Usefulness of the CONUT index upon hospital admission as a potential prognostic indicator of COVID-19 health outcomes

Pre-Infection Nutritional Status, Oxidative Stress, and One-Year-Long COVID Persistence in Patients Undergoing Hemodialysis: A Prospective Cohort Study

BACKGROUND

Nutritional status's role in long COVID is evident in the general population, yet unexplored in patients undergoing hemodialysis (HD), posing a research gap. We hypothesized that pre-infection undernutrition in HD patients might impact long COVID persistence by accelerating oxidative stress. The present study aimed to investigate the association between pre-infection nutritional status, oxidative stress, and one-year-long COVID persistence in HD patients.

METHODS

This prospective observational cohort study enrolled 115 HD patients with confirmed COVID-19. Nutritional status was assessed using the Controlling Nutritional Status (CONUT) score twice: before infection and three months post-infection. Oxidative markers included malondialdehyde (MDAs), ceruloplasmin, transferrin, and sulfhydryl groups. The endpoint was one-year-long COVID persistence.

RESULTS

Moderate pre-infection CONUT scores were associated with heightened severe undernutrition risk (p < 0.0001), elevated MDAs (p < 0.0001), and reduced ceruloplasmin levels (p = 0.0009) at three months post-COVID-19 compared to light CONUT scores. Pre-infection CONUT score independently predicted post-COVID oxidative damage [OR 2.3 (95% CI 1.2; 4.6), p < 0.0001] and one-year-long COVID persistence [HR 4.6 (95% CI 1.4; 9.9), p < 0.0001], even after adjusting for potential confounders.

CONCLUSION

Moderate pre-infection undernutrition heightens post-COVID oxidative stress and increases the risk of one-year-long COVID persistence in HD patients.

The CONUT score predicts the length of hospital stay and the risk of long COVID

Introduction

Objective: the Controlling Nutritional Status (CONUT) score is an objective tool widely used to assess nutritional status of patients. We aimed to investigate the value of CONUT score on predicting length of hospital stay (LOS) and the risk of long COVID in patients with COVID-19. Methods: a total of 151 patients with COVID-19 were enrolled for analysis. Patients were followed up for two years from three months after the onset of SARS-CoV-2 infection. CONUT score was calculated on admission. The correlation between CONUT score and LOS were assessed by Spearman's rank correlation coefficient and multivariate linear analysis. The association between different CONUT grade and long COVID was evaluated by Kaplan-Meier survival curves with log-rank test and Cox proportional hazard models. Results: Spearman's rank correlation coefficient showed that CONUT scores were positively correlated with LOS (r = 0.469, p < 0.001). Multivariate linear analysis showed that CONUT score is the only independent determinant of LOS (B 2.055, 95 % CI: 1.067-3.043, p < 0.001). A total of 53 (35.10 %) patients with long COVID were identified. Kaplan-Meier cumulative survival curves and Cox proportional hazards analyses showed that the incidence of long COVID in patients with a higher CONUT score was significantly higher than in patients with lower CONUT score (p < 0.001). Conclusions: higher CONUT score predicts longer LOS and the risk of long COVID in patients with COVID-19. The CONUT score might be useful for risk stratification in COVID-19 patients and help to develop new nutritional treatment strategies for long COVID.

Assessment of nutritional status and clinical outcomes: A comprehensive retrospective analysis of critically ill patients

Assessing nutritional status is vital for understanding and managing patients' clinical conditions. Controlling Nutritional Status (CONUT) score is one of the most frequently used scores that evaluate caloric status, protein reserve, and immunological response estimation. This study assesses the critically ill patients' nutritional status and its relationship with patient-related factors and mortality. Electronic medical records of 1687 Intensive Care Unit (ICU) patient files were reviewed from January 2019 to May 2023 retrospectively. Patient demographic information, clinical characteristics, and laboratory data were extracted and anonymized for analysis. The patients were divided according to the CONUT scores, and retrieved data were compared. The study population's demographic features and clinical outcomes were stratified by CONUT scores. The gender distribution was similar; however, there was a significant age difference between the Low-CONUT and High-CONUT groups (P < .001). High CONUT values are linked to an increased risk of death, intubation, age, APACHE-II scores, and the number of comorbidities. Malnutrition is prevalent in ICU patients with gastrointestinal illnesses and urogenital system disorders, including infections. Patients with a high CONUT score at admission had an increased risk of ICU death even after correcting for covariates. The CONUT score is a trustworthy and practical tool for assessing ICU patients' nutritional status, which is highly associated with ICU mortality. Patients admitted to the ICU with gastrointestinal, urogenital, or infectious disorders should have their nutritional condition assessed carefully. According to our study, the rate of intubated patients admitted to the intensive care unit is higher in malnourished patients. This suggests it should be reviewed in clinical settings, particularly in elderly, frail patients and those with numerous comorbidities.

ABO blood group as a determinant of COVID-19 and Long COVID: An observational, longitudinal, large study

BACKGROUND

An association of ABO blood group and COVID-19 remains controversial.

METHODS

Following STROBE guidance for observational research, we explored the distribution of ABO blood group in patients hospitalized for acute COVID-19 and in those with Long COVID. Contingency tables were made and risk factors were explored using crude and adjusted Mantle-Haentzel odds ratios (OR and 95% CI).

