Nutritional risk and a high NRS2002 score are closely related to disease progression and poor prognosis in patients with COVID-19

Zhou, Yuanyuan; Chen, Yi; Zhang, Xinyi; Zhao, Bin; Gao, Fengjiao; Yuan, Xiaoyan; Zhu, Yanfeng; Liu, Dafeng

doi:10.3389/fnut.2023.1089972

Cited by 3 publications

(3 citation statements)

References 39 publications

(40 reference statements)

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“…In the post epidemic era, breakthrough infections of SARS-CoV-2 are being increasingly observed worldwide due to the high pervasiveness of viral spread, the emergence of novel variants (11), progressive ease of restrictive measures and waning protection against infection. In this retrospective study, we found that the critical illness rate was 2.8%, and the mortality rate was 0.5%, both of which were low, which was consistent with our previous report (16)(17)(18)(19). We also found that the immune breakthrough rate in this study cohort was 27.2%, with 4.8% of patients having previous infections, 22.4% of patients having been vaccinated, and only 6% of patients having been fully vaccinated.…”

Section: Discussionsupporting

confidence: 92%

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

Wang,

Zhao,

Zhang

et al. 2023

Front. Immunol.

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BackgroundPrevious 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.ObjectivesThe purpose of this retrospective cohort study was to investigate the impact of immune breakthroughs on disease progression and prognosis in patients with COVID-19.MethodsA 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.ResultsThe 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.ConclusionAmong 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 registryChinese Clinical Trial Register ChiCTR2000034563.

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Section: Discussionsupporting

confidence: 92%

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

Wang,

Zhao,

Zhang

et al. 2023

Front. Immunol.

Self Cite

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“…This model holds tremendous potential in the realms of prediction, prevention, and personalized medicine for the elderly, particularly as the global elderly population continues to grow, posing challenges to healthcare resources and economic burdens. Prior research mainly focuses on exploring the progress and prognosis of the disease to reduce mortality [ 24 , 25 ]. However, few studies have predicted all-cause mortality in community-dwelling older adults.…”

Section: Discussionmentioning

confidence: 99%

Predicting 1-, 3-, 5-, and 8-year all-cause mortality in a community-dwelling older adult cohort: relevance for predictive, preventive, and personalized medicine

Chen,

Deng,

Lin

et al. 2023

EPMA Journal

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Background Population aging is a global public health issue involving increased prevalence of age-related diseases, and concomitant burden on medical resources and the economy. Ninety-two diseases have been identified as age-related, accounting for 51.3% of the global adult disease burden. The economic cost per capita for older people over 60 years is 10 times that of the younger population. From the aspects of predictive, preventive, and personalized medicine (PPPM), developing a risk-prediction model can help identify individuals at high risk for all-cause mortality and provide an opportunity for targeted prevention through personalized intervention at an early stage. However, there is still a lack of predictive models to help community-dwelling older adults do well in healthcare. Objectives This study aims to develop an accurate 1-, 3-, 5-, and 8-year all-cause mortality risk-prediction model by using clinical multidimensional variables, and investigate risk factors for 1-, 3-, 5-, and 8-year all-cause mortality in community-dwelling older adults to guide primary prevention. Methods This is a two-center cohort study. Inclusion criteria: (1) community-dwelling adult, (2) resided in the districts of Chaonan or Haojiang for more than 6 months in the past 12 months, and (3) completed a health examination. Exclusion criteria: (1) age less than 60 years, (2) more than 30 incomplete variables, (3) no signed informed consent. The primary outcome of the study was all-cause mortality obtained from face-to-face interviews, telephone interviews, and the medical death database from 2012 to 2021. Finally, we enrolled 5085 community-dwelling adults, 60 years and older, who underwent routine health screening in the Chaonan and Haojiang districts, southern China, from 2012 to 2021. Of them, 3091 participants from Chaonan were recruited as the primary training and internal validation study cohort, while 1994 participants from Haojiang were recruited as the external validation cohort. A total of 95 clinical multidimensional variables, including demographics, lifestyle behaviors, symptoms, medical history, family history, physical examination, laboratory tests, and electrocardiogram (ECG) data were collected to identify candidate risk factors and characteristics. Risk factors were identified using least absolute shrinkage and selection operator (LASSO) models and multivariable Cox proportional hazards regression analysis. A nomogram predictive model for 1-, 3-, 5- and 8-year all-cause mortality was constructed. The accuracy and calibration of the nomogram prediction model were assessed using the concordance index (C-index), integrated Brier score (IBS), receiver operating characteristic (ROC), and calibration curves. The clinical validity of the model was assessed using decision curve analysis (DCA). Results Nine independent risk factors for 1-, 3-, 5-, and 8-year all-cause mortality were identified, including increased age, male, alcohol status, higher daily liquor consumption, history of cancer, elevated fasting glucose, lower hemoglobin, higher heart rate, and the occurrence of heart block. The acquisition of risk factor criteria is low cost, easily obtained, convenient for clinical application, and provides new insights and targets for the development of personalized prevention and interventions for high-risk individuals. The areas under the curve (AUC) of the nomogram model were 0.767, 0.776, and 0.806, and the C-indexes were 0.765, 0.775, and 0.797, in the training, internal validation, and external validation sets, respectively. The IBS was less than 0.25, which indicates good calibration. Calibration and decision curves showed that the predicted probabilities were in good agreement with the actual probabilities and had good clinical predictive value for PPPM. Conclusion The personalized risk prediction model can identify individuals at high risk of all-cause mortality, help offer primary care to prevent all-cause mortality, and provide personalized medical treatment for these high-risk individuals from the PPPM perspective. Strict control of daily liquor consumption, lowering fasting glucose, raising hemoglobin, controlling heart rate, and treatment of heart block could be beneficial for improving survival in elderly populations.

