Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants.

BACKGROUND: Identifying people at risk of cardiovascular diseases (CVD) is a cornerstone of preventative cardiology. Risk prediction models currently recommended by clinical guidelines are typically based on a limited number of predictors with sub-optimal performance across all patient groups. Data-driven techniques based on machine learning (ML) might improve the performance of risk predictions by agnostically discovering novel risk predictors and learning the complex interactions between them. We tested (1) whether ML techniques based on a state-of-the-art automated ML framework (AutoPrognosis) could improve CVD risk prediction compared to traditional approaches, and (2) whether considering non-traditional variables could increase the accuracy of CVD risk predictions. METHODS AND FINDINGS: Using data on 423,604 participants without CVD at baseline in UK Biobank, we developed a ML-based model for predicting CVD risk based on 473 available variables. Our ML-based model was derived using AutoPrognosis, an algorithmic tool that automatically selects and tunes ensembles of ML modeling pipelines (comprising data imputation, feature processing, classification and calibration algorithms). We compared our model with a well-established risk prediction algorithm based on conventional CVD risk factors (Framingham score), a Cox proportional hazards (PH) model based on familiar risk factors (i.e, age, gender, smoking status, systolic blood pressure, history of diabetes, reception of treatments for hypertension and body mass index), and a Cox PH model based on all of the 473 available variables. Predictive performances were assessed using area under the receiver operating characteristic curve (AUC-ROC). Overall, our AutoPrognosis model improved risk prediction (AUC-ROC: 0.774, 95% CI: 0.768-0.780) compared to Framingham score (AUC-ROC: 0.724, 95% CI: 0.720-0.728, p < 0.001), Cox PH model with conventional risk factors (AUC-ROC: 0.734, 95% CI: 0.729-0.739, p < 0.001), and Cox PH model with all UK Biobank variables (AUC-ROC: 0.758, 95% CI: 0.753-0.763, p < 0.001). Out of 4,801 CVD cases recorded within 5 years of baseline, AutoPrognosis was able to correctly predict 368 more cases compared to the Framingham score. Our AutoPrognosis model included predictors that are not usually considered in existing risk prediction models, such as the individuals' usual walking pace and their self-reported overall health rating. Furthermore, our model improved risk prediction in potentially relevant sub-populations, such as in individuals with history of diabetes. We also highlight the relative benefits accrued from including more information into a predictive model (information gain) as compared to the benefits of using more complex models (modeling gain). CONCLUSIONS: Our AutoPrognosis model improves the accuracy of CVD risk prediction in the UK Biobank population. This approach performs well in traditionally poorly served patient subgroups. Additionally, AutoPrognosis uncovered novel predictors for CVD disease that may now be tested in prospective studies. We found that the "information gain" achieved by considering more risk factors in the predictive model was significantly higher than the "modeling gain" achieved by adopting complex predictive models

Does pulmonary rehabilitation address cardiovascular risk factors in patients with COPD?

Background Patients with COPD have an increased risk of cardiovascular disease. Whilst pulmonary rehabilitation has proven benefit for exercise tolerance and quality of life, any effect on cardiovascular risk has not been fully investigated. We hypothesised that pulmonary rehabilitation, through the exercise and nutritional intervention, would address these factors. Methods Thirty-two stable patients with COPD commenced rehabilitation, and were compared with 20 age and gender matched controls at baseline assessment. In all subjects, aortic pulse wave velocity (PWV) an independent non-invasive predictor of cardiovascular risk, blood pressure (BP), interleukin-6 (IL-6) and fasting glucose and lipids were determined. These measures, and the incremental shuttle walk test (ISWT) were repeated in the patients who completed pulmonary rehabilitation. Results On commencement of rehabilitation aortic PWV was increased in patients compared with controls (p < 0.05), despite mean BP, age and gender being similar. The IL-6 was also increased (p < 0.05). Twenty-two patients completed study assessments. In these subjects, rehabilitation reduced mean (SD) aortic PWV (9.8 (3.0) to 9.3 (2.7) m/s (p < 0.05)), and systolic and diastolic BP by 10 mmHg and 5 mmHg respectively (p < 0.01). Total cholesterol and ISWT also improved (p < 0.05). On linear regression analysis, the reduction in aortic PWV was attributed to reducing the BP. Conclusion Cardiovascular risk factors including blood pressure and thereby aortic stiffness were improved following a course of standard multidisciplinary pulmonary rehabilitation in patients with COPD

