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Biological processes are incredibly complex—integrating molecular signaling networks involved in multicellular communication and function, thus maintaining homeostasis. Dysfunction of these processes can result in the disruption of homeostasis, leading to the development of several disease processes including atherosclerosis. We have significantly advanced our understanding of bioprocesses in atherosclerosis, and in doing so, we are beginning to appreciate the complexities, intricacies, and heterogeneity atherosclerosi. We are also now better equipped to acquire, store, and process the vast amount of biological data needed to shed light on the biological circuitry involved. Such data can be analyzed within machine learning frameworks to better tease out such complex relationships. Indeed, there has been an increasing number of studies applying machine learning methods for patient risk stratification based on comorbidities, multi-modality image processing, and biomarker discovery pertaining to atherosclerotic plaque formation. Here, we focus on current applications of machine learning to provide insight into atherosclerotic plaque formation and better understand atherosclerotic plaque progression in patients with cardiovascular disease.

Aims We aimed to evaluate whether traditional risk scores [short-term, ‘psoriasis-modified’ (multiplied by 1.5) and lifetime] were able to capture high cardiovascular disease (CVD) risk as defined by the presence of atherosclerotic plaques in coronary, femoral, or carotid arteries in psoriasis. Methods and results We used two prospectives obseravational cohorts. European cohort: femoral and carotid atherosclerotic plaques were evaluated by ultrasound in 73 psoriasis patients. Lifetime CVD risk (LTCVR) was evaluated with QRISK-LT; short-term CVD risk was evaluated with SCORE and psoriasis-modified SCORE. American cohort: 165 patients underwent coronary computed tomography angiography to assess presence of coronary plaques. LTCVR was evaluated with atherosclerotic cardiovascular disease (ASCVD-LT) lifetime; short-term CVD risk was evaluated with ASCVD and psoriasis-modified ASCVD. European cohort: subclinical atherosclerosis was present in 51% of patients. QRISK-LT identified 64% of patients with atherosclerosis missing a high proportion (35%) with atheroma plaque (P < 0.05). The percentage of patients with atherosclerosis identified by QRISK-LT was significantly higher than those detected by SCORE (0%) and modified SCORE (10%). American cohort: subclinical atherosclerosis was present in 54% of patients. ASCVD-LT captured 54% of patients with coronary plaques missing a high proportion (46%) with coronary plaque (P < 0.05). The percentage of patients with atheroma plaques detected with ASCVD and modified ASCVD were only 20% and 45%, respectively. Conclusions Application of lifetime, short-term and ‘psoriasis-modified’ risk scores did not accurately capture psoriasis patients at high CVD risk.

The choice to postpone treatment while awaiting genetic testing can result in significant delay in definitive therapies in severely pancytopenic patients. Conversely, inherited bone marrow failure (BMF) misdiagnosis can expose patients to ineffectual and expensive therapies, toxic transplant conditioning regimens, and inappropriate use of an affected family member as a stem cell donor. To predict the likelihood of patients having acquired or inherited BMF, we developed a two-step data-driven machine-learning model using 25 clinical and laboratory variables typically recorded at the initial clinical encounter. For model's development, patients were labeled as having acquired or inherited BMF depending on their genomic data. Datasets were unbiasedly clustered and an ensemble model was trained with cases from the largest cluster of the training cohort (n=359) and validated with an independent cohort (n=127). Cluster A, the largest group, was mostly immune or inherited aplastic anemia, whereas Cluster B was composed of underrepresented BMF phenotypes, and not included in the next step of data modeling due to small sample size. The ensemble model Cluster A-specific was accurate (89%) to predict BMF etiology, correctly predicting inherited and likely immune BMF in 79% and 92% of cases, respectively. Our model represents a practical guide for BMF diagnosis and highlights the importance of clinical and laboratory variables in the initial evaluation, particularly telomere length. Our tool can be potentially used by general hematologists and health care providers not specialized in BMF, and in under-resourced centers, to prioritize patients for genetic testing or for expeditious treatment.

