Deep Learning to Estimate Cardiovascular Risk From Chest Radiographs : A Risk Prediction Study.

BACKGROUND: Guidelines for primary prevention of atherosclerotic cardiovascular disease (ASCVD) recommend a risk calculator (ASCVD risk score) to estimate 10-year risk for major adverse cardiovascular events (MACE). Because the necessary inputs are often missing, complementary approaches for opportunistic risk assessment are desirable. OBJECTIVE: To develop and test a deep-learning model (CXR CVD-Risk) that estimates 10-year risk for MACE from a routine chest radiograph (CXR) and compare its performance with that of the traditional ASCVD risk score for implications for statin eligibility. DESIGN: Risk prediction study. SETTING: Outpatients potentially eligible for primary cardiovascular prevention. PARTICIPANTS: The CXR CVD-Risk model was developed using data from a cancer screening trial. It was externally validated in 8869 outpatients with unknown ASCVD risk because of missing inputs to calculate the ASCVD risk score and in 2132 outpatients with known risk whose ASCVD risk score could be calculated. MEASUREMENTS: 10-year MACE predicted by CXR CVD-Risk versus the ASCVD risk score. RESULTS: Among 8869 outpatients with unknown ASCVD risk, those with a risk of 7.5% or higher as predicted by CXR CVD-Risk had higher 10-year risk for MACE after adjustment for risk factors (adjusted hazard ratio [HR], 1.73 [95% CI, 1.47 to 2.03]). In the additional 2132 outpatients with known ASCVD risk, CXR CVD-Risk predicted MACE beyond the traditional ASCVD risk score (adjusted HR, 1.88 [CI, 1.24 to 2.85]). LIMITATION: Retrospective study design using electronic medical records. CONCLUSION: On the basis of a single CXR, CXR CVD-Risk predicts 10-year MACE beyond the clinical standard and may help identify individuals at high risk whose ASCVD risk score cannot be calculated because of missing data. PRIMARY FUNDING SOURCE: None.

Deep learning analysis of epicardial adipose tissue to predict cardiovascular risk in heavy smokers.

BACKGROUND: Heavy smokers are at increased risk for cardiovascular disease and may benefit from individualized risk quantification using routine lung cancer screening chest computed tomography. We investigated the prognostic value of deep learning-based automated epicardial adipose tissue quantification and compared it to established cardiovascular risk factors and coronary artery calcium. METHODS: We investigated the prognostic value of automated epicardial adipose tissue quantification in heavy smokers enrolled in the National Lung Screening Trial and followed for 12.3 (11.9-12.8) years. The epicardial adipose tissue was segmented and quantified on non-ECG-synchronized, non-contrast low-dose chest computed tomography scans using a validated deep-learning algorithm. Multivariable survival regression analyses were then utilized to determine the associations of epicardial adipose tissue volume and density with all-cause and cardiovascular mortality (myocardial infarction and stroke). RESULTS: Here we show in 24,090 adult heavy smokers (59% men; 61 ± 5 years) that epicardial adipose tissue volume and density are independently associated with all-cause (adjusted hazard ratios: 1.10 and 1.38; P < 0.001) and cardiovascular mortality (adjusted hazard ratios: 1.14 and 1.78; P < 0.001) beyond demographics, clinical risk factors, body habitus, level of education, and coronary artery calcium score. CONCLUSIONS: Our findings suggest that automated assessment of epicardial adipose tissue from low-dose lung cancer screening images offers prognostic value in heavy smokers, with potential implications for cardiovascular risk stratification in this high-risk population.

Heavy smokers are at increased risk of poor health outcomes, particularly outcomes related to cardiovascular disease. We explore how fat surrounding the heart, known as epicardial adipose tissue, may be an indicator of the health of heavy smokers. We use an artificial intelligence system to measure the heart fat on chest scans of heavy smokers taken during a lung cancer screening trial and following their health for 12 years. We find that higher amounts and denser epicardial adipose tissue are linked to an increased risk of death from any cause, specifically from heart-related issues, even when considering other health factors. This suggests that measuring epicardial adipose tissue during lung cancer screenings could be a valuable tool for identifying heavy smokers at greater risk of heart problems and death, possibly helping to guide their medical management and improve their cardiovascular health.

