Causal Models for the Result of Percutaneous Coronary Intervention in Coronary Chronic Total Occlusions

Ganopoulou, Maria; Kangelidis, Ioannis; Sianos, Georgios; Angelis, Lefteris

doi:10.3390/app11199258

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Causal discovery goes beyond correlation analysis, aiming to infer causal relationships between variables [38]. It is a tool with wide potential that may aid to validate and/or reveal new knowledge in diverse fields (see, e.g., [39][40][41][42][43][44][45][46]). Common methods in causal discovery include causal Bayesian networks (BNs), which are graphical models representing and describing the causal relations between random variables through a directed acyclic graph (DAG), and structural equation modeling (SEM), which investigates the relationships between constructs relative to a certain phenomenon [47].…”

Section: Introductionmentioning

confidence: 99%

Delving into Causal Discovery in Health-Related Quality of Life Questionnaires

Ganopoulou,

Kontopoulos,

Fokianos

et al. 2024

Algorithms

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Questionnaires on health-related quality of life (HRQoL) play a crucial role in managing patients by revealing insights into physical, psychological, lifestyle, and social factors affecting well-being. A methodological aspect that has not been adequately explored yet, and is of considerable potential, is causal discovery. This study explored causal discovery techniques within HRQoL, assessed various considerations for reliable estimation, and proposed means for interpreting outcomes. Five causal structure learning algorithms were employed to examine different aspects in structure estimation based on simulated data derived from HRQoL-related directed acyclic graphs. The performance of the algorithms was assessed based on various measures related to the differences between the true and estimated structures. Moreover, the Resource Description Framework was adopted to represent the responses to the HRQoL questionnaires and the detected cause–effect relationships among the questions, resulting in semantic knowledge graphs which are structured representations of interconnected information. It was found that the structure estimation was impacted negatively by the structure’s complexity and favorably by increasing the sample size. The performance of the algorithms over increasing sample size exhibited a similar pattern, with distinct differences being observed for small samples. This study illustrates the dynamics of causal discovery in HRQoL-related research, highlights aspects that should be addressed in estimation, and fosters the shareability and interoperability of the output based on globally established standards. Thus, it provides critical insights in this context, further promoting the critical role of HRQoL questionnaires in advancing patient-centered care and management.

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Section: Introductionmentioning

confidence: 99%

Delving into Causal Discovery in Health-Related Quality of Life Questionnaires

Ganopoulou,

Kontopoulos,

Fokianos

et al. 2024

Algorithms

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“…A causal graph-based modeling approach employed the concept of the Markov Blanket in [13], and found it to perform better compared to other models regarding lung cancer prediction. Ganopoulou, et al, assessed the effectiveness of the Markov Blanket as a feature selection tool to detect predictors that are causally related to the result of the percutaneous coronary interventions (PCI) in chronic total occlusions [14]. A customized predictive model of the PCI result, which included these predictors, was compared to other modeling approaches from the literature, and was found to perform equally well or better.…”

Section: Introductionmentioning

confidence: 99%

Single Nucleotide Polymorphisms’ Causal Structure Robustness within Coronary Artery Disease Patients

Ganopoulou

Moysiadis

Gounaris

et al. 2023

Biology

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An ever-growing amount of accumulated data has materialized in several scientific fields, due to recent technological progress. New challenges emerge in exploiting these data and utilizing the valuable available information. Causal models are a powerful tool that can be employed towards this aim, by unveiling the structure of causal relationships between different variables. The causal structure may avail experts to better understand relationships, or even uncover new knowledge. Based on 963 patients with coronary artery disease, the robustness of the causal structure of single nucleotide polymorphisms was assessed, taking into account the value of the Syntax Score, an index that evaluates the complexity of the disease. The causal structure was investigated, both locally and globally, under different levels of intervention, reflected in the number of patients that were randomly excluded from the original datasets corresponding to two categories of the Syntax Score, zero and positive. It is shown that the causal structure of single nucleotide polymorphisms was more robust under milder interventions, whereas in the case of stronger interventions, the impact increased. The local causal structure around the Syntax Score was studied in the case of a positive Syntax Score, and it was found to be resilient, even when the intervention was strong. Consequently, employing causal models in this context may increase the understanding of the biological aspects of coronary artery disease.

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Assessing the transportability of clinical prediction models for cognitive impairment using causal models

Fehr,

Piccininni,

Kurth

et al. 2023

BMC Med Res Methodol

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Background Machine learning models promise to support diagnostic predictions, but may not perform well in new settings. Selecting the best model for a new setting without available data is challenging. We aimed to investigate the transportability by calibration and discrimination of prediction models for cognitive impairment in simulated external settings with different distributions of demographic and clinical characteristics. Methods We mapped and quantified relationships between variables associated with cognitive impairment using causal graphs, structural equation models, and data from the ADNI study. These estimates were then used to generate datasets and evaluate prediction models with different sets of predictors. We measured transportability to external settings under guided interventions on age, APOE ε4, and tau-protein, using performance differences between internal and external settings measured by calibration metrics and area under the receiver operating curve (AUC). Results Calibration differences indicated that models predicting with causes of the outcome were more transportable than those predicting with consequences. AUC differences indicated inconsistent trends of transportability between the different external settings. Models predicting with consequences tended to show higher AUC in the external settings compared to internal settings, while models predicting with parents or all variables showed similar AUC. Conclusions We demonstrated with a practical prediction task example that predicting with causes of the outcome results in better transportability compared to anti-causal predictions when considering calibration differences. We conclude that calibration performance is crucial when assessing model transportability to external settings.

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Causal Models for the Result of Percutaneous Coronary Intervention in Coronary Chronic Total Occlusions

Cited by 3 publications

References 14 publications

Delving into Causal Discovery in Health-Related Quality of Life Questionnaires

Delving into Causal Discovery in Health-Related Quality of Life Questionnaires

Single Nucleotide Polymorphisms’ Causal Structure Robustness within Coronary Artery Disease Patients

Assessing the transportability of clinical prediction models for cognitive impairment using causal models

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