Echocardiographic epicardial fat thickness and immature granulocyte are novel inflammatory predictors of acute ischemic stroke: a prospective study

Relationship of immature granulocytes with disease activity in rheumatoid arthritis

AIM

To determine whether the IG count (#) and IG percentage (%) are associated with disease activity in rheumatoid arthritis (RA).

METHODS

This retrospective study included 65 RA patients and 65 healthy controls. Clinical and demographic characteristics of controls and RA patients (at active period and when the patients achieved remission) were obtained from medical records. Disease activity was defined by disease activity score 28 (DAS28). Furthermore, the clinical disease activity index (CDAI), and simple disease activity index (SDAI) were calculated. For the differential diagnosis of RA patients from healthy controls, the cut-off value was estimated by making receiver-operator curves (ROC).

RESULTS

In active RA patients, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), IG#, and IG% levels were significantly higher compared to the healthy controls (p < .001, for all). When the patients achieved remission, DAS28, CDAI, SDAI, ESR, CRP, IG#, and IG% values were significantly decreased (p < .001, for all). IG# and IG% were significantly positively correlated with DAS28, CDAI, SDAI, ESR, and CRP (p = .024, p = .008, p = .003, p < .001, p < .001, respectively). According to ROC curve analysis, IG% and IG# were the biomarkers to have a significant diagnostic value for RA with the area under the curve of 0.853 and 0.865 (p < .001, for all).

CONCLUSION

The present study demonstrated that two novel inflammatory markers, IG# and IG%, can be useful for monitoring RA patients' disease activity. Furthermore, IG# and IG% can also be used as fast, inexpensive, and easily available complementary diagnostic markers to diagnose RA patients.

Acute ischemic stroke prediction and predictive factors analysis using hematological indicators in elderly hypertensives post-transient ischemic attack

Elderly hypertensive patients diagnosed with transient ischemic attack (TIA) are at a heightened risk for developing acute ischemic stroke (AIS). This underscores the critical need for effective risk prediction and identification of predictive factors. In our study, we utilized patient data from peripheral blood tests and clinical profiles within hospital information systems. These patients were followed for a three-year period to document incident AIS. Our cohort of 11,056 individuals was randomly divided into training, validation, and testing sets in a 5:2:3 ratio. We developed an XGBoost model, developed using selected indicators, provides an effective and non-invasive method for predicting the risk of AIS in elderly hypertensive patients diagnosed with TIA. Impressively, this model achieved a balanced accuracy of 0.9022, a recall of 0.8688, and a PR-AUC of 0.9315. Notably, our model effectively encapsulates essential data variations involving mixed nonlinear interactions, providing competitive performance against more complex models that incorporate a wider range of variables. Further, we conducted an in-depth analysis of the importance and sensitivity of each selected indicator and their interactions. This research equips clinicians with the necessary tools for more precise identification of high-risk individuals, thereby paving the way for more effective stroke prevention and management strategies.