Deep learning to estimate lung disease mortality from chest radiographs.

Prevention and management of chronic lung diseases (asthma, lung cancer, etc.) are of great importance. While tests are available for reliable diagnosis, accurate identification of those who will develop severe morbidity/mortality is currently limited. Here, we developed a deep learning model, CXR Lung-Risk, to predict the risk of lung disease mortality from a chest x-ray. The model was trained using 147,497 x-ray images of 40,643 individuals and tested in three independent cohorts comprising 15,976 individuals. We found that CXR Lung-Risk showed a graded association with lung disease mortality after adjustment for risk factors, including age, smoking, and radiologic findings (Hazard ratios up to 11.86 [8.64-16.27]; p < 0.001). Adding CXR Lung-Risk to a multivariable model improved estimates of lung disease mortality in all cohorts. Our results demonstrate that deep learning can identify individuals at risk of lung disease mortality on easily obtainable x-rays, which may improve personalized prevention and treatment strategies.

Deep Learning Analysis of Chest Radiographs to Triage Patients with Acute Chest Pain Syndrome.

Background Patients presenting to the emergency department (ED) with acute chest pain (ACP) syndrome undergo additional testing to exclude acute coronary syndrome (ACS), pulmonary embolism (PE), or aortic dissection (AD), often yielding negative results. Purpose To assess whether deep learning (DL) analysis of the initial chest radiograph may help triage patients with ACP syndrome more efficiently. Materials and Methods This retrospective study used electronic health records of patients with ACP syndrome at presentation who underwent a combination of chest radiography and additional cardiovascular or pulmonary imaging or stress tests at two hospitals (Massachusetts General Hospital [MGH], Brigham and Women's Hospital [BWH]) between January 2005 and December 2015. A DL model was trained on 23 005 patients from MGH to predict a 30-day composite end point of ACS, PE, AD, and all-cause mortality based on chest radiographs. Area under the receiver operating characteristic curve (AUC) was used to compare performance between models (model 1: age + sex; model 2: model 1 + conventional troponin or d-dimer positivity; model 3: model 2 + DL predictions) in internal and external test sets from MGH and BWH, respectively. Results At MGH, 5750 patients (mean age, 59 years ± 17 [SD]; 3329 men, 2421 women) were evaluated. Model 3, which included DL predictions, significantly improved discrimination of those with the composite outcome compared with models 2 and 1 (AUC, 0.85 [95% CI: 0.84, 0.86] vs 0.76 [95% CI: 0.74, 0.77] vs 0.62 [95% CI: 0.60 0.64], respectively; P < .001 for all). When using a sensitivity threshold of 99%, 14% (813 of 5750) of patients could be deferred from cardiovascular or pulmonary testing for differential diagnosis of ACP syndrome using model 3 compared with 2% (98 of 5750) of patients using model 2 (P < .001). Model 3 maintained its diagnostic performance in different age, sex, race, and ethnicity groups. In external validation at BWH (22 764 patients; mean age, 57 years ± 17; 11 470 women), trends were similar and improved after fine tuning. Conclusion Deep learning analysis of chest radiographs may facilitate more efficient triage of patients with acute chest pain syndrome in the emergency department. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Goo in this issue.

Deep Learning to Predict Mortality After Cardiothoracic Surgery Using Preoperative Chest Radiographs.

