Evidence map›Paper›PMID 34852769›Full record

ArticleBMC medical research methodology2021

A latent-class heteroskedastic hurdle trajectory model: patterns of adherence in obstructive sleep apnea patients on CPAP therapy.

Niek G P Den Teuling, Edwin R van den Heuvel, Mark S Aloia, Steffen C Pauws

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Article in BMC medical research methodology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

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2 citing papers in PubMed.

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5 · Who and what money

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4 authors.

Niek G P Den TeulingDep. of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands. n.g.p.den.teuling@tue.nl.ORCID 0000-0003-1026-5080
Edwin R van den HeuvelDep. of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands.ORCID 0000-0001-9157-7224
Mark S AloiaDepartment of Medicine, National Jewish Health, Denver, CO, USA.
Steffen C PauwsPhilips Research, Eindhoven, The Netherlands.ORCID 0000-0003-2257-9239

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSleep apnea patients on CPAP therapy exhibit differences in how they adhere to the therapy. Previous studies have demonstrated the benefit of describing adherence in terms of discernible longitudinal patterns. However, these analyses have been done on a limited number of patients, and did not properly represent the temporal characteristics and heterogeneity of adherence.

methodsWe illustrate the potential of identifying patterns of adherence with a latent-class heteroskedastic hurdle trajectory approach using generalized additive modeling. The model represents the adherence trajectories on three aspects over time: the daily hurdle of using the therapy, the daily time spent on therapy, and the day-to-day variability. The combination of these three characteristics has not been studied before.

resultsApplying the proposed model to a dataset of 10,000 patients in their first three months of therapy resulted in nine adherence groups, among which 49% of patients exhibited a change in adherence over time. The identified group trajectories revealed a non-linear association between the change in the daily hurdle of using the therapy, and the average time on therapy. The largest difference between groups was observed in the patient motivation score. The adherence patterns were also associated with different levels of high residual AHI, and day-to-day variability in leakage.

conclusionThe inclusion of the hurdle model and the heteroskedastic model into the mixture model enabled the discovery of additional adherence patterns, and a more descriptive representation of patient behavior over time. Therapy adherence was mostly affected by a lack of attempts over time, suggesting that encouraging these patients to attempt therapy on a daily basis, irrespective of the number of hours used, could drive adherence. We believe the methodology is applicable to other domains of therapy or medication adherence.

Indexed as

Continuous Positive Airway PressureSleep Apnea, ObstructiveHumansPatient ComplianceCPAP therapyHeteroskedastic modelingHurdle modelingIntensive longitudinal dataLatent-class trajectory modelingMultilevel mixture modelingObstructive sleep apneaTreatment adherence

Identifiers

PMID34852769
PMCPMC8638441

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