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| 院内新型冠状病毒肺炎患者IgG滴度的动态变化：一项基于组的轨迹分析 |
| 张晨旭1△,郭玉峰2△,王颢3,彭驰1,齐戈尧1,金志超1* |
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(1. 海军军医大学(第二军医大学)卫生勤务学系卫生统计学教研室, 上海 200433;
2. 海军军医大学(第二军医大学)第二附属医院医务处, 上海 200003;
3. 海军军医大学(第二军医大学)第一附属医院肛肠外科, 上海 200433
△共同第一作者
*通信作者) |
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| 摘要: |
| 目的 研究新型冠状病毒肺炎（COVID-19）患者住院期间定量检测的IgG滴度的动态变化轨迹，揭示感染病毒后机体的免疫过程。方法 回顾性分析2020年2月5日至4月15日在武汉市火神山医院和湖北省妇幼保健院光谷院区住院治疗的COVID-19患者的临床资料及不同时间点IgG滴度定量检测数据。应用基于组的轨迹模型从患者抗体的时间序列数据中进行亚组识别，然后对各轨迹组患者的临床特征及结局进行比较。结果 共734例符合筛选标准的患者被纳入研究。从其抗体数据中识别出3个轨迹组：组1（持续低抗体组，60例，占8.17%）、组2（中等抗体组，38例，占5.18%）和组3（高抗体组，636例，占86.65%）。3组患者的住院天数和病毒清除时间差异有统计学意义（P均＜0.001），组1的住院天数和病毒清除时间均最短；而全因死亡率和病情恶化率在3组间差异无统计学意义（P均＞0.05）。结论 具有不同IgG发展轨迹的COVID-19患者可能有不同的预后及免疫特征，抗体滴度持续较高的患者可能需要更多的医疗关注。 |
| 关键词: 新型冠状病毒肺炎 免疫球蛋白G 基于组的轨迹模型 临床结局 |
| DOI：10.16781/j.CN31-2187/R.20211050 |
| 投稿时间：2021-10-20修订日期：2021-12-17 |
| 基金项目:上海市公共卫生体系建设三年行动计划学科建设项目(GWV-10.1-XK05),海军军医大学(第二军医大学)"三航"计划. |
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| Dynamic change of IgG titers in hospitalized patients with coronavirus disease 2019:a group-based trajectory analysis |
| ZHANG Chen-xu1△,GUO Yu-feng2△,WANG Hao3,PENG Chi1,QI Ge-yao1,JIN Zhi-chao1* |
(1. Department of Health Statistics, Faculty of Health Services, Naval Medical University (Second Military Medical University), Shanghai 200433, China;
2. Medical Affair Office, The Second Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai 200003, China;
3. Department of Colorectal Surgery, The First Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai 200433, China
△Co-first authors.
* Corresponding author) |
| Abstract: |
| Objective To study the dynamic trajectories of quantitative immunoglobulin G (IgG) titers of hospitalized coronavirus disease 2019 (COVID-19) patients and reveal the immune process of the organism after infection.Methods The clinical data and quantitative IgG titers at different time points of hospitalized COVID-19 patients in Wuhan Huoshenshan Hospital and Guanggu Branch of Maternity and Child Healthcare Hospital of Hubei Province from Feb. 5 to Apr. 15, 2020 were retrospectively analyzed. Group-based trajectory modeling was used to identify the subgroups from time-series data of patients’ antibody titers, and then the clinical characteristics and outcomes were compared among these trajectory groups.Results Totally, 734 patients who met the criteria were included. Three IgG trajectory groups were identified from the antibody data: group 1 (consistently low group, 60 cases［ 8.17%］ ), group 2 (moderate group, 38 cases ［5.18%］) and group 3 (high group, 636 cases［86.65%］). The hospitalization days and the virus clearance time of patients in the 3 groups were significantly different (both P＜0.001), those in group 1 were the shortest, while the all-cause mortality and disease deterioration rate had no significant difference in the 3 groups (both P＞0.05).Conclusion Patients with different IgG antibody developmental trajectories may have heterogeneous prognosis and immune process. Patients with consistently higher longitudinal antibody titers may require more medical attention. |
| Key words: coronavirus disease 2019 immunoglobulin G group-based trajectory modeling clinical outcomes |