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“…For older adults and critically ill patients with multiple underlying diseases, early initiation of enteral and parenteral nutrition support can effectively protect the intestinal mucosal barrier and immune function, reduce the incidence and mortality of infectious complications. [12][13][14] Therefore, timely and standardized nutritional support can effectively improve the nutritional status, immune function, and clinical outcomes of COVID-19 infection, 15 promote recovery and reduce the risk of recurrence, 16,17 improve patient survival and quality of life. Therefore, how to quickly and accurately foresee the risk factors of COVID-19 "re-positive" in malnourished older adults is a clinical problem that needs to be solved urgently.…”

Section: Introductionmentioning

confidence: 99%

Predicting COVID-19 Re-Positive Cases in Malnourished Older Adults: A Clinical Model Development and Validation

Chen,

Luo,

Sun

et al. 2024

CIA

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Purpose Building and validating a clinical prediction model for novel coronavirus (COVID-19) re-positive cases in malnourished older adults. Patients and Methods Malnourished older adults from January to May 2023 were retrospectively collected from the Department of Geriatrics of the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine. They were divided into a “non-re-positive” group and a “re-positive” group based on the number of COVID-19 infections, and into a training set and a validation set at a 7:3 ratio. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to identify predictive factors for COVID-19 re-positivity in malnourished older adults, and a nomogram was constructed. Independent influencing factors were screened by multivariate logistic regression. The model’s goodness-of-fit, discrimination, calibration, and clinical impact were assessed by Hosmer-Lemeshow test, area under the curve (AUC), calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CIC), respectively. Results We included 347 cases, 243 in the training set, and 104 in the validation set. We screened 10 variables as factors influencing the outcome. By multivariate logistic regression analysis, preliminary identified protective factors, risk factors, and independent influencing factors that affect the re-positive outcome. We constructed a clinical prediction model for COVID-19 re-positivity in malnourished older adults. The Hosmer-Lemeshow test yielded χ 2 =5.916, P =0.657; the AUC was 0.881; when the threshold probability was >8%, using this model to predict whether malnourished older adults were re-positive for COVID-19 was more beneficial than implementing intervention programs for all patients; when the threshold was >80%, the positive estimated value was closer to the actual number of cases. Conclusion This model can help identify the risk of COVID-19 re-positivity in malnourished older adults early, facilitate early clinical decision-making and intervention, and have important implications for improving patient outcomes. We also expect more large-scale, multicenter studies to further validate, refine, and update this model.

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Nutritional risk and a high NRS2002 score are closely related to disease progression and poor prognosis in patients with COVID-19

Cited by 3 publications

References 39 publications

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

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

Predicting 1-, 3-, 5-, and 8-year all-cause mortality in a community-dwelling older adult cohort: relevance for predictive, preventive, and personalized medicine

Predicting COVID-19 Re-Positive Cases in Malnourished Older Adults: A Clinical Model Development and Validation

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