The use of prognostic nutritional index (PNI) and selected inflammatory indicators for predicting malnutrition in COVID-19 patients: A retrospective study

Predictivity of the Prognostic Nutritional Index and Systemic Inflammation Index for All-Cause In-Hospital Mortality in Geriatric and Adult COVID-19 Inpatients

Background: The prognostic nutritional index (PNI) and the systemic immune inflammation index (SII) have been used as simple risk-stratification predictors for COVID-19 severity and mortality in the general population. However, the associations between these indices and mortality might differ due to age-related changes such as inflammaging and several comorbid conditions in older patients. Therefore, we aimed to compare the predictivity of the PNI and SII for mortality among hospitalized older patients and patients under 65 years old. Methods: Patients hospitalized with COVID-19 from March 2020 to December 2020 were retrospectively included. The PNI and SII were calculated from hospital records within the first 48 h after admission. Data were evaluated in the whole group and according to age groups (≥65 < years). Receiver operating characteristic curves were drawn to evaluate the predictivity of the PNI and SII. Results: Out of 407 patients included in this study, 48.4% (n = 197) were older patients, and 51.6% (n = 210) were under 65 years old. For mortality, the area under the curve (AUC) of the PNI and SII in the adult group (<65 years) was 0.706 (95% CI 0.583-0.828) (p = 0.003) and 0.697 (95% CI 0.567-0.827) (p < 0.005), respectively. The AUC of the PNI and SII in the older group was 0.515 (95% CI 0.427-0.604) (p = 0.739) and 0.500 (95% CI 0.411-0.590) (p = 0.993). Conclusions: The accuracy of the PNI and SII in predicting mortality in adult COVID-19 patients seemed to be fair, but no association was found in geriatric patients in this study. The predictivity of the PNI and SII for mortality varies according to age groups.

A Novel Facet of In-Hospital Food Consumption Associated with Hospital Mortality in Patients with Scheduled Admission-Addition of a Study Protocol to Test the Existence of Effects of COVID-19 in the Same Study in the Post-COVID-19 Period

BACKGROUND

Humankind has faced unexperienced pandemic events since 2020. Since the COVID-19 pandemic has calmed down, we felt the need to verify whether in-hospital mortality had worsened compared to pre-pandemic conditions due to the COVID-19 pandemic.

OBJECTIVE

To test the hypothesis that daily food consumption is associated with in-hospital mortality during hospitalization and to provide baseline data to examine whether the effects of COVID-19 exist or not in post-pandemic period.

METHODS

All hospitalized patients staying in a single institution on the third Thursday of May, August, November, and the following February were included. Compared data: (1) among four seasons, (2) between age < 75 vs. ≥75 years, (3) between <75% vs. ≥75% of in-hospital food, and (4) logistic regression analysis to identify factors associated with in-hospital mortality.

RESULTS

In 365 inpatients, the following results were obtained: (1) no seasonality or age effect in in-hospital mortality, (2) the novel cutoff value of 75% of the hospital food requirement was used to identify poor in-hospital survivors, (3) logistic regression analysis showed low food consumption, with <75% of the hospital food requirement as the predictor of high in-hospital mortality.

CONCLUSIONS

A small eater of in-hospital food < 75% during hospitalization was associated with significantly higher in-hospital mortality in patients with scheduled hospitalization in the pre-pandemic period. Then, a study protocol is proposed to test the existence of the effects of COVID-19 in the same study in the post-COVID-19 period. This study protocol is, to our knowledge, the first proposal to test the effects of food consumption in the post-COVID-19 period on in-hospital mortality in the clinical nutritional areas.

The prognostic value of prognostic nutritional index and renal function indicators for mortality prediction in severe COVID-19 elderly patients: A retrospective study

Identifying prognostic factors in elderly patients with severe coronavirus disease 2019 (COVID-19) is crucial for clinical management. Recent evidence suggests malnutrition and renal dysfunction are associated with poor outcome. This study aimed to develop a prognostic model incorporating prognostic nutritional index (PNI), estimated glomerular filtration rate (eGFR), and other parameters to predict mortality risk. This retrospective analysis included 155 elderly patients with severe COVID-19. Clinical data and outcomes were collected. Logistic regression analyzed independent mortality predictors. A joint predictor "L" incorporating PNI, eGFR, D-dimer, and lactate dehydrogenase (LDH) was developed and internally validated using bootstrapping. Decreased PNI (OR = 1.103, 95% CI: 0.78-1.169), decreased eGFR (OR = 0.964, 95% CI: 0.937-0.992), elevated D-dimer (OR = 1.001, 95% CI: 1.000-1.004), and LDH (OR = 1.005, 95% CI: 1.001-1.008) were independent mortality risk factors (all P < .05). The joint predictor "L" showed good discrimination (area under the curve [AUC] = 0.863) and calibration. The bootstrapped area under the curve was 0.858, confirming model stability. A combination of PNI, eGFR, D-dimer, and LDH provides useful prognostic information to identify elderly patients with severe COVID-19 at highest mortality risk for early intervention. Further external validation is warranted.

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

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 χ2 =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.

Nutritional status of elderly patients previously ill with COVID-19: Assessment with nutritional risk screening 2002 (NRS-2002) and mini nutritional assessment (MNA-sf)

BACKGROUND

Long-term effects of COVID-19 showed a wide range of symptoms. Also, it was found that older patients were five times more likely than younger patients to develop long-COVID symptoms (1). This study aimed to investigate the use of Nutrition Risk Screening 2002 (NRS-2002) and the Mini Nutrition Assessment-Short Form (MNA-sf) among COVID-19 in elderly patients in Saudi Arabia.

METHODS

A total of (n = 159) COVID-19 elderly patients were recruited in the study; the relationship between patients' characteristics, including age, gender, Body Mass Index (BMI), infection history, vaccination and chronic disease were evaluated using NRS-2002 and MNA-sf. Multivariate logistic regression to estimate the Odd Ratio (OR) by comparing the OR of different variables between normal nutritional Status and at-risk and Cohen's kappa (κ) coefficient was assessed to analyse the agreement between both tools.

RESULTS

MNA-sf showed a positive association between age and malnutrition risk ≥ 66 years old P = 0.035. Both tools showed a negative association between BMI (P < 0.001 and P = 0.046), respectively and vaccination (P = 0.002 and P = 0.01), respectively, with risk for malnutrition. There was no significant association between Diabetes (DM) and malnutrition risk, but elderly Cardiovascular Disease (CVD) were at malnutrition risk using the NRS- 2002 tool P = 0.003. Inversely, people infected six months or more before malnutrition assessment have a lower risk of malnutrition P = 0.05.

CONCLUSIONS

Both tools were valuable and practical tools for screening elderly people with COVID-19 who are at nutritional risk and those in need of additional nutritional intervention. Further research needed to be applied in the relationship between nutritional status during and post-infectious disease for elderly people using cross-sectional and intervention studies in order to prevent malnutrition complications in Saudi Arabia.