Longitudinal DLQI trajectory patterns and early risk updating in telepharmacy-monitored psoriasis
11 affiliations
- 1Pharmacy Department, Hospital General Mateu Orfila, IB-Salut, Mahón, Menorca, Balearic Islands, Spain
- 2Research Group on Evidence, Lifestyles and Health, IUNICS, University of the Balearic Islands, Palma, Balearic Islands, Spain
- 3Pharmacy Department, Hospital Comarcal D'Inca, Inca, Balearic Islands, Spain
- 4Doctoral Programme in Translational Research in Public Health and High Prevalence Diseases, University of the Balearic Islands (UIB), Palma, Balearic Islands, Spain
- 5Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, Cuenca, Spain
- 6Medical Analysis Expert Group, Castilla-La Mancha Institute of Health Research (IDISCAM), Toledo, Spain
- 7Pharmacy Department, Hospital Manacor, Manacor, Balearic Islands, Spain
- 8Departament de Genètica, Microbiologia I Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
- 9Fundamental Biology and Health Sciences, Universidad de las Islas Baleares, Health Research Institute of the Balearic Islands (IdISBa), Santiago de Compostela, Spain
- 100Faculty of Health Sciences, International University of Valencia, Valencia, Spain
- 111Faculty of Health Science, Technology and Management, Universidad Intercontinental de la Empresa (UIE), Santiago de Compostela, Spain
Abstract
BackgroundTelepharmacy platforms capture longitudinal patient-reported health-related quality-of-life (HRQoL) data in psoriasis, but reports have emphasized average improvement, which may conceal a minority with persistent burden. Whether machine learning adds value beyond baseline severity, and whether the first follow-up sharpens risk estimation, remain unclear.ObjectiveTo characterize 12-month Dermatology Life Quality Index (DLQI) trajectories in a telepharmacy psoriasis cohort, to identify a persistently impaired minority through trajectory-pattern analysis, and to determine where the added value of longitudinal monitoring lies.MethodsAfter curation of the NAVETA telepharmacy dataset, the analytic cohort consisted of 219 patients contributing a total of 831 observations. As the primary analysis, unsupervised Ward clustering of DLQI trajectories (a complete-case subset of 133 of the 219 patients with complete baseline-to-month-6 DLQI) identified exploratory trajectory patterns. A leakage-aware benchmark compared a null rule, a one-predictor baseline-DLQI logistic model, multivariable logistic regression and four ensemble learners for 6-month clinically meaningful improvement (≥4-point reduction) across 100 repeated stratified 80/20 splits, with calibration. For 6-month remission (DLQI ≤ 1), a baseline-DLQI-only comparator and a month-1 updating model were evaluated in the same dynamic cohort.ResultsMean DLQI improved from 7.78 to 2.89 over 12 months. Three exploratory trajectory patterns were identified—low-burden early control (n = 90), high-burden responder (n = 20) and persistent burden (n = 23; 17.3%); the retained three-cluster solution showed a silhouette of 0.43, and the last pattern remained impaired at 12 months (mean DLQI 7.31 vs. 1.91 and 1.80; p = 0.009). For improvement, a one-predictor baseline-DLQI logistic model matched the leading ensembles (AUROC 0.923 vs. XGBoost 0.903), and feature-attribution analysis confirmed the dominance of baseline DLQI. For DLQI remission, the baseline-DLQI-only model reached AUROC 0.641, whereas the month-1 DLQI updating model reached AUROC 0.753 with good calibration (intercept −0.02, slope 1.04, ECE 0.021).ConclusionsA favourable average conceals a clinically important minority with persistent HRQoL burden that trajectory-pattern analysis can identify. For outcomes anchored to baseline severity, machine learning adds little beyond a simple baseline-DLQI model; the added value of telepharmacy may instead emerge from longitudinal information, with the first follow-up providing an early, well-calibrated update to individual risk. External validation is required.
