Severe Disease in Patients With Recent-Onset Psoriatic Arthritis. Prediction Model Based on Machine Learning

Queiró, Rubén; Seoane‐Mato, Daniel; Laíz, A.; Agirregoikoa, E. Galindez; Montilla, Carlos; Park, Hye-Sang; Tasende, José Antonio Pinto; Baute, Juan José Bethencourt; Ibáñez, Beatriz Joven; Toniolo, E.; Ramírez, Julio; García-Hinojosa, Cristina Pruenza

doi:10.3389/fmed.2022.891863

Cited by 13 publications

(12 citation statements)

References 30 publications

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“…Besides, other studies built ML models based on blood immune profiling and serum proteomics to discriminate between PsA and psoriatic patients 106,107 . Based on these models, a minimal disease activity (a clinical state characterized by low levels of disease activity) predictive model was developed, with the variables of global pain, impact of the disease (PsAID), patient global assessment of disease, and physical function (HAQ‐Disability Index) having the greatest predictive ability 108 . Besides PsA, patient records were detected to identify the top predictors of noncalcified coronary plaque burden in psoriasis, which included obesity, dyslipidemia, and inflammation factors 109 .…”

Section: Ai Application In Psoriasis: Where We Are Nowmentioning

confidence: 99%

“…106,107 Based on these models, a minimal disease activity (a clinical state characterized by low levels of disease activity) predictive model was developed, with the variables of global pain, impact of the disease (PsAID), patient global assessment of disease, and physical function (HAQ-Disability Index) having the greatest predictive ability. 108 Besides PsA, patient records were detected to identify the top predictors of noncalcified coronary plaque burden in psoriasis, which included obesity, dyslipidemia, and inflammation factors. 109 Moreover, in a study conducted by Conic et al, the distribution width of red blood cells and mean platelet volume were examined as predictors of major cardiovascular events in persons with PsA, and the result indicated that parameter values above a certain threshold were associated with an increased cardiovascular risk and a poor therapeutic response.…”

Section: Preventive Medicinementioning

confidence: 99%

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Artificial intelligence in psoriasis: Where we are and where we are going

Liu,

Wang,

et al. 2023

Experimental Dermatology

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Artificial intelligence (AI) is a field of computer science that involves the development of programs designed to replicate human cognitive processes and the analysis of complex data. In dermatology, which is predominantly a visual‐based diagnostic field, AI has become increasingly important in improving professional processes, particularly in the diagnosis of psoriasis. In this review, we summarized current AI applications in psoriasis: (i) diagnosis, including identification, classification, lesion segmentation, lesion severity and area scoring; (ii) treatment, including prediction treatment efficiency and prediction candidate drugs; (iii) management, including e‐health and preventive medicine. Key challenges and future aspects of AI in psoriasis were also discussed, in hope of providing potential directions for future studies.

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Section: Ai Application In Psoriasis: Where We Are Nowmentioning

confidence: 99%

Section: Preventive Medicinementioning

confidence: 99%

Artificial intelligence in psoriasis: Where we are and where we are going

Liu,

Wang,

et al. 2023

Experimental Dermatology

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“…Таким образом, полученные нами данные частично согласуются с этими результатами. Следует отметить, что именно шкала «Боль» по PsAID-12 позволяет оценивать аспект, которому пациенты придают максимальное значение при описании своего состояния [16].…”

Section: в данном случае значение пмаб было значительно выше 389 что ...unclassified

Search for predictors of achieving minimal disease activity during tofacitinib therapy in patients with psoriatic arthritis

