Arthritis & rheumatology (Hoboken, N.J.) · 2026
Development and Validation of the ALARN Model: A Predictive Mortality Score for Antisynthetase Syndrome.
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Wu Shiyu, Cai Hourong, Zhang Yinli, Zong Chen, Zhu Longyang, Zhang Xinxin, Chen Yiran, Zhang Yingfang, Sun Chao, Pan Qi, Li Shanshan, Shu Xiaoming, Lu Xin, Wang Guochun, Peng Qinglin
Аннотация
OBJECTIVE: To develop and validate a prognostic model for predicting all-cause mortality in patients with antisynthetase syndrome (ASyS).
METHODS: This retrospective study included 1,194 patients with ASyS from three independent institutions in China. The Cox proportional hazards (CPH) method and random survival forest (RSF) algorithm were used for developing a mortality risk prediction model in the derivation cohort (n = 763). The optimal model was simplified into a scoring system and validated in an external cohort of 431 patients from two institutions.
RESULTS: The model constructed using the CPH method in the training cohort incorporated five prognostic factors: age at onset, lactate dehydrogenase, albumin, respiratory failure, and neutrophil-to-lymphocyte ratio. This model demonstrated better performance than the model developed using the RSF algorithm in the internal validation cohort and was subsequently transformed into a simplified scoring system, termed the ALARN score. The ALARN model achieved time-dependent areas under the receiver operating characteristic curves of 0.914 (95% confidence interval [CI] 0.866-0.955), 0.867 (95% CI 0.829-0.906), 0.839 (95% CI 0.789-0.885), and 0.854 (95% CI 0.789-0.909) for predicting 1-, 3-, 5-, and 10-year mortality, respectively. Moreover, patients were effectively stratified into low-, intermediate-, and high-risk groups (ALARN scores of 0-2, 3-5, and 6-8, respectively) with significantly different survival outcomes (log-rank P < 0.0001). Internal and external validation confirmed the robustness of the ALARN score in predicting overall mortality in ASyS.
CONCLUSION: The ALARN score incorporates five readily available clinical parameters and provides a simple, practical tool for predicting mortality risk in patients with ASyS.
METHODS: This retrospective study included 1,194 patients with ASyS from three independent institutions in China. The Cox proportional hazards (CPH) method and random survival forest (RSF) algorithm were used for developing a mortality risk prediction model in the derivation cohort (n = 763). The optimal model was simplified into a scoring system and validated in an external cohort of 431 patients from two institutions.
RESULTS: The model constructed using the CPH method in the training cohort incorporated five prognostic factors: age at onset, lactate dehydrogenase, albumin, respiratory failure, and neutrophil-to-lymphocyte ratio. This model demonstrated better performance than the model developed using the RSF algorithm in the internal validation cohort and was subsequently transformed into a simplified scoring system, termed the ALARN score. The ALARN model achieved time-dependent areas under the receiver operating characteristic curves of 0.914 (95% confidence interval [CI] 0.866-0.955), 0.867 (95% CI 0.829-0.906), 0.839 (95% CI 0.789-0.885), and 0.854 (95% CI 0.789-0.909) for predicting 1-, 3-, 5-, and 10-year mortality, respectively. Moreover, patients were effectively stratified into low-, intermediate-, and high-risk groups (ALARN scores of 0-2, 3-5, and 6-8, respectively) with significantly different survival outcomes (log-rank P < 0.0001). Internal and external validation confirmed the robustness of the ALARN score in predicting overall mortality in ASyS.
CONCLUSION: The ALARN score incorporates five readily available clinical parameters and provides a simple, practical tool for predicting mortality risk in patients with ASyS.