Evidence map›Paper›PMID 37936709›Full record

ArticleFrontiers in immunology2023

How immune breakthroughs could slow disease progression and improve prognosis in COVID-19 patients: a retrospective study.

Yiting Wang, Bennan Zhao, Xinyi Zhang, Xia Zhang, Fengjiao Gao, Xiaoyan Yuan, Xiaoxia Ren, Maoquan Li, Dafeng Liu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

Yiting Wang *School of Public Health, Chengdu Medical College, Chengdu, China.
Bennan Zhao *The First Ward of Internal Medicine, Public Health Clinic Centre of Chengdu, Chengdu, China.
Xinyi Zhang *Department of Endocrinology & Metabolism, Sichuan University West China Hospital, Chengdu, China.
Xia ZhangThe First Ward of Internal Medicine, Public Health Clinic Centre of Chengdu, Chengdu, China.
Fengjiao GaoThe First Ward of Internal Medicine, Public Health Clinic Centre of Chengdu, Chengdu, China.
Xiaoyan YuanThe First Ward of Internal Medicine, Public Health Clinic Centre of Chengdu, Chengdu, China.
Xiaoxia RenThe First Ward of Internal Medicine, Public Health Clinic Centre of Chengdu, Chengdu, China.
Maoquan LiSchool of Public Health, Chengdu Medical College, Chengdu, China.
Dafeng LiuThe First Ward of Internal Medicine, Public Health Clinic Centre of Chengdu, Chengdu, China.
Public Health Clinical Center of Chengdu · CNChengdu Medical College · CNSichuan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Previous infections and vaccinations have produced preexisting immunity, which differs from primary infection in the organism immune response and may lead to different disease severities and prognoses when reinfected. Objectives: The purpose of this retrospective cohort study was to investigate the impact of immune breakthroughs on disease progression and prognosis in patients with COVID-19. Methods: A retrospective cohort study was conducted on 1513 COVID-19 patients in Chengdu Public Health Clinical Medical Center from January 2020 to November 2022. All patients were divided into the no immunity group (primary infection and unvaccinated, n=1102) and the immune breakthrough group (previous infection or vaccination, n=411). The immune breakthrough group was further divided into the natural immunity subgroup (n=73), the acquired immunity subgroup (n=322) and the mixed immunity subgroup (n=16). The differences in clinical and outcome data and T lymphocyte subsets and antibody levels between two groups or between three subgroups were compared by ANOVA, t test and chi-square test, and the relationship between T lymphocyte subsets and antibody levels and the disease progression and prognosis of COVID-19 patients was assessed by univariate analysis and logistic regression analysis. Results: The total critical rate and the total mortality rate were 2.11% and 0.53%, respectively. The immune breakthrough rate was 27.16%. In the no immunity group, the critical rate and the mortality rate were all higher, and the coronavirus negative conversion time was longer than those in the immune breakthrough group. The differences in the critical rate and the coronavirus negative conversion time between the two groups were all statistically significant (3.72% vs. 0.24%, 14.17 vs. 11.90 days, all p<0.001). In addition, in the no immunity group, although lymphocyte counts and T subsets at admission were higher, all of them decreased consistently and significantly and were significantly lower than those in the immune breakthrough group at the same time from the first week to the fourth week after admission (all p<0.01). The total antibody levels and specific Immunoglobulin G (IgG) levels increased gradually and were always significantly lower than those in the immune breakthrough group at the same time from admission to the fourth week after admission (all p<0.001). Moreover, in the natural immunity subgroup, lymphocyte counts and T subsets at admission were the highest, and total antibody levels and specific IgG levels at admission were the lowest. Then, all of them decreased significantly and were the lowest among the three subgroups at the same time from admission to one month after admission (total antibody: from 546.07 to 158.89, IgG: from 6.00 to 3.95) (all p<0.001). Those in the mixed immunity subgroup were followed by those in the acquired immunity subgroup. While lymphocyte counts and T subsets in these two subgroups and total antibody levels (from 830.84 to 1008.21) and specific IgG levels (from 6.23 to 7.51) in the acquired immunity subgroup increased gradually, total antibody levels (from 1100.82 to 908.58) and specific IgG levels (from 7.14 to 6.58) in the mixed immunity subgroup decreased gradually. Furthermore, T lymphocyte subsets and antibody levels were negatively related to disease severity, prognosis and coronavirus negative conversion time. The total antibody, specific IgM and IgG levels showed good utility for predicting critical COVID-19 patients and dead COVID-19 patients. Conclusion: Among patients with COVID-19 patients, immune breakthroughs resulting from previous infection or vaccination, could decelerate disease progression and enhance prognosis by expediting host cellular and humoral immunity to accelerate virus clearance, especially in individuals who have been vaccinated and previously infected. Clinical trial registry: Chinese Clinical Trial Register ChiCTR2000034563.

Indexed as

COVID-19Disease ProgressionHumansImmunoglobulin GPrognosisRetrospective StudiesSARS-CoV-2Immunoglobulin Gcoronavirus disease 2019 (COVID-19)disease progressionimmune breakthroughsprevious infectionprognosisvaccination

Identifiers

PMID37936709
PMCPMC10627193
OpenAlexW4387868875

What OpenQuestion holds

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.