Early recognition of catastrophic antiphospholipid syndrome in patients with antiphospholipid syndrome based on a Chinese cohort study.

Abstract

Catastrophic antiphospholipid syndrome (CAPS) is a life-threatening form of antiphospholipid syndrome (APS) with high mortality. We try to develop a predictive model to achieve early recognition of CAPS. Data of APS patients referred into Peking Union Medical College Hospital from May 2013 to October 2021 was collected. A binary logistic regression method was used to identify predictors of CAPS, coefficient B was assigned with score value in the development of prediction model, and risk-stratification was based on the calculated scores using the model. Twenty-seven CAPS (11.9%) occurred in 226 APS patients. CAPS was more likely to occur in male secondary APS patients with a history of hypertension, hyperlipidaemia, and arterial thrombosis, presented with haematological, nephrological and immunological abnormalities simultaneously. Hypertension history (OR 5.091, 95% CI 1.119-23.147), anaemia (OR 116.231, 95% CI 10.512-1285.142), elevated LDH (OR 59.743, 95% CI 7.439-479.815) and proteinuria (OR 11.265, 95% CI 2.118-59.930) were independent predictors for CAPS, and the scores were 1, 3, 3 and 2 points, respectively. The risk scores were divided into high-risk (6-9) and low risk (0-5), the risk for CAPS were 54.1% and 0.6%, with sensitivity of 0.963 and specificity of 0.886. The Nagelkerke's R2 (0.739) and the Omnibus test (χ2 =109.231, df=4, p=0.000) indicated the model has a good fit. The AUC of 0.971 indicated good discrimination. The calibration curve in internal validation showed good calibration of this predictive model. A predictive model of CAPS was developed with hypertension, anaemia, elevated LDH and proteinuria. This model could help identify CAPS in high-risk patients, achieve early recognition and intervention to improve prognosis.