Zhang Weidong, Wu Chenxi, Ni Peifeng, Zhang Sheng, Zhang Hongwei, Zhu Ying, Hu Wei, Diao Mengyuan
Fourth Clinical Medical College of Zhejiang Chinese Medical University, Zhejiang, 310006, Hangzhou, China.
Zhejiang University School of Medicine, Zhejiang, 310006, Hangzhou, China.
J Transl Med. 2025 Jan 6;23(1):16. doi: 10.1186/s12967-024-05975-1.
Cardiac arrest (CA), characterized by its heterogeneity, poses challenges in patient management. This study aimed to identify clinical subphenotypes in CA patients to aid in patient classification, prognosis assessment, and treatment decision-making.
For this study, comprehensive data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) 2.0 database. We excluded patients under 18 years old, those not initially admitted to the intensive care unit (ICU), or treated in the ICU for less than 72 h. A total of 57 clinical parameters relevant to CA patients were selected for analysis. These included demographic data, vital signs, and laboratory parameters. After an extensive literature review and expert consultations, key factors such as temperature (T), sodium (Na), creatinine (CR), glucose (GLU), heart rate (HR), PaO2/FiO2 ratio (P/F), hemoglobin (HB), mean arterial pressure (MAP), platelets (PLT), and white blood cell count (WBC) were identified as the most significant for cluster analysis. Consensus cluster analysis was utilized to examine the mean values of these routine clinical parameters within the first 24 h post-ICU admission to categorize patient classes. Furthermore, in-hospital and 28-day mortality rates of patients across different CA subphenotypes were assessed using multivariate logistic and Cox regression analysis.
After applying exclusion criteria, 719 CA patients were included in the study, with a median age of 67.22 years (IQR: 55.50-79.34), of whom 63.28% were male. The analysis delineated two distinct subphenotypes: Subphenotype 1 (SP1) and Subphenotype 2 (SP2). Compared to SP1, patients in SP2 exhibited significantly higher levels of P/F, HB, MAP, PLT, and Na, but lower levels of T, HR, GLU, WBC, and CR. SP2 patients had a notably higher in-hospital mortality rate compared to SP1 (53.01% for SP2 vs. 39.36% for SP1, P < 0.001). 28-day mortality decreased continuously for both subphenotypes, with a more rapid decline in SP2. These differences remained significant after adjusting for potential covariates (adjusted OR = 1.82, 95% CI: 1.26-2.64, P = 0.002; HR = 1.84, 95% CI: 1.40-2.41, P < 0.001).
The study successfully identified two distinct clinical subphenotypes of CA by analyzing routine clinical data from the first 24 h following ICU admission. SP1 was characterized by a lower rate of in-hospital and 28-day mortality when compared to SP2. This differentiation could play a crucial role in tailoring patient care, assessing prognosis, and guiding more targeted treatment strategies for CA patients.
心脏骤停（CA）具有异质性，给患者管理带来挑战。本研究旨在识别CA患者的临床亚表型，以辅助患者分类、预后评估和治疗决策。
在本研究中，从重症监护医学信息数据库IV（MIMIC-IV）2.0中提取了全面的数据。我们排除了18岁以下的患者、未最初入住重症监护病房（ICU）或在ICU治疗少于72小时的患者。总共选择了57个与CA患者相关的临床参数进行分析。这些参数包括人口统计学数据、生命体征和实验室参数。经过广泛的文献综述和专家咨询，确定温度（T）、钠（Na）、肌酐（CR）、葡萄糖（GLU）、心率（HR）、动脉血氧分压/吸入氧分数比（P/F）、血红蛋白（HB）、平均动脉压（MAP）、血小板（PLT）和白细胞计数（WBC）等关键因素对聚类分析最为重要。采用共识聚类分析来检查ICU入院后最初24小时内这些常规临床参数的平均值，以对患者类别进行分类。此外，使用多因素逻辑回归和Cox回归分析评估不同CA亚表型患者的院内死亡率和28天死亡率。
应用排除标准后，719例CA患者纳入研究，中位年龄为67.22岁（四分位间距：55.50 - 79.34），其中63.28%为男性。分析确定了两种不同的亚表型：亚表型1（SP1）和亚表型2（SP2）。与SP1相比，SP2患者的P/F、HB、MAP、PLT和Na水平显著更高，但T、HR、GLU、WBC和CR水平更低。与SP1相比，SP2患者的院内死亡率显著更高（SP2为53.01%，SP1为39.36%，P < 0.001）。两种亚表型的28天死亡率均持续下降，SP2下降更快。在调整潜在协变量后，这些差异仍然显著（调整后的比值比 = 1.82，95%置信区间：1.26 - 2.64，P = 0.002；风险比 = 1.84，95%置信区间：1.40 - 2.41，P < 0.001）。
本研究通过分析ICU入院后最初24小时的常规临床数据，成功识别出CA的两种不同临床亚表型。与SP2相比，SP1的院内死亡率和28天死亡率较低。这种区分对于为CA患者量身定制护理、评估预后和指导更有针对性的治疗策略可能具有至关重要的作用。
