Sarıdaş Ali, Çetinkaya Remzi
Department of Emergency Medicine, University of Health Sciences, Prof. Dr. Cemil Taşçıoğlu City Hospital, 34383 Istanbul, Türkiye.
Department of Emergency Medicine, University of Health Sciences, Gazi Yaşargil Training and Research Hospital, 21070 Diyarbakır, Türkiye.
Diagnostics (Basel). 2025 Apr 17;15(8):1026. doi: 10.3390/diagnostics15081026.
: Sepsis remains a leading cause of mortality worldwide, necessitating the development of effective prognostic markers for early risk stratification. The C-reactive protein-albumin-lymphocyte (CALLY) index is a novel biomarker that integrates inflammatory, nutritional, and immunological parameters. This study aimed to evaluate the association between the CALLY index and 30-day all-cause mortality in sepsis patients. : This retrospective cohort study included adult patients diagnosed with sepsis in the emergency department between 1 January 2022, and 1 January 2025. The CALLY index was calculated as (CRP × absolute lymphocyte count)/albumin. The primary outcome was 30-day all-cause mortality. Five machine learning models-extreme gradient boosting (XGBoost), multilayer perceptron, random forest, support vector machine, and generalized linear model-were developed for mortality prediction. Four feature selection strategies (gain score, SHAP values, Boruta, and LASSO regression) were used to evaluate predictor consistency. The clinical utility of the CALLY index was assessed using decision curve analysis (DCA). : A total of 1644 patients were included, of whom 345 (21.0%) died within 30 days. Among the five machine learning models, the XGBoost model achieved the highest performance (AUC: 0.995, R: 0.867, MAE: 0.063, RMSE: 0.145). In gain-based feature selection, the CALLY index emerged as the top predictor (gain: 0.187), followed by serum lactate (0.185) and white blood cell count (0.117). The CALLY index also ranked second in SHAP analysis (mean value: 0.317) and first in Boruta importance (mean importance: 37.54). DCA showed the highest net clinical benefit of the CALLY index within the 0.10-0.15 risk threshold range. : This study demonstrates that the CALLY index is a significant predictor of 30-day mortality in sepsis patients. Machine learning analysis further reinforced the prognostic value of the CALLY index.
脓毒症仍然是全球范围内主要的死亡原因，因此需要开发有效的预后标志物用于早期风险分层。C反应蛋白-白蛋白-淋巴细胞（CALLY）指数是一种整合了炎症、营养和免疫参数的新型生物标志物。本研究旨在评估CALLY指数与脓毒症患者30天全因死亡率之间的关联。：这项回顾性队列研究纳入了2022年1月1日至2025年1月1日期间在急诊科被诊断为脓毒症的成年患者。CALLY指数的计算方法为（CRP×绝对淋巴细胞计数）/白蛋白。主要结局是30天全因死亡率。开发了五种机器学习模型——极端梯度提升（XGBoost）、多层感知器、随机森林、支持向量机和广义线性模型——用于死亡率预测。使用四种特征选择策略（增益分数、SHAP值、Boruta和LASSO回归）来评估预测指标的一致性。使用决策曲线分析（DCA）评估CALLY指数的临床实用性。：共纳入1644例患者，其中345例（21.0%）在30天内死亡。在五种机器学习模型中，XGBoost模型表现最佳（AUC：0.995，R：0.867，MAE：0.063，RMSE：0.145）。在基于增益的特征选择中，CALLY指数成为首要预测指标（增益：0.187），其次是血清乳酸（0.185）和白细胞计数（0.117）。CALLY指数在SHAP分析中也排名第二（平均值：0.317），在Boruta重要性分析中排名第一（平均重要性：37.54）。DCA显示，在0.10 - 0.15风险阈值范围内，CALLY指数的净临床效益最高。：本研究表明，CALLY指数是脓毒症患者30天死亡率的重要预测指标。机器学习分析进一步强化了CALLY指数的预后价值。