BACKGROUND: The Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM) estimates mortality risk only for certain common procedures (eg, coronary artery bypass or valve surgery) and is cumbersome, requiring greater than 60 inputs. We hypothesized that deep learning can estimate postoperative mortality risk based on a preoperative chest radiograph for cardiac surgeries in which STS-PROM scores were available (STS index procedures) or unavailable (non-STS index procedures). METHODS: We developed a deep learning model (CXR-CTSurgery) to predict postoperative mortality based on preoperative chest radiographs in 9283 patients at Massachusetts General Hospital (MGH) having cardiac surgery before April 8, 2014. CXR-CTSurgery was tested on 3615 different MGH patients and externally tested on 2840 patients from Brigham and Women's Hospital (BWH) having surgery after April 8, 2014. Discrimination for mortality was compared with the STS-PROM using the C-statistic. Calibration was assessed using the observed-to-expected ratio (O/E ratio). RESULTS: For STS index procedures, CXR-CTSurgery had a C-statistic similar to STS-PROM at MGH (CXR-CTSurgery: 0.83 vs STS-PROM: 0.88; P = .20) and BWH (0.74 vs 0.80; P = .14) testing cohorts. The CXR-CTSurgery C-statistic for non-STS index procedures was similar to STS index procedures in the MGH (0.87 vs 0.83) and BWH (0.73 vs 0.74) testing cohorts. For STS index procedures, CXR-CTSurgery had better calibration than the STS-PROM in the MGH (O/E ratio: 0.74 vs 0.52) and BWH (O/E ratio: 0.91 vs 0.73) testing cohorts. CONCLUSIONS: CXR-CTSurgery predicts postoperative mortality based on a preoperative CXR with similar discrimination and better calibration than the STS-PROM. This may be useful when the STS-PROM cannot be calculated or for non-STS index procedures.

Validation of a Deep Learning-Based Model to Predict Lung Cancer Risk Using Chest Radiographs and Electronic Medical Record Data.

Importance: Lung cancer screening with chest computed tomography (CT) prevents lung cancer death; however, fewer than 5% of eligible Americans are screened. CXR-LC, an open-source deep learning tool that estimates lung cancer risk from existing chest radiograph images and commonly available electronic medical record (EMR) data, may enable automated identification of high-risk patients as a step toward improving lung cancer screening participation. Objective: To validate CXR-LC using EMR data to identify individuals at high-risk for lung cancer to complement 2022 US Centers for Medicare & Medicaid Services (CMS) lung cancer screening eligibility guidelines. Design, Setting, and Participants: This prognostic study compared CXR-LC estimates with CMS screening guidelines using patient data from a large US hospital system. Included participants were persons who currently or formerly smoked cigarettes with an outpatient posterior-anterior chest radiograph between January 1, 2013, and December 31, 2014, with no history of lung cancer or screening CT. Data analysis was performed between May 2021 and June 2022. Exposures: CXR-LC lung cancer screening eligibility (previously defined as having a 3.297% or greater 12-year risk) based on inputs (chest radiograph image, age, sex, and whether currently smoking) extracted from the EMR. Main Outcomes and Measures: 6-year incident lung cancer. Results: A total of 14 737 persons were included in the study population (mean [SD] age, 62.6 [6.8] years; 7154 [48.5%] male; 204 [1.4%] Asian, 1051 [7.3%] Black, 432 [2.9%] Hispanic, 12 330 [85.2%] White) with a 2.4% rate of incident lung cancer over 6 years (361 patients with cancer). CMS eligibility could be determined in 6277 patients (42.6%) using smoking pack-year and quit-date from the EMR. Patients eligible by both CXR-LC and 2022 CMS criteria had a high rate of lung cancer (83 of 974 patients [8.5%]), higher than those eligible by 2022 CMS criteria alone (5 of 177 patients [2.8%]; P < .001). Patients eligible by CXR-LC but not 2022 CMS criteria also had a high 6-year incidence of lung cancer (121 of 3703 [3.3%]). In the 8460 cases (57.4%) where CMS eligibility was unknown, CXR-LC eligible patients had a 5-fold higher rate of lung cancer than ineligible (127 of 5177 [2.5%] vs 18 of 2283 [0.5%]; P < .001). Similar results were found in subgroups, including female patients and Black persons. Conclusions and Relevance: Using routine chest radiographs and other data automatically extracted from the EMR, CXR-LC identified high-risk individuals who may benefit from lung cancer screening CT.