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Genetic testing has been increasingly used to assist with differential diagnosis of acquired vs inherited bone marrow failure syndromes (IBMFS), a group of rare and heterogeneous diseases. However, the assay is still costly and not routinely available for many hematologists. To improve decision-making for genetic testing, we developed a genomic-based machine-learning model based on a two-step data-driven clustering and classification process to predict the likelihood of BMF patients having either an acquired or inherited disease based on 27 clinical and laboratory variables recorded at initial clinical encounter. Clinical records from two independent cohorts of patients screened for pathogenic variants in genes associated with IBMFS were included in this study: the NIH cohort with 441 consecutive patients followed at the NHLBI and NCI, and the USP cohort with 172 consecutive patients from the Medical School of Ribeirão Preto/USP. In a binary target classification, cases were labeled as inherited if they had a pathogenic/likely pathogenic disease-causing variant and as acquired when they had benign or likely benign variants or negative genetic test, regardless of patients' clinical diagnoses. K-means clustering was first applied to resolve our highly dimensional data into two main clusters (Clusters A and B). An optimized bootstrap aggregation ensemble Cluster A specific was trained with cases from the NIH cohort (n=359). The model was then validated with Cluster A cases from the external USP cohort (n=127). The binary classification task was utilized to predict the etiology of BMF cases, labeled as acquired or inherited depending on patients' genomic data. At first, unsupervised clustering separately grouped datasets into Cluster A, the largest group mostly represented by aplastic anemia (AA), and Cluster B, those underrepresented in our cohort including some classical IBMFS at early disease onset. The ensemble model Cluster A-specific was accurate to predict the BMF etiology in 88% of cases, correctly predicting inherited and likely immune BMF in 72% and 92% of cases, respectively. Out of the 27 initial clinical variables included in the model, 25 were found to be important for prediction. Telomere length (TL), age, and clinical variables were most important for the model's predictive accuracy, highlighting that a comprehensive history and physical examination encompassing all organ systems is imperative. Based on our model, genetic testing must be considered for patients in Cluster A predicted to have inherited disease and also for patients in Cluster B as no specific model was available but they were more likely to have IBMFS in comparison to Cluster A (50% vs 30%). We also recommend genetic screening in patients from Cluster A predicted to have acquired disease who are children (age <18 years who may not have clinical signs of IBMFS), have consanguinity in the family, have a diagnosis of myelodysplastic syndromes with or without suspicion for familial predisposition to myeloid malignancies (all cases where the model had limited prediction). A model without TL, an assay that can also be limited in low-resource centers, underperformed for prediction of inherited cases with sensitivity of 55%, highlighting the importance of TL measurement for the model's performance. Our machine-learning model reproduced the clinical knowledge used by clinicians specialized in BMF and accurately predicted BMF etiology in 88% of cases. The model was particularly accurate for differential diagnosis of immune AA in adults, which may allow for selections of patients in whom rapidly starting immunosuppression rather than waiting weeks for genetic results is preferable. Clinical variables were strong predictors and adult patients with severe AA rarely had an inherited disease without a positive family history, a suggestive phenotype of IBMFS, or consanguinity being present. The generalizability of our model indicates that this tool can be used by hematologists not specialized in BMF to prioritize patients that would benefit from genetic testing. TL was a top predictor and a key variable for this model's accuracy. Implementation of TL measurement may be critical for differential diagnosis of BMF, especially in low-resource centers where genetic testing is not feasible or readily available. We plan to continue adding to the model to better predict IBMFS cases that were underrepresented in the current cohort. Disclosures Calado: Instituto Butantan: Consultancy; Agios: Membership on an entity's Board of Directors or advisory committees; Alexion Brasil: Consultancy; Novartis Brasil: Honoraria; Team Telomere, Inc.: Membership on an entity's Board of Directors or advisory committees; AA&MDS International Foundation: Research Funding. Young: Novartis: Research Funding.