Prognostic factors in patients with secondary hemophagocytic lymphohistioc ytosis in a Chinese cohort.

Hemophagocytic lymphohistiocytosis (HLH) is a rare hyperinflammatory syndrome with high mortality mediated by an unbridled and persistent activation of cytotoxic T lymphocytes and natural killer cells. However, the influence factors of early death in adult sHLH patients are still not fully elucidated, which need further investigating. We have conducted an observational study of adult HLH patients between January 2016 and December 2022. All patients are enrolled according to HLH-2004 criteria. Clinical manifestations, laboratory data, treatments, and outcomes have been recorded. Influence factors associated with prognosis are calculated by using logistic regression models. Overall, 220 patients enrolled in this study. The etiologies of HLH were divided into five groups including autoimmune-associated hemophagocytic syndrome (AAHS) (n = 90, 40.9%), malignancies (n = 73, 33.2%), EBV-HLH (n = 18, 8.2%), infection excluded EBV (n = 24, 10.9%), and other triggers (n = 15, 6.8%). Among them, EBV-HLH had the highest mortality (77.8%), and AAHS had the lowest mortality (14.4%). Multivariate analysis indicated that age (≥ 38 years old), cytopenia ≥ 2 lines, platelets (≤ 50 × 109/L), aspartate aminotransferase (≥ 135U/L), prothrombin time (≥ 14.9 s) and activated partial thromboplastin time (≥ 38.5s), EBV, and fungal infection are independent risk factors for poor prognosis of HLH. Adult HLH patients with elder age, cytopenia ≥ 2 lines, levels of decreased platelets, increased AST, prolonged PT and APTT, EBV, and fungal infection tend to have a poor prognosis.

A predictive model for premature atherosclerosis in systemic lupus erythematosus based on clinical characteristics.

OBJECTIVE: Systemic lupus erythematosus (SLE) is associated with a significant risk of atherosclerotic cardiovascular disease, especially in the development of premature atherosclerosis. Specific prediction models for premature atherosclerosis in SLE patients are still limited. The objective of this study was to establish a predictive model for premature atherosclerosis in SLE. METHOD: The study collected clinical and laboratory data from 148 SLE patients under the age of 55, between January 2021 and June 2023. The least absolute shrinkage and selection operator logistic regression model was utilized to identify potentially relevant features. Subsequently, a nomogram was developed using multivariable logistic analysis. The performance of the nomogram was evaluated through a receiver-operating characteristic curve, calibration curve, and decision curve analysis (DCA). RESULTS: A total of 148 SLE patients who fulfilled the inclusion criteria were enrolled in the study, of whom 53 patients (35.81%) met the definition of premature atherosclerosis. Hypertension, antiphospholipid syndrome, azathioprine use, duration of glucocorticoid, and age of patients were included in the multivariable regression. The nomogram, based on the non-overfitting multivariable model, was internally validated and demonstrated sufficient clinical utility for assessing the risk of premature atherosclerosis (area under curve: 0.867). CONCLUSIONS: The comprehensive nomogram constructed in this study serves as a useful and convenient tool for evaluating the risk of premature atherosclerosis in SLE patients. It is helpful for clinicians to early identify SLE patients with premature atherosclerosis and facilitates the implementation of more effective preventive measures. Key Points • SLE patients are at a significantly higher risk of developing premature atherosclerosis compared to the general population, and this risk persists even in cases with low disease activity. Traditional models used to evaluate and predict premature atherosclerosis in SLE patients often underestimate the risk. • This study establishes a comprehensive and visually orientated predictive model of premature atherosclerosis in SLE patients, based on clinical characteristics. • The scoring system allows for convenient and effective prediction of individual incidence of premature atherosclerosis, and could provide valuable information for identification and making further intervention decision.