Unsupervised clustering for phenotypic stratification of clinical, demographic, and stress attributes of cardiac risk in patients with nonischemic exercise stress echocardiography

Deciphering Popliteal Artery Aneurysm Patient Diversity: Insights From a Cluster Analysis of the POPART Registry

BACKGROUND

Popliteal artery aneurysms (PAAs) are the most common peripheral aneurysm. However, due to its rarity, the cumulative body of evidence regarding patient patterns, treatment strategies, and perioperative outcomes is limited. This analysis aims to investigate distinct phenotypical patient profiles and associated treatment and outcomes in patients with a PAA by performing an unsupervised clustering analysis of the POPART (Practice of Popliteal Artery Aneurysm Repair and Therapy) registry.

METHODS AND RESULTS

A cluster analysis (using k-means clustering) was performed on data obtained from the multicenter POPART registry (42 centers from Germany and Luxembourg). Sensitivity analyses were conducted to explore validity and stability. Using 2 clusters, patients were primarily separated by the absence or presence of clinical symptoms. Within the cluster of symptomatic patients, the main difference between patients with acute limb ischemia presentation and nonemergency symptomatic patients was PAA diameter. When using 6 clusters, patients were primarily grouped by comorbidities, with patients with acute limb ischemia forming a separate cluster. Despite markedly different risk profiles, perioperative complication rates appeared to be positively associated with the proportion of emergency patients. However, clusters with a higher proportion of patients having any symptoms before treatment experienced a lower rate of perioperative complications.

CONCLUSIONS

The conducted analyses revealed both an insight to the public health reality of PAA care as well as patients with PAA at elevated risk for adverse outcomes. This analysis suggests that the preoperative clinic is a far more crucial adjunct to the patient's preoperative risk assessment than the patient's epidemiological profile by itself.

Computational clustering reveals differentiated coronary artery calcium progression at prevalent levels of pulse wave velocity by classifying high-risk patients

Many studies found that increased arterial stiffness is significantly associated with the presence and progression of Coronary Calcium Score (CCS). However, none so far have used machine learning algorithms to improve their value. Therefore, this study aims to evaluate the association between carotid-femoral Pulse Wave Velocity (cfPWV) and CCS score through computational clustering. We conducted a retrospective cross-sectional study using data from a cardiovascular risk screening program that included 377 participants. We used an unsupervised clustering algorithm using age, weight, height, blood pressure, heart rate, and cfPWV as input variables. Differences between cluster groups were analyzed through Chi-square and T-student tests. The association between (i) cfPWV and age groups, (ii) log (CCS) and age groups, and (iii) cfPWV and log(CCS) were addressed through linear regression analysis. Clusters were labeled post hoc based on cardiovascular risk. A "higher-risk group" had significantly higher left (0.76 vs. 0.70 mm, P < 0.001) and right (0.71 vs. 0.66 mm, P = 0.003) intima-media thickness, CCS (42 vs. 4 Agatston units, P = 0.012), and ascending (3.40 vs. 3.20 cm, P < 0.001) and descending (2.60 vs. 2.37 cm, P < 0.001) aorta diameters. Association with age appeared linear for cfPWV and exponential for log (CCS). The progression of the log (CCS) and cfPWV through age groups was steeper in the "higher-risk group" than in the "lower-risk group". cfPWV strongly correlated with CCS, and CCS progression over cfPWV differed among clusters. This finding could improve PWV as a "gate-keeper" of CCS testing and potentially enhance cardiovascular risk stratification.

Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning

Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan−Meier survival curves among the three phenotypes had significant difference (pairwise comparison p < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29−3.37, p = 0.003), and 0.26 (95%CI 0.11−0.61, p = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition.

Machine learning approaches to improve disease management of patients with rheumatoid arthritis: review and future directions

INTRODUCTION

Although the management of rheumatoid arthritis (RA) has improved in major way over the last decades, this disease still leads to an important burden for patients and society, and there is a need to develop more personalized approaches. Machine learning (ML) methods are more and more used in health-related studies and can be applied to different sorts of data (clinical, radiological, or 'omics' data). Such approaches may improve the management of patients with RA.

AREAS COVERED

In this paper, we propose a review regarding ML approaches applied to RA. A scoping literature search was performed in PubMed, in September 2021 using the following MeSH terms: 'arthritis, rheumatoid' and 'machine learning'. Based on this search, the usefulness of ML methods for RA diagnosis, monitoring, and prediction of response to treatment and RA outcomes, is discussed.

