence of neural communication mediated by extracellular S100B without restricting the influence of astrocytes on the developmental phase.