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A novel aGAPSS-based nomogram for the prediction of ischemic stroke in patients with antiphospholipid syndrome
Xiaodong Song1, Yangyi Fan1, Yuan Jia2
1Department of Neurology, Peking University People's Hospital, Beijing, China.
Insights
Antiphospholipid syndrome (APS) patients at high risk for ischemic stroke (IS) can be identified using a new nomogram. The aGAPSS-IS tool combines age, diabetes, hyperuricemia, platelet count, and the adjusted Global Anti-Phospholipid Syndrome Score (aGAPSS) for better prediction.
Area of Science:
- Cardiovascular Medicine
- Rheumatology
- Neurology
Background:
- Antiphospholipid syndrome (APS) is a significant risk factor for arterial thrombosis, particularly ischemic stroke (IS).
- IS in APS patients leads to high mortality and severe disability, necessitating early risk identification and preventive strategies.
- Current risk assessment tools require enhancement for better IS prediction in APS.
Purpose of the Study:
- To identify independent clinical and laboratory risk factors for IS in patients with APS.
- To develop and validate a predictive nomogram (aGAPSS-IS) for IS risk stratification in APS patients.
- To compare the performance of the novel nomogram against the existing adjusted Global Anti-Phospholipid Syndrome Score (aGAPSS).
Main Methods:
- Retrospective enrollment of 478 APS patients, divided into training and validation cohorts.
- Univariate and multivariate binary logistic regression analyses to identify IS predictors.
- Development of a nomogram incorporating identified predictors and evaluation of its performance using AUROC, calibration plots, and decision curve analysis (DCA).
Main Results:
- During a mean follow-up of 2.7 years, 26.9% of patients experienced IS.
- Independent predictors of IS included age, diabetes, hyperuricemia, platelet count, and aGAPSS.
- The developed aGAPSS-IS nomogram demonstrated satisfactory predictive performance in both training (AUROC=0.853) and validation (AUROC=0.793) cohorts.
- Decision curve analysis indicated that aGAPSS-IS offers superior net benefits compared to aGAPSS.
Conclusions:
- Age, diabetes, hyperuricemia, platelet count, and aGAPSS are key risk factors for IS in APS patients.
- The aGAPSS-IS nomogram provides a valuable tool for IS risk stratification in APS patients.
- This nomogram utilizes routinely available data, facilitating clinical application for personalized preventive measures.
Background:
Ischemic stroke (IS) is the most common and life-threatening arterial manifestation of antiphospholipid syndrome (APS). It is related to high mortality and severe permanent disability in survivors. Thus, it is essential to identify patients with APS at high risk of IS and adopt individual-level preventive measures. This study was conducted to identify risk factors for IS in patients with APS and to develop a nomogram specifically for IS prediction in these patients by combining the adjusted Global Anti-Phospholipid Syndrome Score (aGAPSS) with additional clinical and laboratory data.
Methods:
A total of 478 consecutive patients with APS were enrolled retrospectively. All patients were randomly assigned to the training and validation cohorts. Univariate and multivariate binary logistic analyses were conducted to identify predictors of IS in the training cohort. Then, a nomogram was developed based on these predictors. The predictive performance of the nomogram for the training and validation cohorts was evaluated by determining areas under the receiver operating characteristic curve (AUROC) and creating calibration plots. A decision curve analysis (DCA) was conducted to compare the potential net benefits of the nomogram with those of the aGAPSS.
Results:
During a mean follow-up period of 2.7 years, 26.9% (129/478) of the patients were diagnosed with IS. Binary logistic regression analysis revealed that five risk factors were independent clinical predictors of IS: age (P < 0.001), diabetes (P = 0.030), hyperuricemia (P < 0.001), the platelet count (P = 0.001), and the aGAPSS (P = 0.001). These predictors were incorporated into the nomogram, named the aGAPSS-IS. The nomogram showed satisfactory performance in the training [AUROC = 0.853 (95% CI, 0.802-0.896] and validation [AUROC = 0.793 (95% CI, 0.737-0.843)] cohorts. Calibration curves showed good concordance between observed and nomogram-predicted probability in the training and validation cohorts. The DCA confirmed that the aGAPSS-IS provided more net benefits than the aGAPSS in both cohorts.
Conclusion:
Age, diabetes, hyperuricemia, the platelet count, and the aGAPSS were risk factors for IS in patients with APS. The aGAPSS-IS may be a good tool for IS risk stratification for patients with APS based on routinely available data.