Deep Learning to Optimize Candidate Selection for Lung Cancer CT Screening: Advancing the 2021 USPSTF Recommendations.

Background A deep learning (DL) model to identify lung cancer screening candidates based on their chest radiographs requires external validation with a recent real-world non-U.S. sample. Purpose To validate the DL model and identify added benefits to the 2021 U.S. Preventive Services Task Force (USPSTF) recommendations in a health check-up sample. Materials and Methods This single-center retrospective study included consecutive current and former smokers aged 50-80 years who underwent chest radiography during a health check-up between January 2004 and June 2018. Discrimination performance, including receiver operating characteristic curve analysis and area under the receiver operating characteristic curve (AUC) calculation, of the model for incident lung cancers was evaluated. The added value of the model to the 2021 USPSTF recommendations was investigated for lung cancer inclusion rate, proportion of selected CT screening candidates, and positive predictive value (PPV). Results For model validation, a total of 19 488 individuals (mean age, 58 years ± 6 [SD]; 18 467 [95%] men) and the subset of USPSTF-eligible individuals (n = 7835; mean age, 57 years ± 6; 7699 [98%] men) were assessed, and the AUCs for incident lung cancers were 0.68 (95% CI: 0.62, 0.73) and 0.75 (95% CI: 0.68, 0.81), respectively. In individuals with pack-year information (n = 17 390), when excluding low- and indeterminate-risk categories from the USPSTF-eligible sample, the proportion of selected CT screening candidates was reduced to 35.8% (6233 of 17 390) from 45.1% (7835 of 17 390, P < .001), with three missed lung cancers (0.2%). The cancer inclusion rate (0.3% [53 of 17 390] vs 0.3% [56 of 17 390], P = .85) and PPV (0.9% [53 of 6233] vs 0.7% [56 of 7835], P = .42) remained unaffected. Conclusion An externally validated deep learning model showed the added value to the 2021 U.S. Preventive Services Task Force recommendations for low-dose CT lung cancer screening in reducing the number of screening candidates while maintaining the inclusion rate and positive predictive value for incident lung cancer. © RSNA, 2022 Online supplemental material is available for this article.

Pre-Existing Autoimmune Disease Increases the Risk of Cardiovascular and Noncardiovascular Events After Immunotherapy.

Background: The use of immune checkpoint inhibitors (ICI) is associated with cardiovascular (CV) events, and patients with pre-existing autoimmune disease are at increased CV risk. Objectives: The aim of this study was to characterize the risk for CV events in patients with pre-existing autoimmune disease post-ICI. Methods: This was a retrospective study of 6,683 patients treated with ICIs within an academic network. Autoimmune disease prior to ICI was confirmed by chart review. Baseline characteristics and risk for CV and non-CV immune-related adverse events were compared with a matched control group (1:1 ratio) of ICI patients without autoimmune disease. Matching was based on age, sex, history of coronary artery disease, history of heart failure, and diabetes mellitus. CV events were a composite of myocardial infarction, percutaneous coronary intervention, coronary artery bypass graft, stroke, transient ischemic attack, deep venous thrombosis, pulmonary embolism, or myocarditis. Univariable and multivariable Cox proportional hazards models were used to determine the association between autoimmune disease and CV events. Results: Among 502 patients treated with ICIs, 251 patients with and 251 patients without autoimmune disease were studied. During a median follow-up period of 205 days, there were 45 CV events among patients with autoimmune disease and 22 CV events among control subjects (adjusted HR: 1.77; 95% CI: 1.04-3.03; P = 0.0364). Of the non-CV immune-related adverse events, there were increased rates of psoriasis (11.2% vs 0.4%; P < 0.001) and colitis (24.3% vs 16.7%; P = 0.045) in patients with autoimmune disease. Conclusions: Patients with autoimmune disease have an increased risk for CV and non-CV events post-ICI.

Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature.

