Applying systems thinking to unravel the mechanisms underlying orthostatic hypotension related fall risk

Abstract

Orthostatic hypotension (OH) is an established and common cardiovascular risk factor for falls. An in-depth understanding of the various interacting pathophysiological pathways contributing to OH-related falls is essential to guide improvements in diagnostic and treatment opportunities. We applied systems thinking to multidisciplinary map out causal mechanisms and risk factors. For this, we used group model building (GMB) to develop a causal loop diagram (CLD). The GMB was based on the input of experts from multiple domains related to OH and falls and all proposed mechanisms were supported by scientific literature. Our CLD is a conceptual representation of factors involved in OH-related falls, and their interrelatedness. Network analysis and feedback loops were applied to analyze and interpret the CLD, and quantitatively summarize the function and relative importance of the variables. Our CLD contains 50 variables distributed over three intrinsic domains (cerebral, cardiovascular, and musculoskeletal), and an extrinsic domain (e.g., medications). Between the variables, 181 connections and 65 feedback loops were identified. Decreased cerebral blood flow, low blood pressure, impaired baroreflex activity, and physical inactivity were identified as key factors involved in OH-related falls, based on their high centralities. Our CLD reflects the multifactorial pathophysiology of OH-related falls. It enables us to identify key elements, suggesting their potential for new diagnostic and treatment approaches in fall prevention. The interactive online CLD renders it suitable for both research and educational purposes and this CLD is the first step in the development of a computational model for simulating the effects of risk factors on falls.

Using network analysis to identify leverage points based on causal loop diagrams leads to false inference

“…The problems with the use of betweenness- and closeness centrality to identify “structurally important nodes” 66 are far from unfamiliar, where many of the problems previously detected in other fields 61 also apply to CLDs and several papers applying network analysis metrics to CLDs also hint at potential limitations 8 , 23 , 24 , 26 , 28 , 36 – 38 , 42 , 52 . It has already been shown for different types of networks that betweenness- and closeness centrality do not provide reliable results over networks that differ slightly in structure, for example when a node is included or excluded 61 , 69 , 70 .…”

“…In seeking alternative methods, network analysis 35 has gained momentum as an accepted practice to quantitatively identify leverage points based on CLDs 8 , 23 – 30 , 36 – 55 —for example in public health 24 , 38 , 45 , 50 , 52 , 53 , 55 . Here, 22 of the 29 papers that we identified that adopt this practice were published in 2021 (4 papers 23 , 25 , 37 , 50 ), 2022 (8 papers 27 , 28 , 43 – 45 , 47 , 49 , 52 ), and 2023 (10 papers to date 26 , 30 , 36 , 38 , 39 , 41 , 42 , 46 , 53 , 55 ). Network analysis seemingly has the potential to mathematically analyse a CLD, which in this context can be considered a network of factors, to systematically find which factors represent leverage points.…”

“…The factor with the highest betweenness centrality lies on the highest number of shortest causal chains between pairs of other factors in the CLD 58 , hypothesised to indicate that it is an important mediator 8 , 24 , 26 , 29 , 38 , 42 . The factor with the highest closeness centrality on average has the shortest causal chains to other factors 58 , reasoned to signal its importance as a spreader of causal power 8 , 26 , 29 , 42 . In the context of CLDs, the rationale behind using these metrics, which rely on a factor’s position on short causal chains, is that one could assume that causal power gradually diminishes while going down a causal chain 8 , making interventions on the factors that are involved in short causal chains the most likely to shift system-level behaviour.…”

“…Currently in the literature relating to the development and analysis of CLDs, the term 'leverage point' tends to be used as referring to a factor within the CLD "in which change is likely to yield significant shifts elsewhere in the system" 23 . The key premise is that the system depicted in the CLD is producing undesirable system-level behaviour, and that there are central drivers in the system that could be influenced in such a way that this undesirable system-level behaviour is sustainably disrupted 8,[24][25][26][27][28][29][30] .…”

“…In seeking alternative methods, network analysis 35 has gained momentum as an accepted practice to quantitatively identify leverage points based on CLDs 8,[23][24][25][26][27][28][29][30][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55] -for example in public health 24,38,45,50,52,53,55 . Here, 22 of the 29 papers that we identified that adopt this practice were published in 2021 (4 papers 23,25,37,50 ), 2022 (8 papers 27,28,[43][44][45]47,49,52 ), and 2023 (10 papers to date …”