RESULTS

Up to September 2022, there were a total of 5,832 acute COVID-19 hospitalizations in our hospital, corresponding to 5,503 individual patients, of whom blood group determination was available for 1,513 (27.5%). Their distribution by ABO was: 653 (43.2%) group 0, 690 (45.6%) A, 113 (7.5%) B, and 57 (3.8%) AB, which corresponds to the expected frequencies in the general population. In parallel, of 676 patients with Long COVID, blood group determination was available for 135 (20.0%). Their distribution was: 60 (44.4%) from group 0, 61 (45.2%) A, 9 (6.7%) B, and 5 (3.7%) AB. The distribution of the ABO system of Long COVID patients did not show significant differences with respect to that of the total group (p ≥ 0.843). In a multivariate analysis adjusting for age, sex, ethnicity, and severity of acute COVID-19 infection, subgroups A, AB, and B were not significantly associated with developing Long COVID with an OR of 1.015 [0.669-1.541], 1.327 [0.490-3.594] and 0.965 [0.453-2.058], respectively. The effect of the Rh+ factor was also not significant 1,423 [0.772-2,622] regarding Long COVID.

CONCLUSIONS

No association of any ABO blood subgroup with COVID-19 or developing Long COVID was identified.

Prognostic models in COVID-19 infection that predict severity: a systematic review

Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.

Influence of Nutritional Parameters on the Evolution, Severity and Prognosis of Critically Ill Patients with COVID-19

This study evaluated the clinical and nutritional status, the evolution over three days, and the relationship between nutritional, inflammatory, and clinical parameters of critically ill patients with COVID-19. A longitudinal study was conducted in the Intensive Care Unit of the Virgen de las Nieves University Hospital in Granada (Spain). The study population comprised patients with a positive polymerase chain reaction test for COVID-19 presenting critical clinical involvement. Clinical outcomes were collected, and inflammatory and nutritional parameters (albumin, prealbumin, transferrin, transferrin saturation index, cholesterol, triglycerides and Controlling Nutritional Status (CONUT) score) were determined. A total of 202 critical patients with COVID-19 were selected, presenting highly altered clinical-nutritional parameters. The evolution experienced by the patients on the third day of admission was a decrease in albumin (p < 0.001) and an increase in prealbumin (p < 0.001), transferrin (p < 0.002), transferrin saturation index (p < 0.018), and cholesterol (p < 0.001). Low levels of albumin, prealbumin (on the third day) and high CONUT score (on the third day) showed an association with higher mortality. Nutritional variables were inversely correlated with clinical and inflammatory parameters. Critically ill patients with COVID-19 have poor nutritional status related to a poor prognosis of disease severity and mortality.

Poor Nutritional Status and Dynapenia Are Highly Prevalent in Post-Acute COVID-19

Poor nutritional status is common (estimated prevalence 5–69%) in acute coronavirus disease-2019 (COVID-19), and has been associated with hospitalization, the need for intensive care, and mortality. Body composition (BC) and muscle function have also been related in such patients to poor disease outcomes.

As the evidence in the literature is limited, a cross-sectional study was carried out to determine the frequency of malnutrition in a cohort of post-acute COVID-19 patients referred to a rehabilitation center after hospital discharge. BC and muscle strength were assessed and the differences between bedridden and not bedridden patients were specifically evaluated.

The study sample was composed of 144 post-acute COVID-19 patients (mean age 64.8 years; males = 95), 37% of whom were bedridden (males = 60%). Nutritional status was evaluated with Mini-Nutritional Assessment (MNA) and Controlling Nutritional status (CONUT). Fat-free mass (FFM) and skeletal muscle mass (SM) were estimated using bioelectrical impedance analysis (BIA). Raw BIA variables (phase angle = PhA and impedance ratios = IRs) were also determined and handgrip strength (HGS) was measured. Dynapenia was identified according to the 2019 EWGSOP criteria.

According to MNA, 18% (n. 26) of patients were malnourished and 62% (n. 89) were at risk of malnutrition. As for CONUT, 21% (n. 31) of cases had moderate–severe malnutrition and 58% (n. 83) had light malnutrition. Abnormalities of raw BIA variables (low PhA and high IRs) and low HGS were more common in bedridden patients, in those who were malnourished, or had low FFM or SM. Dynapenic patients were 65% men and 47% women.

In conclusion, malnutrition, BC alterations, and low HGS occur in post-acute COVID-19 patients and are more common in bedridden patients. Further studies are needed to identify reliable algorithms for assessing nutritional status in post-acute COVID-19 patients undergoing rehabilitation.

Nutritional Risk Assessment Scores Effectively Predict Mortality in Critically Ill Patients with Severe COVID-19

Background: Malnutrition predicts a worse outcome for critically ill patients. However, quick, easy-to-use nutritional risk assessment tools have not been adequately validated. Aims and Methods: The study aimed to evaluate the role of four biological nutritional risk assessment instruments (the Prognostic Nutritional Index—PNI, the Controlling Nutritional Status Score—CONUT, the Nutrition Risk in Critically Ill—NUTRIC, and the modified NUTRIC—mNUTRIC), along with CT-derived fat tissue and muscle mass measurements in predicting in-hospital mortality in a consecutive series of 90 patients hospitalized in the intensive care unit for COVID-19-associated ARDS. Results: In-hospital mortality was 46.7% (n = 42/90). Non-survivors had a significantly higher nutritional risk, as expressed by all four scores. All scores were independent predictors of mortality on the multivariate regression models. PNI had the best discriminative capabilities for mortality, with an area under the curve (AUC) of 0.77 for a cut-off value of 28.05. All scores had an AUC above 0.72. The volume of fat tissue and muscle mass were not associated with increased mortality risk. Conclusions: PNI, CONUT, NUTRIC, and mNUTRIC are valuable nutritional risk assessment tools that can accurately predict mortality in critically ill patients with COVID-19-associated ARDS.