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

ObjectiveCatastrophic 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. MethodsData 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.ResultsTwenty-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 (?(2) =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.ConclusionA 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.