BACKGROUND: The microvasculature, the smallest blood vessels in the body, has key roles in maintenance of organ health and tumorigenesis. The retinal fundus is a window for human in vivo noninvasive assessment of the microvasculature. Large-scale complementary machine learning-based assessment of the retinal vasculature with phenome-wide and genome-wide analyses may yield new insights into human health and disease. METHODS: We used 97 895 retinal fundus images from 54 813 UK Biobank participants. Using convolutional neural networks to segment the retinal microvasculature, we calculated vascular density and fractal dimension as a measure of vascular branching complexity. We associated these indices with 1866 incident International Classification of Diseases-based conditions (median 10-year follow-up) and 88 quantitative traits, adjusting for age, sex, smoking status, and ethnicity. RESULTS: Low retinal vascular fractal dimension and density were significantly associated with higher risks for incident mortality, hypertension, congestive heart failure, renal failure, type 2 diabetes, sleep apnea, anemia, and multiple ocular conditions, as well as corresponding quantitative traits. Genome-wide association of vascular fractal dimension and density identified 7 and 13 novel loci, respectively, that were enriched for pathways linked to angiogenesis (eg, vascular endothelial growth factor, platelet-derived growth factor receptor, angiopoietin, and WNT signaling pathways) and inflammation (eg, interleukin, cytokine signaling). CONCLUSIONS: Our results indicate that the retinal vasculature may serve as a biomarker for future cardiometabolic and ocular disease and provide insights into genes and biological pathways influencing microvascular indices. Moreover, such a framework highlights how deep learning of images can quantify an interpretable phenotype for integration with electronic health record, biomarker, and genetic data to inform risk prediction and risk modification.

Immune checkpoint inhibitors for cancer and venous thromboembolic events.

BACKGROUND: Immune checkpoint inhibitors (ICIs) are widely used cancer treatments. There are limited data on the risk for developing venous thromboembolism (VTE) among patients on an ICI. METHODS: This was a retrospective study of 2854 patients who received ICIs at a single academic centre. VTE events, defined as a composite of deep vein thrombosis or pulmonary embolism, were identified by individual chart review and blindly adjudicated using standard imaging criteria. A self-controlled risk-interval design was applied with an 'at-risk period' defined as the two-year period after and the 'control period', defined as the two-year before treatment. The hazard ratio (HR) was calculated using a fixed-effect proportional hazards model. RESULTS: Of the 2854 patients, 1640 (57.5%) were men; the mean age was 64 ± 13 years. The risk for VTE was 7.4% at 6 months and 13.8% at 1 year after starting an ICI. The rate of VTE was > 4-fold higher after starting an ICI (HR 4.98, 95% CI 3.65-8.59, p < 0.001). There was a 5.7-fold higher risk for deep vein thrombosis (HR 5.70, 95% CI 3.79-8.59, p < 0.001) and a 4.75-fold higher risk for pulmonary embolism (HR 4.75, 95% CI 3.20-7.10, p < 0.001). Comparing patients with and without a VTE event, a history of melanoma and older age predicted lower risk of VTE, while a higher Khorana risk score, history of hypertension and history of VTE predicted higher risk. CONCLUSIONS: The rate of VTE among patients on an ICI is high and increases after starting an ICI.

Association between incidental statin use and skeletal myopathies in patients treated with immune checkpoint inhibitors.

