Blood-Based Immune Profiling Combined with Machine Learning Discriminates Psoriatic Arthritis from Psoriasis Patients

Psoriasis (Pso) is a chronic inflammatory skin disease, and up to 30% of Pso patients develop psoriatic arthritis (PsA), which can lead to irreversible joint damage. Early detection of PsA in Pso patients is crucial for timely treatment but difficult for dermatologists to implement. We, therefore, aimed to find disease-specific immune profiles, discriminating Pso from PsA patients, possibly facilitating the correct identification of Pso patients in need of referral to a rheumatology clinic. The phenotypes of peripheral blood immune cells of consecutive Pso and PsA patients were analyzed, and disease-specific immune profiles were identified via a machine learning approach. This approach resulted in a random forest classification model capable of distinguishing PsA from Pso (mean AUC = 0.95). Key PsA-classifying cell subsets selected included increased proportions of differentiated CD4+CD196+CD183-CD194+ and CD4+CD196-CD183-CD194+ T-cells and reduced proportions of CD196+ and CD197+ monocytes, memory CD4+ and CD8+ T-cell subsets and CD4+ regulatory T-cells. Within PsA, joint scores showed an association with memory CD8+CD45RA-CD197- effector T-cells and CD197+ monocytes. To conclude, through the integration of in-depth flow cytometry and machine learning, we identified an immune cell profile discriminating PsA from Pso. This immune profile may aid in timely diagnosing PsA in Pso

Blood-Based Immune Profiling Combined with Machine Learning Discriminates Psoriatic Arthritis from Psoriasis Patients

Psoriasis (Pso) is a chronic inflammatory skin disease, and up to 30% of Pso patients develop psoriatic arthritis (PsA), which can lead to irreversible joint damage. Early detection of PsA in Pso patients is crucial for timely treatment but difficult for dermatologists to implement. We, therefore, aimed to find disease-specific immune profiles, discriminating Pso from PsA patients, possibly facilitating the correct identification of Pso patients in need of referral to a rheumatology clinic. The phenotypes of peripheral blood immune cells of consecutive Pso and PsA patients were analyzed, and disease-specific immune profiles were identified via a machine learning approach. This approach resulted in a random forest classification model capable of distinguishing PsA from Pso (mean AUC = 0.95). Key PsA-classifying cell subsets selected included increased proportions of differentiated CD4+CD196+CD183-CD194+ and CD4+CD196-CD183-CD194+ T-cells and reduced proportions of CD196+ and CD197+ monocytes, memory CD4+ and CD8+ T-cell subsets and CD4+ regulatory T-cells. Within PsA, joint scores showed an association with memory CD8+CD45RACD197- effector T-cells and CD197+ monocytes. To conclude, through the integration of in-depth flow cytometry and machine learning, we identified an immune cell profile discriminating PsA from Pso. This immune profile may aid in timely diagnosing PsA in Pso

Prognostic Value of Thrombus Volume and Interaction With First-Line Endovascular Treatment Device Choice

BACKGROUND: A larger thrombus in patients with acute ischemic stroke might result in more complex endovascular treatment procedures, resulting in poorer patient outcomes. Current evidence on thrombus volume and length related to procedural and functional outcomes remains contradicting. This study aimed to assess the prognostic value of thrombus volume and thrombus length and whether this relationship differs between first-line stent retrievers and aspiration devices for endovascular treatment.METHODS: In this multicenter retrospective cohort study, 670 of 3279 patients from the MR CLEAN Registry (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands) for endovascularly treated large vessel occlusions were included. Thrombus volume (0.1 mL) and length (0.1 mm) based on manual segmentations and measurements were related to reperfusion grade (expanded Treatment in Cerebral Infarction score) after endovascular treatment, the number of retrieval attempts, symptomatic intracranial hemorrhage, and a shift for functional outcome at 90 days measured with the reverted ordinal modified Rankin Scale (odds ratio &gt;1 implies a favorable outcome). Univariable and multivariable linear and logistic regression were used to report common odds ratios (cORs)/adjusted cOR and regression coefficients (B/aB) with 95% CIs. Furthermore, a multiplicative interaction term was used to analyze the relationship between first-line device choice, stent retrievers versus aspiration device, thrombus volume, and outcomes.RESULTS: Thrombus volume was associated with functional outcome (adjusted cOR, 0.83 [95% CI, 0.71-0.97]) and number of retrieval attempts (aB, 0.16 [95% CI, 0.16-0.28]) but not with the other outcome measures. Thrombus length was only associated with functional independence (adjusted cOR, 0.45 [95% CI, 0.24-0.85]). Patients with more voluminous thrombi had worse functional outcomes if endovascular treatment was based on first-line stent retrievers (interaction cOR, 0.67 [95% CI, 0.50-0.89]; P=0.005; adjusted cOR, 0.74 [95% CI, 0.55-1.0]; P=0.04). CONCLUSIONS: In this study, patients with a more voluminous thrombus required more endovascular thrombus retrieval attempts and had a worse functional outcome. Patients with a lengthier thrombus were less likely to achieve functional independence at 90 days. For more voluminous thrombi, first-line stent retrieval compared with first-line aspiration might be associated with worse functional outcome.</p