Pulmonary hypertension in association with lung disease : quantitative CT and artificial intelligence to the rescue? State-of-the-art review

Accurate phenotyping of patients with pulmonary hypertension (PH) is an integral part of informing disease classification, treatment, and prognosis. The impact of lung disease on PH outcomes and response to treatment remains a challenging area with limited progress. Imaging with computed tomography (CT) plays an important role in patients with suspected PH when assessing for parenchymal lung disease, however, current assessments are limited by their semi-qualitative nature. Quantitative chest-CT (QCT) allows numerical quantification of lung parenchymal disease beyond subjective visual assessment. This has facilitated advances in radiological assessment and clinical correlation of a range of lung diseases including emphysema, interstitial lung disease, and coronavirus disease 2019 (COVID-19). Artificial Intelligence approaches have the potential to facilitate rapid quantitative assessments. Benefits of cross-sectional imaging include ease and speed of scan acquisition, repeatability and the potential for novel insights beyond visual assessment alone. Potential clinical benefits include improved phenotyping and prediction of treatment response and survival. Artificial intelligence approaches also have the potential to aid more focused study of pulmonary arterial hypertension (PAH) therapies by identifying more homogeneous subgroups of patients with lung disease. This state-of-the-art review summarizes recent QCT developments and potential applications in patients with PH with a focus on lung disease

Machine learning cardiac-MRI features predict mortality in newly diagnosed pulmonary arterial hypertension

Background Pulmonary arterial hypertension (PAH) is a rare but serious disease associated with high mortality if left untreated. This study aims to assess the prognostic cardiac magnetic resonance (CMR) features in PAH using machine learning. Methods 723 consecutive treatment-naive PAH patients were identified from the ASPIRE registry; 516 were included in the training and 207 in the validation cohort. A multilinear principal component analysis (MPCA) based machine learning approach was used to extract mortality and survival features throughout the cardiac cycle. The features were overlaid on the original imaging using thresholding and clustering of high- and low-risk of mortality prediction values. Results The one-year mortality rate in the validation cohort was 10%. Univariable Cox regression analysis of the combined short-axis and 4-chamber MPCA-based predictions was statistically significant (Hazard Ratios 2.1, 95% CI 1.3, 3.4, c-index = 0.70, p = .002). The MPCA features improved the one-year mortality prediction of REVEAL from c-index = 0.71 to 0.76 (p = < .001). Abnormalities in the end-systolic interventricular septum and end-diastolic left ventricle indicated the highest risk of mortality. Conclusion The MPCA-based machine learning is an explainable time-resolved approach that allows visualisation of prognostic cardiac features throughout the cardiac cycle at population level, making this approach transparent and clinically interpretable. In addition, the added prognostic value over the REVEAL risk score and CMR volumetric measurements allows for a more accurate prediction of one-year mortality risk in PAH

Pension reform, the stock market, capital formation and economic growth: a critical commentary on the World Bank's proposals

Abstract Proposing far-reaching reforms to the pension systems, the World Bank has recently suggested that the existing pay-as-you-go pension systems in many rich as well as poor countries, should be replaced by fully funded, mandatory, preferably private pensions, as the main pillars of the new system. It argues that these reforms will not only benefit the pensioners, but also enhance savings, promote capital formation and economic development. This paper provides a critical examination of the Bank's theses and concludes that it has adopted a one-sided view of the relationships between the key critical variables. The proposed reform may therefore neither protect the old nor achieve faster economic growth

Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements

Background Right atrial (RA) area predicts mortality in patients with pulmonary hypertension, and is recommended by the European Society of Cardiology/European Respiratory Society pulmonary hypertension guidelines. The advent of deep learning may allow more reliable measurement of RA areas to improve clinical assessments. The aim of this study was to automate cardiovascular magnetic resonance (CMR) RA area measurements and evaluate the clinical utility by assessing repeatability, correlation with invasive haemodynamics and prognostic value. Methods A deep learning RA area CMR contouring model was trained in a multicentre cohort of 365 patients with pulmonary hypertension, left ventricular pathology and healthy subjects. Inter-study repeatability (intraclass correlation coefficient (ICC)) and agreement of contours (DICE similarity coefficient (DSC)) were assessed in a prospective cohort (n = 36). Clinical testing and mortality prediction was performed in n = 400 patients that were not used in the training nor prospective cohort, and the correlation of automatic and manual RA measurements with invasive haemodynamics assessed in n = 212/400. Radiologist quality control (QC) was performed in the ASPIRE registry, n = 3795 patients. The primary QC observer evaluated all the segmentations and recorded them as satisfactory, suboptimal or failure. A second QC observer analysed a random subcohort to assess QC agreement (n = 1018). Results All deep learning RA measurements showed higher interstudy repeatability (ICC 0.91 to 0.95) compared to manual RA measurements (1st observer ICC 0.82 to 0.88, 2nd observer ICC 0.88 to 0.91). DSC showed high agreement comparing automatic artificial intelligence and manual CMR readers. Maximal RA area mean and standard deviation (SD) DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 is 92.4 +/- 3.5 cm(2), 91.2 +/- 4.5 cm(2) and 93.2 +/- 3.2 cm(2), respectively. Minimal RA area mean and SD DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 was 89.8 +/- 3.9 cm(2), 87.0 +/- 5.8 cm(2) and 91.8 +/- 4.8 cm(2). Automatic RA area measurements all showed moderate correlation with invasive parameters (r = 0.45 to 0.66), manual (r = 0.36 to 0.57). Maximal RA area could accurately predict elevated mean RA pressure low and high-risk thresholds (area under the receiver operating characteristic curve artificial intelligence = 0.82/0.87 vs manual = 0.78/0.83), and predicted mortality similar to manual measurements, both p < 0.01. In the QC evaluation, artificial intelligence segmentations were suboptimal at 108/3795 and a low failure rate of 16/3795. In a subcohort (n = 1018), agreement by two QC observers was excellent, kappa 0.84. Conclusion Automatic artificial intelligence CMR derived RA size and function are accurate, have excellent repeatability, moderate associations with invasive haemodynamics and predict mortality.Radiolog

Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements

Background Right atrial (RA) area predicts mortality in patients with pulmonary hypertension, and is recommended by the European Society of Cardiology/European Respiratory Society pulmonary hypertension guidelines. The advent of deep learning may allow more reliable measurement of RA areas to improve clinical assessments. The aim of this study was to automate cardiovascular magnetic resonance (CMR) RA area measurements and evaluate the clinical utility by assessing repeatability, correlation with invasive haemodynamics and prognostic value. Methods A deep learning RA area CMR contouring model was trained in a multicentre cohort of 365 patients with pulmonary hypertension, left ventricular pathology and healthy subjects. Inter-study repeatability (intraclass correlation coefficient (ICC)) and agreement of contours (DICE similarity coefficient (DSC)) were assessed in a prospective cohort (n = 36). Clinical testing and mortality prediction was performed in n = 400 patients that were not used in the training nor prospective cohort, and the correlation of automatic and manual RA measurements with invasive haemodynamics assessed in n = 212/400. Radiologist quality control (QC) was performed in the ASPIRE registry, n = 3795 patients. The primary QC observer evaluated all the segmentations and recorded them as satisfactory, suboptimal or failure. A second QC observer analysed a random subcohort to assess QC agreement (n = 1018). Results All deep learning RA measurements showed higher interstudy repeatability (ICC 0.91 to 0.95) compared to manual RA measurements (1st observer ICC 0.82 to 0.88, 2nd observer ICC 0.88 to 0.91). DSC showed high agreement comparing automatic artificial intelligence and manual CMR readers. Maximal RA area mean and standard deviation (SD) DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 is 92.4 +/- 3.5 cm(2), 91.2 +/- 4.5 cm(2) and 93.2 +/- 3.2 cm(2), respectively. Minimal RA area mean and SD DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 was 89.8 +/- 3.9 cm(2), 87.0 +/- 5.8 cm(2) and 91.8 +/- 4.8 cm(2). Automatic RA area measurements all showed moderate correlation with invasive parameters (r = 0.45 to 0.66), manual (r = 0.36 to 0.57). Maximal RA area could accurately predict elevated mean RA pressure low and high-risk thresholds (area under the receiver operating characteristic curve artificial intelligence = 0.82/0.87 vs manual = 0.78/0.83), and predicted mortality similar to manual measurements, both p < 0.01. In the QC evaluation, artificial intelligence segmentations were suboptimal at 108/3795 and a low failure rate of 16/3795. In a subcohort (n = 1018), agreement by two QC observers was excellent, kappa 0.84. Conclusion Automatic artificial intelligence CMR derived RA size and function are accurate, have excellent repeatability, moderate associations with invasive haemodynamics and predict mortality

Plasma NT-proBNP as predictor of change in functional status, cardiovascular morbidity and mortality in the oldest old: the Leiden 85-plus study

In the aging society, it is important to identify very old persons at high risk of functional decline, cardiovascular disease and mortality. However, traditional risk markers lose their predictive value with age. We investigated whether plasma N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels predict change in functional status, cardiovascular morbidity and mortality in very old age. Here we present an observational prospective cohort study (Leiden 85-plus Study, 1997–2004) in a population-based sample of 560 individuals aged 85 years with a 5-year complete follow-up for functional status, cardiovascular morbidity and cause-specific mortality. Median NT-proBNP for men was 351 pg/ml (cutoff values for low-medium tertiles 201 pg/ml and medium-high tertiles 649 pg/ml) and, for women, 297 pg/ml (cutoffs 204 and 519 pg/ml, respectively). During the 5-year follow-up, participants with high NT-proBNP had an accelerated cognitive decline and increase of activities of daily living (ADL) disability over time (all at p < 0.01) and an increased risk of incident heart failure [hazard ratio (HR) 3.3 (95 % confidence interval (CI) 1.8–6.1)], atrial fibrillation [HR 4.1 (2.0–8.7)], myocardial infarction [HR 2.1 (1.2–3.7)], stroke [HR 3.4 (1.9–6.3)], cardiovascular mortality [HR 5.5 (3.1–10)], non-cardiovascular mortality [HR 2.0 (1.4–3.0)] and all-cause mortality [HR 2.9 (2.1–4.0)], independent of other known risk markers. All results remained similar after exclusion of participants with heart failure at baseline. In very old age, high-NT-proBNP levels predict accelerated cognitive and functional decline, as well as cardiovascular morbidity and mortality. Results suggest that NT-proBNP can help clinicians to identify very old people at high risk of functional impairment and incident cardiovascular morbidity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11357-014-9660-1) contains supplementary material, which is available to authorized users