OBJECTIVES: Skeletal myopathies are highly morbid, and in rare cases even fatal, immune-related adverse events (irAE) associated with immune checkpoint inhibitors (ICI). Skeletal myopathies are also a recognized statin-associated side effect. It is unknown whether concurrent use of statins and ICIs increases the risk of skeletal myopathies. METHODS: This was a retrospective cohort study of all patients who were treated with an ICI at a single academic institution (Massachusetts General Hospital, Boston, MA, USA). The primary outcome of interest was the development of a skeletal myopathy. The secondary outcome of interest was an elevated creatine kinase level (above the upper limit of normal). RESULTS: Among 2757 patients, 861 (31.2%) were treated with a statin at the time of ICI start. Statin users were older, more likely to be male and had a higher prevalence of cardiovascular and non-cardiovascular co-morbidities. During a median follow-up of 194 days (inter quartile range 65-410), a skeletal myopathy occurred in 33 patients (1.2%) and was more common among statin users (2.7 vs. 0.9%, P < 0.001). Creatine kinase (CK) elevation was present in 16.3% (114/699) and was higher among statin users (20.0 vs. 14.3%, P = 0.067). In a multivariable Cox model, statin therapy was associated with a >2-fold higher risk for skeletal myopathy (HR, 2.19; 95% confidence interval, 1.07-4.50; P = 0.033). CONCLUSION: In this large cohort of ICI-treated patients, a higher risk was observed for skeletal myopathies and elevation in CK levels in patients undergoing concurrent statin therapy. Prospective observational studies are warranted to further elucidate the potential association between statin use and ICI-associated myopathies.

Pericardial disease in patients treated with immune checkpoint inhibitors.

BACKGROUND: There are limited data on the occurrence, associations and outcomes of pericardial effusions and pericarditis on or after treatment with immune checkpoint inhibitors (ICIs). METHODS: This was a retrospective study at a single academic center that compared 2842 consecutive patients who received ICIs with 2699 age- and cancer-type matched patients with metastatic disease who did not receive ICI. A pericardial event was defined as a composite outcome of pericarditis and new or worsening moderate or large pericardial effusion. The endpoints were obtained through chart review and were blindly adjudicated. To identify risk factors associated with a pericardial event, we compared patients who developed an event on an ICI with patients treated with an ICI who did not develop a pericardial event. Cox proportional-hazard model and logistical regression analysis were performed to study the association between ICI use and pericardial disease as well as pericardial disease and mortality. An additional 6-week landmark analysis was performed to account for lead-time bias. RESULTS: There were 42 pericardial events in the patients treated with ICI (n=2842) over 193 days (IQR: 64-411), yielding an incidence rate of 1.57 events per 100 person-years. There was a more than fourfold increase in risk of pericarditis or a pericardial effusion among patients on an ICI compared with controls not treated with ICI after adjusting for potential confounders (HR 4.37, 95% CI 2.09 to 9.14, p<0.001). Patients who developed pericardial disease while on an ICI had a trend for increased all-cause mortality compared with patients who did not develop a pericardial event (HR 1.53, 95% CI 0.99 to 2.36, p=0.05). When comparing those who developed pericardial disease after ICI treatment with those who did not, a higher dose of corticosteroid pre-ICI (>0.7 mg/kg prednisone) was associated with increased risk of pericardial disease (HR 2.56, 95% CI 1.00 to 6.57, p=0.049). CONCLUSIONS: ICI use was associated with an increased risk of development of pericardial disease among patients with cancer and a pericardial event on an ICI was associated with a trend towards increase in mortality.

Neurological Complications Acquired During Pediatric Critical Illness: Exploratory "Mixed Graphical Modeling" Analysis Using Serum Biomarker Levels.