Vorobyova,

Korotaeva,

Glukhova

et al. 2023

Sovremennaâ revmatologiâ

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Objective: to search predictors of achieving minimal disease activity (MDA) during therapy in patients with psoriatic arthritis (PsA).Materials and methods. The study included 41 patients, predominantly men (58.9 %), with a confirmed PsA diagnosis and a disease duration of at least 6 months. In all cases, the diagnosis fulfilled the CASPAR criteria. The mean age of the patients at the time of enrolment in the study was 43.0±10.1 years, the duration of PsA was 7.7±7.1 years, the duration of psoriasis was 18.6±10.4 years, and the DAPSA index was 44.2±17.1. All patients were prescribed tofacitinib at a dose of 5 mg twice daily, followed by a possible dose increase to 10 mg twice daily. In addition to a general clinical examination and a standard rheumatological examination, the level of secreted DKK-1 protein and health-related quality of life (HRQoL, using a special PsAID-12 questionnaire) were determined. Multivariate stepwise discriminant analysis was used to search for predictors for the achievement of MDA in patients with PsA and to calculate the coefficients.Results and discussion. Based on the results obtained, a predictor for the achievement of MDA (PMDA) was developed: PMDA=-1.165 × number of inflamed entheses + DKK-1 level (pmol/l) + 3.086 × PsAID-12 “Skin lesions” scale value (if this indicator was ≤3 points, it was assigned a value of 1, if it was >3 points – 0) + 2.568 × PsAID-12 “Pain” scale (if this indicator was ≤6 points, it was assigned a value of 1, if it was >6 points – 0).The ROC analysis, which reflects the prognostic significance of this index, showed AUC (area under the curve) of 0.803 (95% confidence interval 0.739–0.867; p=0.02). PMDA=3.89 was chosen as the cut-off value; the sensitivity of this indicator was 91 %, the specificity – 79 %. Therefore with a PMDA ≥3.89, the probability of the patient achieving a MDA after 3 months is high; with a PMDA ˂ 3.89, it is low.Conclusion. We identified factors influencing the achievement of MDA in patients with PsA and developed a mathematical model. It allows timely assessment of the quality of treatment and its correction if necessary, thereby slowing disease progression.

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“…However, due to the heterogeneity of this complex syndrome and despite the presence of well-known prognostic factors, there are still a few studies on the severity of disease course in PsA. The concept of severity has been often associated, or confused, with disease activity, but it has also been associated with the presence of polyarticular involvement, joint function reduction, and mainly damage [ 12 ]. Generally, the concept of severity should be distinguished from the mere inflammatory activity and, at present, this is still debated.…”

Section: Introductionmentioning

confidence: 99%

Clinical Characteristics of “Severe” Peripheral Psoriatic Arthritis: A Retrospective Analysis of a Longitudinal Cohort

Lubrano,

Scriffignano,

Perrotta

2024

Rheumatol Ther

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Introduction The concept of severity in a multidomain disease such as psoriatic arthritis (PsA) is still not well defined. The aim of this study was to identify the clinical characteristics of patients with severe peripheral PsA. Methods Retrospective analysis of a longitudinal cohort. Demographic and clinical characteristics of patients with PsA were collected at baseline and at last follow-up. We defined the severe population using the modified Composite Psoriatic Disease Activity Index (mCPDAI); which excludes ankylosing spondylitis quality of life scale). Hence, patients with a score of 3 in at least one domain were defined as having severe PsA. Clinical characteristics of patients fulfilling the definition of severe PsA were compared to those non-severe. Results We evaluated 177 patients with peripheral PsA (M/F: 98/76). Of these, 64 (36.1%) were identified as severe according to the mCPDAI criteria, at baseline. Eighteen patients (10.1%) at last follow-up still met the definition of severe PsA. At last follow-up visit, severe patients with PsA were only males (18/18, P < 0.01) and have worse outcomes in terms of disease activity, pain, function, and impact of disease. Male sex and the severity of skin involvement at baseline were factors associated with the presence of severe PsA. The agreement between the presence of severe PsA and the absence of minimal disease activity was slight [Cohen’s k : 0.174 (0.084–0.264)]. Conclusions Our study showed that severe patients with PsA had more disease activity, pain, and impact of disease than non-severe patients. Furthermore, we demonstrated that severity and disease activity are not interchangeable concepts.

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Severe Disease in Patients With Recent-Onset Psoriatic Arthritis. Prediction Model Based on Machine Learning

Cited by 13 publications

References 30 publications

Artificial intelligence in psoriasis: Where we are and where we are going

Artificial intelligence in psoriasis: Where we are and where we are going

Search for predictors of achieving minimal disease activity during tofacitinib therapy in patients with psoriatic arthritis

Clinical Characteristics of “Severe” Peripheral Psoriatic Arthritis: A Retrospective Analysis of a Longitudinal Cohort

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