TOPLINE:

A five-item clinical score incorporating anti-transcriptional intermediary factor 1-gamma (TIF1-gamma) antibody status, skin findings, anemia, disease subtype, and lung involvement demonstrated a strong predictive capability for cancer in patients with dermatomyositis.

METHODOLOGY:

• To develop and validate a practical model to predict the likelihood of cancer in patients with dermatomyositis (DM), whose risk for cancer is increased compared with that of the general population, researchers conducted a retrospective study of 546 adults (mean age, 49.8 years; 69.6% female) with DM or clinically amyopathic DM (CADM) from dermatology (training cohort, n = 284) and rheumatology (validation cohort, n = 262) departments between 2015 and 2022.
• They measured anti-TIF1-gamma antibodies; variable selection for the prediction model was performed with Least Absolute Shrinkage and Selection Operator regression followed by multivariable logistic regression to identify independent predictors.
• They converted regression coefficients into a simple 0-5 TIP-CA score and externally validated discrimination using area under the curve and sensitivity or specificity with a 2.5-point cutoff.
• The primary outcome was the occurrence of cancer in patients with DM.

TAKEAWAY:

• Five factors significantly associated with cancers were identified: positive anti-TIF1-gamma antibody (odds ratio [OR], 3.77; 95% CI, 1.82-7.83), poikiloderma (OR, 3.05; 95% CI, 1.48-6.29), classic DM subtype vs clinically amyopathic disease (OR, 2.76; 95% CI, 1.23-6.23), and anemia (OR, 2.44; 95% CI, 1.18-5.03); interstitial lung disease was associated with a lower odds of concurrent cancer (OR, 0.39; 95% CI, 0.17-0.89).
• High prediction model (named TIP-CA) scores of 4-5 identified patients with a markedly increased likelihood of concurrent cancer, whereas scores of 0-1 were low risk and scores of 2-3 were moderate risk.
• The model demonstrated robust discriminatory capability in both the derivation and validation cohorts.
• A cutoff of 2.5 points was used to balance sensitivity and specificity for clinical stratification.

IN PRACTICE:

“The TIP-CA scoring system provides a novel tool for assessing cancer association in patients with DM/CADM, with clinical potential to improve patient outcomes by facilitating early detection and enhancing survival rates,” the authors of the study concluded.

SOURCE:

The study was led by Jiaqi Ye, MD, Department of Dermatology, Ruijin Hospital, and Wanlong Wu, MD, Department of Rheumatology, Renji Hospital, both in Shanghai, China, and was published online on December 3 in JAMA Dermatology.

LIMITATIONS:

Laboratory data were collected at varying timepoints, potentially introducing variability due to differences in personnel and equipment. The model estimated association with concurrent cancer rather than future cancer risk.

DISCLOSURES:

The study received support from the National Natural Science Foundation of China, Shanghai Yiyuan Rising Star Outstanding Young Medical Talents, the innovative research team of high-level local universities in Shanghai, and the Science and Technology Commission of Shanghai Municipal Jiading District. The authors reported having a patent pending for the TIP-CA model to predict malignant tumors associated with DM.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.