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BibTeX
@article{mercadalorfila2026longitudinal,
title = {{Longitudinal DLQI trajectory patterns and early risk updating in telepharmacy-monitored psoriasis}},
author = {Gabriel Mercadal-Orfila and Gabriel Mercadal-Orfila and Sergio Salvador-Pareja and Sergio Salvador-Pareja and Jorge Mateo Sotos and Jorge Mateo Sotos and Ana M. Torres Aranda and Ana M. Torres Aranda and Manuel Pinteño-Blanco and María Antonia Maestre-Fullana and Santiago Ríos and Pedro Tauler Riera and Salvador Herrera-Pérez and Salvador Herrera-Pérez},
journal = {Frontiers in Digital Health},
year = {2026},
month = oct,
doi = {10.3389/fdgth.2026.1961097},
url = {https://doi.org/10.3389/fdgth.2026.1961097},
note = {CC BY},
}
RIS
TY - JOUR
TI - Longitudinal DLQI trajectory patterns and early risk updating in telepharmacy-monitored psoriasis
AU - Gabriel Mercadal-Orfila
AU - Gabriel Mercadal-Orfila
AU - Sergio Salvador-Pareja
AU - Sergio Salvador-Pareja
AU - Jorge Mateo Sotos
AU - Jorge Mateo Sotos
AU - Ana M. Torres Aranda
AU - Ana M. Torres Aranda
AU - Manuel Pinteño-Blanco
AU - María Antonia Maestre-Fullana
AU - Santiago Ríos
AU - Pedro Tauler Riera
AU - Salvador Herrera-Pérez
AU - Salvador Herrera-Pérez
PY - 2026
DA - 2026/10/26
JO - Frontiers in Digital Health
DO - 10.3389/fdgth.2026.1961097
UR - https://doi.org/10.3389/fdgth.2026.1961097
AB - BackgroundTelepharmacy platforms capture longitudinal patient-reported health-related quality-of-life (HRQoL) data in psoriasis, but reports have emphasized average improvement, which may conceal a minority with persistent burden. Whether machine learning adds value beyond baseline severity, and whether the first follow-up sharpens risk estimation, remain unclear.ObjectiveTo characterize 12-month Dermatology Life Quality Index (DLQI) trajectories in a telepharmacy psoriasis cohort, to identify a persistently impaired minority through trajectory-pattern analysis, and to determine where the added value of longitudinal monitoring lies.MethodsAfter curation of the NAVETA telepharmacy dataset, the analytic cohort consisted of 219 patients contributing a total of 831 observations. As the primary analysis, unsupervised Ward clustering of DLQI trajectories (a complete-case subset of 133 of the 219 patients with complete baseline-to-month-6 DLQI) identified exploratory trajectory patterns. A leakage-aware benchmark compared a null rule, a one-predictor baseline-DLQI logistic model, multivariable logistic regression and four ensemble learners for 6-month clinically meaningful improvement (≥4-point reduction) across 100 repeated stratified 80/20 splits, with calibration. For 6-month remission (DLQI ≤ 1), a baseline-DLQI-only comparator and a month-1 updating model were evaluated in the same dynamic cohort.ResultsMean DLQI improved from 7.78 to 2.89 over 12 months. Three exploratory trajectory patterns were identified—low-burden early control (n = 90), high-burden responder (n = 20) and persistent burden (n = 23; 17.3%); the retained three-cluster solution showed a silhouette of 0.43, and the last pattern remained impaired at 12 months (mean DLQI 7.31 vs. 1.91 and 1.80; p = 0.009). For improvement, a one-predictor baseline-DLQI logistic model matched the leading ensembles (AUROC 0.923 vs. XGBoost 0.903), and feature-attribution analysis confirmed the dominance of baseline DLQI. For DLQI remission, the baseline-DLQI-only model reached AUROC 0.641, whereas the month-1 DLQI updating model reached AUROC 0.753 with good calibration (intercept −0.02, slope 1.04, ECE 0.021).ConclusionsA favourable average conceals a clinically important minority with persistent HRQoL burden that trajectory-pattern analysis can identify. For outcomes anchored to baseline severity, machine learning adds little beyond a simple baseline-DLQI model; the added value of telepharmacy may instead emerge from longitudinal information, with the first follow-up providing an early, well-calibrated update to individual risk. External validation is required.
SN - 2673-253X
ER -