{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Pharmacy Department, Hospital General Mateu Orfila, IB-Salut, Mahón, Menorca, Balearic Islands, Spain",
    "Research Group on Evidence, Lifestyles and Health, IUNICS, University of the Balearic Islands, Palma, Balearic Islands, Spain",
    "Pharmacy Department, Hospital Comarcal D'Inca, Inca, Balearic Islands, Spain",
    "Doctoral Programme in Translational Research in Public Health and High Prevalence Diseases, University of the Balearic Islands (UIB), Palma, Balearic Islands, Spain",
    "Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, Cuenca, Spain",
    "Medical Analysis Expert Group, Castilla-La Mancha Institute of Health Research (IDISCAM), Toledo, Spain",
    "Pharmacy Department, Hospital Manacor, Manacor, Balearic Islands, Spain",
    "Departament de Genètica, Microbiologia I Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain",
    "Fundamental Biology and Health Sciences, Universidad de las Islas Baleares, Health Research Institute of the Balearic Islands (IdISBa), Santiago de Compostela, Spain",
    "0Faculty of Health Sciences, International University of Valencia, Valencia, Spain",
    "1Faculty of Health Science, Technology and Management, Universidad Intercontinental de la Empresa (UIE), Santiago de Compostela, Spain"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Gabriel Mercadal-Orfila"
    },
    {
      "affs": [
        1
      ],
      "name": "Gabriel Mercadal-Orfila"
    },
    {
      "affs": [
        2
      ],
      "name": "Sergio Salvador-Pareja"
    },
    {
      "affs": [
        3
      ],
      "name": "Sergio Salvador-Pareja"
    },
    {
      "affs": [
        4
      ],
      "name": "Jorge Mateo Sotos"
    },
    {
      "affs": [
        5
      ],
      "name": "Jorge Mateo Sotos"
    },
    {
      "affs": [
        4
      ],
      "name": "Ana M. Torres Aranda"
    },
    {
      "affs": [
        5
      ],
      "name": "Ana M. Torres Aranda"
    },
    {
      "affs": [
        2
      ],
      "name": "Manuel Pinteño-Blanco"
    },
    {
      "affs": [
        6
      ],
      "name": "María Antonia Maestre-Fullana"
    },
    {
      "affs": [
        7
      ],
      "name": "Santiago Ríos"
    },
    {
      "affs": [
        8
      ],
      "name": "Pedro Tauler Riera"
    },
    {
      "affs": [
        9
      ],
      "name": "Salvador Herrera-Pérez"
    },
    {
      "affs": [
        10
      ],
      "name": "Salvador Herrera-Pérez"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/aat3mtdtyqptn.bib",
  "cited_by": 0,
  "doi": "10.3389/fdgth.2026.1961097",
  "fulltext_url": "https://www.frontiersin.org/articles/10.3389/fdgth.2026.1961097/full",
  "id": "aat3mtdtyqptn",
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    "2673-253X"
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  "landing_url": "https://doi.org/10.3389/fdgth.2026.1961097",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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  "pdf_url": "https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1961097/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "Frontiers Media S.A.",
  "ris": "https://research.draconitic.com/paper/aat3mtdtyqptn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:47:27Z",
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      "origin": "openalex:2403|Psoriasis: Treatment and Pathogenesis",
      "subfield": "immunology"
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  "title": "Longitudinal DLQI trajectory patterns and early risk updating in telepharmacy-monitored psoriasis",
  "title_text": "Longitudinal DLQI trajectory patterns and early risk updating in telepharmacy-monitored psoriasis",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:47:27Z",
  "url": "https://research.draconitic.com/paper/aat3mtdtyqptn",
  "venue": "Frontiers in Digital Health",
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}