Cao Zhantao, Chen Ningjing, Jiang Hanjing, Li Jian, Zheng Kailin, Chen Jingting, Wang Yunsu, Chen Jun
Department of Cardiology, Xiamen Hospital of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, 361015 Xiamen, Fujian, China.
Department of Breast Surgery, Xiamen Hospital of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, 361015 Xiamen, Fujian, China.
Rev Cardiovasc Med. 2025 Aug 20;26(8):43942. doi: 10.31083/RCM43942. eCollection 2025 Aug.
The hemoglobin, albumin, lymphocyte, and platelet (HALP) score represents a meaningful predictor in many cardiovascular diseases. However, the predictive utility of this score for the outcome of patients admitted to the intensive care unit (ICU) due to acute myocardial infarction (AMI) has yet to be fully elucidated.
Information from the Medical Information Mart for Intensive Care (MIMIC)-IV v3.1 database was used to analyze the association between the HALP score and 90 days and 365 days all-cause mortality in critically ill patients with AMI. Patients were grouped according to the calculated HALP quartiles. Cox proportional hazards regression analysis and restricted cubic spline (RCS) analysis were performed to assess the association between the HALP score and mortality risk. A recursive algorithm identified the HALP inflection point, thus defining high and low HALP groups for the Kaplan-Meier survival analysis. Subgroup analyses analyzed the robustness across clinical strata. Furthermore, predictive models based on machine learning algorithms that included the HALP score were constructed to estimate 90 days mortality. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC).
A total of 818 AMI patients were included. The analysis revealed mortality rates of 31% at 90 days and 40% at 365 days. Elevated HALP values were independently linked to a reduced risk of death. In fully adjusted models, patients in the top HALP quartile exhibited significantly lower all-cause mortality at 90 days (hazard ratio (HR) = 0.68; 95% confidence interval (CI): 0.47-0.99; = 0.047) and 365 days (HR = 0.66; 95% CI: 0.47-0.90; = 0.011). A nonlinear, inverse "L-shaped" association was observed, with an inflection point identified at a HALP value of 19.41. Below this value, each unit increase in the HALP score reduced mortality risk by 2.4%-2.7%. The Kaplan-Meier curves confirmed an improved survival above the threshold. Meanwhile, the subgroup analyses revealed a generally consistent association between the HALP score and mortality, except for age, where a significant interaction was observed ( = 0.003), indicating a stronger protective effect in older patients. Machine learning analyses supported the robustness and predictive value of the HALP score, with a maximum AUC of 0.7804.
The HALP score is significantly associated with all-cause mortality among critically ill individuals suffering from AMI.
血红蛋白、白蛋白、淋巴细胞和血小板（HALP）评分是许多心血管疾病中有意义的预测指标。然而，该评分对因急性心肌梗死（AMI）入住重症监护病房（ICU）患者预后的预测效用尚未完全阐明。
使用重症监护医学信息集市（MIMIC）-IV v3.1数据库中的信息，分析HALP评分与AMI重症患者90天和365天全因死亡率之间的关联。根据计算出的HALP四分位数对患者进行分组。进行Cox比例风险回归分析和限制性立方样条（RCS）分析，以评估HALP评分与死亡风险之间的关联。一种递归算法确定了HALP拐点，从而为Kaplan-Meier生存分析定义了高HALP组和低HALP组。亚组分析分析了各临床亚组间的稳健性。此外，构建了包含HALP评分的基于机器学习算法的预测模型，以估计90天死亡率。使用受试者工作特征曲线（AUC）下的面积评估模型的性能。
共纳入818例AMI患者。分析显示90天死亡率为31%，365天死亡率为40%。HALP值升高与死亡风险降低独立相关。在完全调整的模型中，HALP四分位数最高的患者在90天（风险比（HR）=0.68；95%置信区间（CI）：0.47-0.99；P=0.047）和365天（HR=0.66；95%CI：0.47-0.90；P=0.011）时全因死亡率显著较低。观察到一种非线性的反向“L形”关联，在HALP值为19.41时确定了一个拐点。低于该值，HALP评分每增加一个单位，死亡风险降低2.4%-2.7%。Kaplan-Meier曲线证实阈值以上生存率有所提高。同时，亚组分析显示HALP评分与死亡率之间的关联总体一致，但年龄组存在显著交互作用（P=0.003），表明在老年患者中保护作用更强。机器学习分析支持HALP评分具有稳健性和预测价值，最大AUC为0.7804。
HALP评分与AMI重症患者的全因死亡率显著相关。
