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Construction of a Psoriasis and Psoriatic Arthritis Diagnostic Model Based on Multidimensional Nail Information

Construction of a Psoriasis and Psoriatic Arthritis Diagnostic Model Based on Multidimensional Nail Information

Status
Not yet recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT06790940
Enrollment
310
Registered
2025-01-24
Start date
2025-01-20
Completion date
2026-12-01
Last updated
2025-01-24

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Palmoplantar Pustulosis (PPP), Plaque Psoriasis, Psoriasis (PsO), Psoriatic Arthritis

Brief summary

Psoriasis is a globally prevalent chronic relapsing skin disease, characterized by its long duration and tendency to relapse. In addition to skin symptoms, it can also affect nails and joints, leading to pathological features such as pitting, leukonychia, red lunula, or severe nail dystrophy. Some patients with psoriasis may develop psoriatic arthritis. Psoriatic arthritis (PsA) is a chronic relapsing musculoskeletal disease, characterized by psoriatic skin lesions accompanied by axial and peripheral joint damage, and often associated with characteristic manifestations of psoriatic nails. These nail changes typically indicate more severe disease and poorer prognosis. However, current diagnostic methods largely depend on the experience and professional knowledge of clinicians, which are subjective and uncertain. Moreover, histopathological examination is invasive and can cause additional pain and inconvenience to patients. To develop an effective, convenient, and non-invasive early diagnostic tool for psoriasis, our research team has conducted in-depth studies in the field of psoriasis-related diagnosis and predictive models. We have successfully developed a predictive model for psoriatic arthritis, including six key predictive factors: history of joint swelling, history of arthritis, history of swelling and pain in fingers or toes, nail involvement, genital involvement, and a history of long-term local use of corticosteroids. Clinicians can effectively assess the risk of psoriatic arthritis by obtaining information about these six factors from patients. The paper Early detection of psoriatic arthritis in patients with psoriasis: construction of a multifactorial prediction model was published in Front. Immunol (DOI: 10.3389/fimmu.2024.1426127). Raman spectroscopy is a rapid, non-invasive molecular vibration detection method that has shown great potential in medical diagnostics. Studies have shown that Raman spectroscopy can distinguish normal and abnormal tissues at the molecular level and has been proven feasible in nail testing. For psoriasis, a disease that causes significant nail changes, Raman spectroscopy offers unique advantages. Based on this background, our project will conduct a prospective observational study on psoriasis and psoriatic arthritis using multidimensional nail data. We will integrate Raman spectroscopy data of nails and multidimensional clinical information and apply artificial intelligence algorithms to develop a new diagnostic tool for psoriasis and psoriatic arthritis. This tool aims to improve the accuracy and efficiency of diagnosis, providing strong support for the early detection and precise treatment of psoriasis and psoriatic arthritis.

Interventions

OTHERNo intervention

This is an observational study, no intervention will be implemented.

Sponsors

Shanghai 10th People's Hospital
CollaboratorOTHER
Shanghai Yueyang Integrated Medicine Hospital
Lead SponsorOTHER

Study design

Observational model
OTHER
Time perspective
CROSS_SECTIONAL

Eligibility

Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
Yes

Inclusion criteria

\- Inclusion criteria for patients with psoriasis: 1. Previous or first-time diagnosis o psoriasis; if previously diagnosed, no restrictions on prior treatments; 2. Age ≥ 18 and ≤ 80 years, with no gender restrictions; 3. Consent to participate in this study and sign an informed consent form. Inclusion criteria for non-psoriasis control subjects: (1) Patients who visit for other non-psoriasis skin conditions such as eczema or acne, and are confirmed by dermatologists not to have psoriasis; (3) Age ≥ 18 and ≤ 80 years, with no gender restrictions; (4) Consent to participate in this study and sign an informed consent form. \-

Exclusion criteria

for patients with psoriasis: 1. Those with unsuitable nail conditions for collection: patients with amputated fingers due to trauma or other reasons, and patients with any nail that is severely broken and cannot be effectively collected. 2. Patients with severe mental illness or cognitive impairment, lacking personal decision-making capacity, and unsuitable for participation in clinical research. 3. Patients with severe systemic diseases. 4. Patients with a history of malignant tumors, as well as those with primary or secondary immunodeficiency and hypersensitivity. 5. Patients whom the researchers deem unsuitable for participation in this study for other reasons.

Design outcomes

Primary

MeasureTime frame
Raman data1 day (The collected nail samples will be placed on aluminum-coated slides for Raman spectroscopic analysis, and the Raman peak data will be exported to form a txt file.)

Secondary

MeasureTime frame
Psoriasis Area and Severity Index (PASI)The researcher conducted a Psoriasis Area and Severity Index (PASI) assessment on the psoriasis subjects on the 1 day of sample collection.
Body Surface Area (BSA)The researcher conducted a Body Surface Area (BSA) assessment on the psoriasis subjects on the 1 day of sample collection.
Modified Nail Psoriasis Severity Index (mNAPSI)The researcher conducted a Modified Nail Psoriasis Severity Index (mNAPSI) assessment on the psoriasis subjects on the 1 day of sample collection.
Classification of Psoriatic Arthritis (CASPAR)The researcher conducted a Classification of Psoriatic Arthritis (CASPAR) assessment on the psoriasis subjects on the 1 day of sample collection.

Contacts

Primary ContactXin Li Doctor
13661956326@163.com13661956326
Backup ContactQingyun Wang Miss
WqySci@126.com18251339800

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026