OBJECTIVES: Neurologic complications, consisting of the acute development of a neurologic disorder, that is, not present at admission but develops during the course of illness, can be difficult to detect in the PICU due to sedation, neuromuscular blockade, and young age. We evaluated the direct relationships of serum biomarkers and clinical variables to the development of neurologic complications. Analysis was performed using mixed graphical models, a machine learning approach that allows inference of cause-effect associations from continuous and discrete data. DESIGN: Secondary analysis of a previous prospective observational study. SETTING: PICU, single quaternary-care center. PATIENTS: Individuals admitted to the PICU, younger than18 years old, with intravascular access via an indwelling catheter. INTERVENTIONS: None. MEASUREMENTS: About 101 patients were included in this analysis. Serum (days 1-7) was analyzed for glial fibrillary acidic protein, ubiquitin C-terminal hydrolase-L1, and alpha-II spectrin breakdown product 150 utilizing enzyme-linked immunosorbent assays. Serum levels of neuron-specific enolase, myelin basic protein, and S100 calcium binding protein B used in these models were reported previously. Demographic data, use of selected clinical therapies, lengths of stay, and ancillary neurologic testing (head CT, brain MRI, and electroencephalogram) results were recorded. The Mixed Graphical Model-Fast-Causal Inference-Maximum algorithm was applied to the dataset. MAIN RESULTS: About 13 of 101 patients developed a neurologic complication during their critical illness. The mixed graphical model identified peak levels of the neuronal biomarker neuron-specific enolase and ubiquitin C-terminal hydrolase-L1, and the astrocyte biomarker glial fibrillary acidic protein to be the direct causal determinants for the development of a neurologic complication; in contrast, clinical variables including age, sex, length of stay, and primary neurologic diagnosis were not direct causal determinants. CONCLUSIONS: Graphical models that include biomarkers in addition to clinical data are promising methods to evaluate direct relationships in the development of neurologic complications in critically ill children. Future work is required to validate and refine these models further, to determine if they can be used to predict which patients are at risk for/or with early neurologic complications.

The Incidence, Risk Factors, and Outcomes With 5-Fluorouracil-Associated Coronary Vasospasm.

BACKGROUND: Coronary vasospasm is a recognized side effect of 5-FU (fluorouracil). There are limited and conflicting data on the incidence, risk factors and prognostic effect of 5-FU associated vasospasm. OBJECTIVES: To assess the incidence, risk factors and prognostic implications of 5-FU coronary vasospasm among patients receiving 5-FU regimens at a single tertiary care center. METHODS: We conducted a retrospective analysis of all patients who received 5-FU at a single academic center from January 2009 to July 2019. Vasospasm was defined as the occurrence of a typical chest pain syndrome in the presence of 5-FU. The presence of associated electrocardiogram (ECG) changes and/or elevated biomarkers was used to further confirm the diagnosis. Patients with vasospasm were compared to patients treated with 5-FU without vasospasm in a 1:2 ratio. Data regarding demographics, medical history, and follow-up were collected by manual chart review. RESULTS: From approximately 4019 individual patients who received 5-FU from 2009 to 2019 at a single center, 87 (2.16%) developed vasospasm. Patients who developed vasospasm were younger (58±13 vs. 64±13 years, P = 0.001), and were less likely to have any cardiovascular risk factors (70.1% vs. 84.5%, P = 0.007). Patients with vasospasm and patients without vasospasm were otherwise similar in terms of types of cancer, stage of cancer, sex, and race. There was no significant difference in progression-free survival, overall mortality or cancer specific mortality between patients who developed vasospasm versus those who did not. CONCLUSION: In a large, single-center report of 5-FU associated vasospasm, patients who developed vasospasm were younger, had lower rates of traditional cardiovascular risk factors and had no significant difference in progression-free or overall survival compared to those who did not develop vasospasm.

Deep Learning to Estimate Biological Age From Chest Radiographs.

OBJECTIVES: The goal of this study was to assess whether a deep learning estimate of age from a chest radiograph image (CXR-Age) can predict longevity beyond chronological age. BACKGROUND: Chronological age is an imperfect measure of longevity. Biological age, a measure of overall health, may improve personalized care. This paper proposes a new way to estimate biological age using a convolutional neural network that takes as input a CXR image and outputs a chest x-ray age (in years) as a measure of long-term mortality risk. METHODS: CXR-Age was developed using CXR from 116,035 individuals and validated in 2 held-out testing sets: 1) 75% of the CXR arm of PLCO (Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial) (N = 40,967); and 2) the CXR arm of NLST (National Lung Screening Trial) (N = 5,414). CXR-Age was compared to chronological age and a multivariable regression model of chronological age, risk factors, and radiograph findings to predict all-cause and cardiovascular mortality with a maximum 23 years and 13 years of follow-up, respectively. The primary outcome was observed mortality; results are provided for the testing datasets only. RESULTS: In the PLCO testing dataset, a 5-year increase in CXR-Age carried a higher risk of all-cause mortality than a 5-year increase in chronological age (CXR-Age hazard ratio [HR]: 2.26 [95% confidence interval (CI): 2.24 to 2.29] vs. chronological age HR: 1.77 [95% CI: 1.75 to 1.78]; p < 0.001). A similar pattern was found for cardiovascular mortality (CXR-Age cause-specific HR: 2.45 per 5 years [95% CI: 2.34 to 2.56] vs. chronological age HR: 1.82 per 5 years [95% CI: 1.74 to 1.90]). Similar results were seen for both outcomes in the NLST external testing dataset. Adding CXR-Age to the multivariable model resulted in significant improvements for predicting both outcomes in both testing datasets (p < 0.001 for all comparisons). CONCLUSIONS: Based on a CXR image, CXR-Age predicted long-term all-cause and cardiovascular mortality.

