Interrupted time series analysis on first cardiovascular disease hospitalization for adherence to lipid‐lowering therapy

Hu, Feiyu; Warren, Jim; Exeter, Daniel

doi:10.1002/pds.4916

Cited by 7 publications

(5 citation statements)

References 57 publications

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“…Similar to the above mentioned study, other recent studies in medication adherence have also underscored the temporal patterns present in patient adherence behaviour and the relationships between clinical events in patients history to adherence [ 60 , 61 ]. To the best of our knowledge, the prediction of long-term medication adherence using temporal deep learning models on a large routinely collected population data set has not been investigated.…”

Section: Introductionsupporting

confidence: 69%

Medication adherence prediction through temporal modelling in cardiovascular disease management

Hsu

Warren

Riddle

2022

BMC Med Inform Decis Mak

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Background Chronic conditions place a considerable burden on modern healthcare systems. Within New Zealand and worldwide cardiovascular disease (CVD) affects a significant proportion of the population and it is the leading cause of death. Like other chronic diseases, the course of cardiovascular disease is usually prolonged and its management necessarily long-term. Despite being highly effective in reducing CVD risk, non-adherence to long-term medication continues to be a longstanding challenge in healthcare delivery. The study investigates the benefits of integrating patient history and assesses the contribution of explicitly temporal models to medication adherence prediction in the context of lipid-lowering therapy. Methods Data from a CVD risk assessment tool is linked to routinely collected national and regional data sets including pharmaceutical dispensing, hospitalisation, lab test results and deaths. The study extracts a sub-cohort from 564,180 patients who had primary CVD risk assessment for analysis. Based on community pharmaceutical dispensing record, proportion of days covered (PDC) $$\ge$$ ≥ 80 is used as the threshold for adherence. Two years (8 quarters) of patient history before their CVD risk assessment is used as the observation window to predict patient adherence in the subsequent 5 years (20 quarters). The predictive performance of temporal deep learning models long short-term memory (LSTM) and simple recurrent neural networks (Simple RNN) are compared against non-temporal models multilayer perceptron (MLP), ridge classifier (RC) and logistic regression (LR). Further, the study investigates the effect of lengthening the observation window on the task of adherence prediction. Results Temporal models that use sequential data outperform non-temporal models, with LSTM producing the best predictive performance achieving a ROC AUC of 0.805. A performance gap is observed between models that can discover non-linear interactions between predictor variables and their linear counter parts, with neural network (NN) based models significantly outperforming linear models. Additionally, the predictive advantage of temporal models become more pronounced when the length of the observation window is increased. Conclusion The findings of the study provide evidence that using deep temporal models to integrate patient history in adherence prediction is advantageous. In particular, the RNN architecture LSTM significantly outperforms all other model comparators.

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Section: Introductionsupporting

confidence: 69%

Medication adherence prediction through temporal modelling in cardiovascular disease management

Hsu

Warren

Riddle

2022

BMC Med Inform Decis Mak

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“…Time-series analysis was then integrated with TI analysis to uncover the regulatory factors implied in dynamic biological processes in a quantitative way. Time-series analysis has been successfully applied in studies focusing on human disease and drug development [18] , [19] . Therefore, time-series analysis clearly takes full advantage of the time-series information involved in dynamic processes.…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, time-series analysis [17] that can take into account the information generated at several time points at the same time, determine expression patterns (such as cyclical pattern), and identify regulatory factors in a dynamic biological process in a quantitative and knowledge-free way, will be a perfect choice for time series scRNA-seq data obtained from multiple snapshots. At present, time-series analysis is being successfully applied in studies focusing on human disease and drug development [18] , [19] . Although not applied to scRNA-seq data, the advantage of time-series analysis in identifying DEGs in dynamic biological processes based on high-throughput RNA-seq data has also been revealed [20] , [21] .…”

Section: Introductionmentioning

confidence: 99%

Identify differential genes and cell subclusters from time-series scRNA-seq data using scTITANS

Xue

et al. 2021

Computational and Structural Biotechnology Journal

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“…Unlike the single time point (e.g., snapshot) profiling of transcriptome that allocates cells on pseudotime or lineages using purely computational strategies [ 4 – 6 ], in particular, the time-course scRNA-seq profiling of whole transcriptome with respect to real, physical time, is capable of providing additional insights into dynamic biological processes [ 2 , 7 ]. For example, how the cells naturally differentiate into other types or states during the development processes and how the cellular response to specific drug treatments [ 8 ], viral infections [ 9 ], etc. Therefore, accurately characterizing the temporal dynamics of gene expression over time points is crucial for developmental biology [ 10 , 11 ], tumor biology [ 12 – 14 ], and biogerontology [ 15 – 17 ], which allows us to decipher the dynamic cellular heterogeneity during cell differentiation [ 18 ], identifying cancer driver genes during the status transformation [ 14 ], and investigating the mechanisms of cell senescence during aging [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq

Fan,

Li,

Sun

2024

Genome Biol

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We present a non-parametric statistical method called TDEseq that takes full advantage of smoothing splines basis functions to account for the dependence of multiple time points in scRNA-seq studies, and uses hierarchical structure linear additive mixed models to model the correlated cells within an individual. As a result, TDEseq demonstrates powerful performance in identifying four potential temporal expression patterns within a specific cell type. Extensive simulation studies and the analysis of four published scRNA-seq datasets show that TDEseq can produce well-calibrated p-values and up to 20% power gain over the existing methods for detecting temporal gene expression patterns.

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Interrupted time series analysis on first cardiovascular disease hospitalization for adherence to lipid‐lowering therapy

Cited by 7 publications

References 57 publications

Medication adherence prediction through temporal modelling in cardiovascular disease management

Medication adherence prediction through temporal modelling in cardiovascular disease management

Identify differential genes and cell subclusters from time-series scRNA-seq data using scTITANS

Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq

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