Cardiovascular and musculskeletal co-morbidities in patients with alpha 1 antitrypsin deficiency

Background Determining the presence and extent of co-morbidities is fundamental in assessing patients with chronic respiratory disease, where increased cardiovascular risk, presence of osteoporosis and low muscle mass have been recognised in several disease states. We hypothesised that the systemic consequences are evident in a further group of subjects with COPD due to Alpha-1 Antitrypsin Deficiency (A1ATD), yet are currently under-recognised. Methods We studied 19 patients with PiZZ A1ATD COPD and 20 age, sex and smoking matched controls, all subjects free from known cardiovascular disease. They underwent spirometry, haemodynamic measurements including aortic pulse wave velocity (aPWV), an independent predictor or cardiovascular risk, dual energy X-ray absorptiometry to determine body composition and bone mineral density. Results The aPWV was greater in patients: 9.9(2.1) m/s than controls: 8.5(1.6) m/s, p = 0.03, despite similar mean arterial pressure (MAP). The strongest predictors of aPWV were age, FEV1% predicted and MAP (all p < 0.01). Osteoporosis was present in 8/19 patients (2/20 controls) and was previously unsuspected in 7 patients. The fat free mass and bone mineral density were lower in patients than controls (p < 0.001). Conclusions Patients with A1ATD related COPD have increased aortic stiffness suggesting increased risk of cardiovascular disease and evidence of occult musculoskeletal changes, all likely to contribute hugely to overall morbidity and mortality

The Sloan Bright Arcs Survey : Six Strongly Lensed Galaxies at z=0.4-1.4

We present new results of our program to systematically search for strongly lensed galaxies in the Sloan Digital Sky Survey (SDSS) imaging data. In this study six strong lens systems are presented which we have confirmed with follow-up spectroscopy and imaging using the 3.5m telescope at the Apache Point Observatory. Preliminary mass models indicate that the lenses are group-scale systems with velocity dispersions ranging from 466-878 km s^{-1} at z=0.17-0.45 which are strongly lensing source galaxies at z=0.4-1.4. Galaxy groups are a relatively new mass scale just beginning to be probed with strong lensing. Our sample of lenses roughly doubles the confirmed number of group-scale lenses in the SDSS and complements ongoing strong lens searches in other imaging surveys such as the CFHTLS (Cabanac et al 2007). As our arcs were discovered in the SDSS imaging data they are all bright ($r\lesssim22$), making them ideally suited for detailed follow-up studies.Comment: 13 pages, 3 figures, submitted to ApJL, the Sloan Bright Arcs page is located here: http://home.fnal.gov/~kubo/brightarcs.htm

The Sloan Bright Arcs Survey: Four Strongly Lensed Galaxies with Redshift >2

We report the discovery of four very bright, strongly-lensed galaxies found via systematic searches for arcs in Sloan Digital Sky Survey Data Release 5 and 6. These were followed-up with spectroscopy and imaging data from the Astrophysical Research Consortium 3.5m telescope at Apache Point Observatory and found to have redshift $z>2.0$. With isophotal magnitudes $r = 19.2 - 20.4$ and 3\arcsec-diameter magnitudes $r = 20.0 - 20.6$, these systems are some of the brightest and highest surface brightness lensed galaxies known in this redshift range. In addition to the magnitudes and redshifts, we present estimates of the Einstein radii, which range from 5.0 \arcsec to 12.7 \arcsec, and use those to derive the enclosed masses of the lensing galaxies