A Pipeline for Integrated Theory and Data-Driven Modeling of Biomedical Data.

Genome sequencing technologies have the potential to transform clinical decision making and biomedical research by enabling high-throughput measurements of the genome at a granular level. However, to truly understand mechanisms of disease and predict the effects of medical interventions, high-throughput data must be integrated with demographic, phenotypic, environmental, and behavioral data from individuals. Further, effective knowledge discovery methods must infer relationships between these data types. We recently proposed a pipeline (CausalMGM) to achieve this. CausalMGM uses probabilistic graphical models to infer the relationships between variables in the data; however, CausalMGM's graphical structure learning algorithm can only handle small datasets efficiently. We propose a new methodology (piPref-Div) that selects the most informative variables for CausalMGM, enabling it to scale. We validate the efficacy of piPref-Div against other feature selection methods and demonstrate how the use of the full pipeline improves breast cancer outcome prediction and provides biologically interpretable views of gene expression data.

Association Between Immune Checkpoint Inhibitors With Cardiovascular Events and Atherosclerotic Plaque.

BACKGROUND: Immune checkpoint inhibitors (ICIs) treat an expanding range of cancers. Consistent basic data suggest that these same checkpoints are critical negative regulators of atherosclerosis. Therefore, our objectives were to test whether ICIs were associated with accelerated atherosclerosis and a higher risk of atherosclerosis-related cardiovascular events. METHODS: The study was situated in a single academic medical center. The primary analysis evaluated whether exposure to an ICI was associated with atherosclerotic cardiovascular events in 2842 patients and 2842 controls matched by age, a history of cardiovascular events, and cancer type. In a second design, a case-crossover analysis was performed with an at-risk period defined as the 2-year period after and the control period as the 2-year period before treatment. The primary outcome was a composite of atherosclerotic cardiovascular events (myocardial infarction, coronary revascularization, and ischemic stroke). Secondary outcomes included the individual components of the primary outcome. In addition, in an imaging substudy (n=40), the rate of atherosclerotic plaque progression was compared from before to after the ICI was started. All study measures and outcomes were blindly adjudicated. RESULTS: In the matched cohort study, there was a 3-fold higher risk for cardiovascular events after starting an ICI (hazard ratio, 3.3 [95% CI, 2.0-5.5]; P<0.001). There was a similar increase in each of the individual components of the primary outcome. In the case-crossover, there was also an increase in cardiovascular events from 1.37 to 6.55 per 100 person-years at 2 years (adjusted hazard ratio, 4.8 [95% CI, 3.5-6.5]; P<0.001). In the imaging study, the rate of progression of total aortic plaque volume was >3-fold higher with ICIs (from 2.1%/y before 6.7%/y after). This association between ICI use and increased atherosclerotic plaque progression was attenuated with concomitant use of statins or corticosteroids. CONCLUSIONS: Cardiovascular events were higher after initiation of ICIs, potentially mediated by accelerated progression of atherosclerosis. Optimization of cardiovascular risk factors and increased awareness of cardiovascular risk before, during, and after treatment should be considered among patients on an ICI.