BackgroundMathematical modelling has been a vital research tool for exploring complex systems, most recently to aid understanding of health system functioning and optimisation. System dynamics models (SDM) and agent-based models (ABM) are two popular complementary methods, used to simulate macro- and micro-level health system behaviour. This systematic review aims to collate, compare and summarise the application of both methods in this field and to identify common healthcare settings and problems that have been modelled using SDM and ABM.MethodsWe searched MEDLINE, EMBASE, Cochrane Library, MathSciNet, ACM Digital Library, HMIC, Econlit and Global Health databases to identify literature for this review. We described papers meeting the inclusion criteria using descriptive statistics and narrative synthesis, and made comparisons between the identified SDM and ABM literature.ResultsWe identified 28 papers using SDM methods and 11 papers using ABM methods, one of which used hybrid SDM-ABM to simulate health system behaviour. The majority of SDM, ABM and hybrid modelling papers simulated health systems based in high income countries. Emergency and acute care, and elderly care and long-term care services were the most frequently simulated health system settings, modelling the impact of health policies and interventions such as those targeting stretched and under resourced healthcare services, patient length of stay in healthcare facilities and undesirable patient outcomes.ConclusionsFuture work should now turn to modelling health systems in low- and middle-income countries to aid our understanding of health system functioning in these settings and allow stakeholders and researchers to assess the impact of policies or interventions before implementation. Hybrid modelling of health systems is still relatively novel but with increasing software developments and a growing demand to account for both complex system feedback and heterogeneous behaviour exhibited by those who access or deliver healthcare, we expect a boost in their use to model health systems.

Cardiovascular ageing is a process that begins early in life and leads to a progressive change in structure and decline in function due to accumulated damage across diverse cell types, tissues and organs contributing to multi-morbidity. Damaging biophysical, metabolic and immunological factors exceed endogenous repair mechanisms resulting in a pro-fibrotic state, cellular senescence and end-organ damage, however the genetic architecture of cardiovascular ageing is not known. Here we used machine learning approaches to quantify cardiovascular ageing from image-derived traits of vascular function, cardiac motion and myocardial fibrosis, as well as conduction traits from electrocardiograms, in 39,559 participants of UK Biobank. Cardiovascular ageing was found to be significantly associated with common or rare variants in genes regulating sarcomere homeostasis, myocardial immunomodulation, and tissue responses to biophysical stress. Ageing is accelerated by cardiometabolic risk factors and we also identified prescribed medications that were potential modifiers of ageing. Through large-scale modelling of ageing across multiple traits our results reveal insights into the mechanisms driving premature cardiovascular ageing and reveal potential molecular targets to attenuate age-related processes.

Background Hypertrophic cardiomyopathy (HCM) is an important cause of sudden cardiac death associated with heterogeneous structural phenotypes but there is no systematic framework for classifying morphology or assessing associated risks. In this study we quantitatively survey genotype-phenotype associations in HCM to derive a data-driven taxonomy of disease expression for automated patient stratification. Methods An observational, single-centre study enrolled 436 HCM patients (median age 60 years; 28.8% women) with clinical, genetic and imaging data. An independent cohort of 60 HCM patients from Singapore (median age 59 years; 11% women) and a normative reference population from UK Biobank (n = 16,691, mean age 55 years; 52.5% women) with equivalent data were also recruited. We used machine learning to analyse the three dimensional structure of the left ventricle from cardiac magnetic resonance imaging and build a tree-based classification of HCM phenotypes. Genotype and mortality risk distributions were projected on the tree. Results The prevalence of pathogenic or likely pathogenic variants for HCM (P/LP) was 24.6%, while 66% were genotype negative. Carriers of P/LP variants had lower left ventricular mass, but greater basal septal hypertrophy, with reduced lifespan (mean follow-up 9.9 years) compared to genotype negative individuals (hazard ratio: 2.66; 95% confidence interval [CI]: 1.42-4.96; P < 0.002). Four main phenotypic branches were identified using unsupervised learning of three dimensional shape: 1) non-sarcomeric hypertrophy with co-existing hypertension; 2) diffuse and basal asymmetric hypertrophy associated with outflow tract obstruction; 3) isolated basal hypertrophy; 4) milder non-obstructive hypertrophy enriched for familial sarcomeric HCM (odds ratio for P/LP variants: 2.18 [95% CI: 1.93-2.28, P = 0.0001]). Phenotypic variation and associated risks could be visualised as a continuous distribution across the taxonomic tree. The model was generalisable to an independent cohort (trustworthiness M1: 0.86-0.88). Conclusions We report a data-driven taxonomy of HCM for identifying groups of patients with similar morphology while preserving a continuum of disease severity, genetic risk and outcomes. This approach will be of value for developing personalized clinical profiles to guide diagnosis, surveillance and intervention in patients with HCM, and improve understanding of the drivers of heterogeneity.

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): British Heart Foundation (RG/19/6/34387, RE/18/4/34215). Background Population ageing is a global trend and places an increased burden on healthcare resources, predominantly through cardiovascular morbidity and mortality. Ageing can be detected in the cardiovascular system as a decline in structure and function through cardiac magnetic resonance (CMR) imaging. This decline occurs at both organ and cellular levels, modulated by genetic, oxidative, inflammatory and metabolic stresses. Effect sizes and mechanisms of cardiovascular ageing are currently unknown; moreover, a robust biomarker is lacking. Purpose To develop a cardiovascular ageing model using CMR features to quantify an individual’s deviation ("age-delta") from healthy ageing and explore potential mechanisms contributing to the process. Methods We used data from the UK Biobank (UKB), a population-based cohort study of over 500,000 participants aged 40–69 recruited between 2006–2010. We studied 39,559 participants that had a CMR scan and used a machine learning model to estimate a "cardiovascular age" in 5065 healthy individuals from imaging traits including cardiac volumes, diastolic function, aortic distensibility and T1 mapping of fibrosis (Figure 1a). We applied this trained healthy ageing model to predict cardiovascular age in 34,147 new individuals (Figure 1b) and computed their age-deltas, which we used in a linear regression model to calculate the effect-sizes of selected diseases and lifestyle factors. We next performed a genome wide association study on 29,506 subjects to identify significant single nucleotide polymorphism (SNP) associations and computed a polygenic risk score in 373,948 independent genotyped participants of UKB to further explore phenotypic associations in a phenome wide association study. Results Hypertension (+1.58 years), diabetes (+0.74 years), smoking (+0.031 years/pack year) and alcohol (+0.015 years/gram per day) were significantly associated with adverse cardiovascular ageing (Figure 2a). Heart rate, blood pressure, inflammatory biomarkers and alkaline phosphatase were also significantly correlated with accelerated cardiovascular ageing, whereas telomere length, greater lung function, fat-free mass and basal metabolic rate were significantly correlated with attenuated ageing. GWAS revealed variants that contribute to myocardial contractility (Titin), arterial function (Elastin), inflammatory states (PLCE1, TREM2) and calcium signalling (MICU3) (Figure 2b). Conclusion Our study introduces a general population-derived CMR-based biomarker for cardiovascular ageing, which may in future be used to summarise an individual’s trajectory of ageing. The associations established with risk factors and physical measures could enable personalised cardioprotective interventions, complemented by novel therapies targeting mechanistic pathways discovered by our genetic associations.