npj Digital Medicine (Feb 2023)

Multi-modality data-driven analysis of diagnosis and treatment of psoriatic arthritis

  • Jing Xu,
  • Jiarui Ou,
  • Chen Li,
  • Zheng Zhu,
  • Jian Li,
  • Hailun Zhang,
  • Junchen Chen,
  • Bin Yi,
  • Wu Zhu,
  • Weiru Zhang,
  • Guanxiong Zhang,
  • Qian Gao,
  • Yehong Kuang,
  • Jiangning Song,
  • Xiang Chen,
  • Hong Liu

DOI
https://doi.org/10.1038/s41746-023-00757-3
Journal volume & issue
Vol. 6, no. 1
pp. 1 – 11

Abstract

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Abstract Psoriatic arthritis (PsA) is associated with psoriasis, featured by its irreversible joint symptoms. Despite the significant impact on the healthcare system, it is still challenging to leverage machine learning or statistical models to predict PsA and its progression, or analyze drug efficacy. With 3961 patients’ clinical records, we developed a machine learning model for PsA diagnosis and analysis of PsA progression risk, respectively. Furthermore, general additive models (GAMs) and the Kaplan–Meier (KM) method were applied to analyze the efficacy of various drugs on psoriasis treatment and inhibiting PsA progression. The independent experiment on the PsA prediction model demonstrates outstanding prediction performance with an AUC score of 0.87 and an AUPR score of 0.89, and the Jackknife validation test on the PsA progression prediction model also suggests the superior performance with an AUC score of 0.80 and an AUPR score of 0.83, respectively. We also identified that interleukin-17 inhibitors were the more effective drug for severe psoriasis compared to other drugs, and methotrexate had a lower effect in inhibiting PsA progression. The results demonstrate that machine learning and statistical approaches enable accurate early prediction of PsA and its progression, and analysis of drug efficacy.