EXPERT OPINION

ML methods have the potential to revolutionize RA-related research and improve disease management and patient care. Nevertheless, these models are not yet ready to contribute fully to rheumatologists' daily practice. Indeed, these methods raise technical, methodological, and ethical issues, which should be addressed properly to allow their implementation. Collaboration between data scientists, clinical researchers, and physicians is therefore required to move this field forward.

Arterial-Ventricular Coupling Impairment is Evidenced in Both Normal and Ischemic Subjects by Applying Cluster Analysis

INTRODUCTION

Left ventricular (LV) interaction with the arterial system (arterial-ventricular coupling, AVC) is a central determinant of cardiovascular performance and cardiac energetics. Stress Echocardiography (SE) constitutes a valuable clinical tool in both diagnosis and risk stratification of patients with suspected and established coronary artery disease. Cluster Analysis (CA), an unsupervised Machine Learning technique, defines an exploratory statistical method which can be used to uncover natural groups within data.

OBJECTIVE

To evaluate the capacity of CA to identify uncoupled groups with ischemic condition based on SE baseline information.

MATERIAL AND METHODS

CA was applied to SE data acquired at baseline and peak exercise (PE) conditions. Obtained clusters were evaluated in terms of coupling conditions and LV wall motility alterations.

RESULTS

Inter cluster significant AVC differences were obtained in terms of baseline data and changes in wall motility, confirmed by CA applied to PE data.

CONCLUSION

AVC impairment was evidenced in both normal and ischemic subjects by applying CA.

Artificial intelligence applied to support medical decisions for the automatic analysis of echocardiogram images: A systematic review

The echocardiogram is a test that is widely used in Heart Disease Diagnoses. However, its analysis is largely dependent on the physician's experience. In this regard, artificial intelligence has become an essential technology to assist physicians. This study is a Systematic Literature Review (SLR) of primary state-of-the-art studies that used Artificial Intelligence (AI) techniques to automate echocardiogram analyses. Searches on the leading scientific article indexing platforms using a search string returned approximately 1400 articles. After applying the inclusion and exclusion criteria, 118 articles were selected to compose the detailed SLR. This SLR presents a thorough investigation of AI applied to support medical decisions for the main types of echocardiogram (Transthoracic, Transesophageal, Doppler, Stress, and Fetal). The article's data extraction indicated that the primary research interest of the studies comprised four groups: 1) Improvement of image quality; 2) identification of the cardiac window vision plane; 3) quantification and analysis of cardiac functions, and; 4) detection and classification of cardiac diseases. The articles were categorized and grouped to show the main contributions of the literature to each type of ECHO. The results indicate that the Deep Learning (DL) methods presented the best results for the detection and segmentation of the heart walls, right and left atrium and ventricles, and classification of heart diseases using images/videos obtained by echocardiography. The models that used Convolutional Neural Network (CNN) and its variations showed the best results for all groups. The evidence produced by the results presented in the tabulation of the studies indicates that the DL contributed significantly to advances in echocardiogram automated analysis processes. Although several solutions were presented regarding the automated analysis of ECHO, this area of research still has great potential for further studies to improve the accuracy of results already known in the literature.

Artificial Intelligence and Echocardiography

Artificial intelligence (AI) is evolving in the field of diagnostic medical imaging, including echocardiography. Although the dynamic nature of echocardiography presents challenges beyond those of static images from X-ray, computed tomography, magnetic resonance, and radioisotope imaging, AI has influenced all steps of echocardiography, from image acquisition to automatic measurement and interpretation. Considering that echocardiography often is affected by inter-observer variability and shows a strong dependence on the level of experience, AI could be extremely advantageous in minimizing observer variation and providing reproducible measures, enabling accurate diagnosis. Currently, most reported AI applications in echocardiographic measurement have focused on improved image acquisition and automation of repetitive and tedious tasks; however, the role of AI applications should not be limited to conventional processes. Rather, AI could provide clinically important insights from subtle and non-specific data, such as changes in myocardial texture in patients with myocardial disease. Recent initiatives to develop large echocardiographic databases can facilitate development of AI applications. The ultimate goal of applying AI to echocardiography is automation of the entire process of echocardiogram analysis. Once automatic analysis becomes reliable, workflows in clinical echocardiographic will change radically. The human expert will remain the master controlling the overall diagnostic process, will not be replaced by AI, and will obtain significant support from AI systems to guide acquisition, perform measurements, and integrate and compare data on request.